*Result*: Artificial Intelligence, Optimization, and Modeling Techniques in Water Resource Management: Interconnections and Emerging Synergies.
Title:
Artificial Intelligence, Optimization, and Modeling Techniques in Water Resource Management: Interconnections and Emerging Synergies.
Authors:
Razavi HS; Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA., Nejadhashemi AP; Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA.; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA., Deb K; Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan, USA., Toscano G; Department of Computer Science, The Catholic University of America, Washington, DC, USA., Harrigan T; Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan, USA., Linker L; US EPA Chesapeake Bay Program Office, Annapolis, Maryland, USA.
Source:
Water environment research : a research publication of the Water Environment Federation [Water Environ Res] 2026 Jan; Vol. 98 (1), pp. e70246.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Wiley Subscription Services on behalf of The Water Environment Foundation Country of Publication: United States NLM ID: 9886167 Publication Model: Print Cited Medium: Internet ISSN: 1554-7531 (Electronic) Linking ISSN: 10614303 NLM ISO Abbreviation: Water Environ Res Subsets: MEDLINE
Imprint Name(s):
Publication: Hoboken, NJ : Wiley Subscription Services on behalf of The Water Environment Foundation
Original Publication: Alexandria, VA : The Federation, c1992-
Original Publication: Alexandria, VA : The Federation, c1992-
MeSH Terms:
References:
Alekszejenkó, L., and T. Dobrowiecki. 2022. “Privacy‐Aware Methods for Data Sharing Between Autonomous Vehicles*.” IFAC‐PapersOnLine 55, no. 14: 7–13. https://doi.org/10.1016/J.IFACOL.2022.07.575.
Alemu, E. T., R. N. Palmer, A. Polebitski, and B. Meaker. 2011. “Decision Support System for Optimizing Reservoir Operations Using Ensemble Streamflow Predictions.” Journal of Water Resources Planning and Management 137, no. 1: 72–82. https://doi.org/10.1061/(ASCE)WR.1943‐5452.0000088/ASSET/79388153‐EC79‐4123‐A441‐3258C4F7E7BE/ASSETS/IMAGES/LARGE/7.JPG.
Alsabt, R., Y. A. Adenle, and H. M. Alshuwaikhat. 2024. “Exploring the Roles, Future Impacts, and Strategic Integration of Artificial Intelligence in the Optimization of Smart City—From Systematic Literature Review to Conceptual Model.” Sustainability 2024 16, no. 8: 3389. https://doi.org/10.3390/SU16083389.
Arora, B., D. Dwivedi, S. S. Hubbard, C. I. Steefel, and K. H. Williams. 2016. “Identifying Geochemical hot Moments and Their Controls on a Contaminated River Floodplain System Using Wavelet and Entropy Approaches.” Environmental Modelling & Software 85: 27–41. https://doi.org/10.1016/J.ENVSOFT.2016.08.005.
AWS. 2023. “What is Hyperparameter Tuning? Hyperparameter Tuning Methods Explained—AWS.” https://aws.amazon.com/what‐is/hyperparameter‐tuning/.
Azad, A., H. Karami, S. Farzin, S. F. Mousavi, and O. Kisi. 2019. “Modeling River Water Quality Parameters Using Modified Adaptive Neuro Fuzzy Inference System.” Water Science and Engineering 12, no. 1: 45–54. https://doi.org/10.1016/j.wse.2018.11.001.
Balson, T., and A. S. Ward. 2022. “A Machine Learning Approach to Water Quality Forecasts and Sensor Network Expansion: Case Study in the Wabash River Basin, United States.” Hydrological Processes 36, no. 6: e14619. https://doi.org/10.1002/HYP.14619.
Barcellos, D. D. S., G. M. K. Fernandes, and F. T. de Souza. 2021. “Data Based Model for Predicting COVID‐19 Morbidity and Mortality in Metropolis.” Scientific Reports 11, no. 1: 1–11. https://doi.org/10.1038/s41598‐021‐04029‐6.
Behmel, S., M. Damour, R. Ludwig, and M. J. Rodriguez. 2016. “Water Quality Monitoring Strategies — A Review and Future Perspectives.” Science of the Total Environment 571: 1312–1329. https://doi.org/10.1016/J.SCITOTENV.2016.06.235.
Behmel, S., M. Damour, R. Ludwig, and M. J. Rodriguez. 2021. “Intelligent Decision‐Support System to Plan, Manage and Optimize Water Quality Monitoring Programs: Design of a Conceptual Framework.” Journal of Environmental Planning and Management 64, no. 4: 703–733. https://doi.org/10.1080/09640568.2020.1782858.
Bender, M. J., and S. P. Simonovic. 1996. “Systems Approach for Collaborative Decision Support in Water Resources Planning.” International Symposium on Technology and Society: 357–363. https://doi.org/10.1109/ISTAS.1996.541172.
Bernhardt, E. S., J. R. Blaszczak, C. D. Ficken, M. L. Fork, K. E. Kaiser, and E. C. Seybold. 2017. “Control Points in Ecosystems: Moving Beyond the hot Spot hot Moment Concept.” Ecosystems 20, no. 4: 665–682. https://doi.org/10.1007/S10021‐016‐0103‐Y/METRICS.
Bhosekar, A., and M. Ierapetritou. 2018. “Advances in Surrogate Based Modeling, Feasibility Analysis, and Optimization: A Review.” Computers and Chemical Engineering 108: 250–267. https://doi.org/10.1016/j.compchemeng.2017.09.017.
Bilgin, A., and M. U. Konanç. 2016. “Evaluation of Surface Water Quality and Heavy Metal Pollution of Coruh River Basin (Turkey) by Multivariate Statistical Methods.” Environmental Earth Sciences 75, no. 12: 1029. https://doi.org/10.1007/s12665‐016‐5821‐0.
Bischl, B., M. Binder, M. Lang, et al. 2021. “Hyperparameter Optimization: Foundations, Algorithms, Best Practices, and Open Challenges.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13, no. 2: e1484. http://arxiv.org/abs/2107.05847.
Bishop, C. M. 1995. “Neural Networks for Pattern Recognition.” https://doi.org/10.1093/OSO/9780198538493.001.0001.
Bouguettaya, A., H. Zarzour, A. Kechida, and A. M. Taberkit. 2022. “Deep Learning Techniques to Classify Agricultural Crops Through UAV Imagery: A Review.” Neural Computing and Applications 34, no. 12: 9511–9536 10.1007/S00521‐022‐07104‐9.
Brentan, B., S. Carpitella, D. Barros, G. Meirelles, A. Certa, and J. Izquierdo. 2021. “Water Quality Sensor Placement: A Multi‐Objective and Multi‐Criteria Approach.” Water Resources Management 35, no. 1: 225–241. https://doi.org/10.1007/S11269‐020‐02720‐3.
Budamala, V., and A. Baburao Mahindrakar. 2020. “Integration of Adaptive Emulators and Sensitivity Analysis for Enhancement of Complex Hydrological Models.” Environmental Processes 7, no. 4: 1235–1253. https://doi.org/10.1007/S40710‐020‐00468‐X/TABLES/2.
Bui, D. T., K. Khosravi, J. Tiefenbacher, H. Nguyen, and N. Kazakis. 2020. “Improving Prediction of Water Quality Indices Using Novel Hybrid Machine‐Learning Algorithms.” Science of the Total Environment 721: 137612. https://doi.org/10.1016/J.SCITOTENV.2020.137612.
Cai, X., L. Wu, T. Zhao, D. Wu, W. Zhang, and J. Chen. 2024. “Dynamic Adaptive Multi‐Objective Optimization Algorithm Based on Type Detection.” Information Sciences 654: 119867. https://doi.org/10.1016/J.INS.2023.119867.
Calazans, G. M., C. C. Pinto, E. P. da Costa, A. F. Perini, and S. C. Oliveira. 2018a. “The use of Multivariate Statistical Methods for Optimization of the Surface Water Quality Network Monitoring in the Paraopeba River Basin, Brazil.” Environmental Monitoring and Assessment 190, no. 8: 491. https://doi.org/10.1007/s10661‐018‐6873‐2.
Calazans, G. M., C. C. Pinto, E. P. da Costa, A. F. Perini, and S. C. Oliveira. 2018b. “Using Multivariate Techniques as a Strategy to Guide Optimization Projects for the Surface Water Quality Network Monitoring in the Velhas River Basin, Brazil.” Environmental Monitoring and Assessment 190, no. 12: 1–15. https://doi.org/10.1007/S10661‐018‐7099‐Z/TABLES/4.
Camara, M., N. R. Jamil, A. F. Bin Abdullah, R. binti Hashim, and A. G. Aliyu. 2020. “Economic and Efficiency Based Optimisation of Water Quality Monitoring Network for Land Use Impact Assessment.” Science of the Total Environment 737: 139800. https://doi.org/10.1016/J.SCITOTENV.2020.139800.
Castelletti, A., F. Pianosi, M. Restelli, A. Castelletti, F. Pianosi, and M. Restelli. 2013. “A Multiobjective Reinforcement Learning Approach to Water Resources Systems Operation: Pareto Frontier Approximation in a Single run.” WRR 49, no. 6: 3476–3486. https://doi.org/10.1002/WRCR.20295.
Cawley, G. C., and N. L. C. Talbot. 2010. “On Over‐Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation.” Journal of Machine Learning Research 11: 2079–2107.
Chang, F.‐J., L.‐C. Chang, J. Chen, F.‐J. Chang, L.‐C. Chang, and J.‐F. Chen. 2023. “Artificial Intelligence Techniques in Hydrology and Water Resources Management.” Water 15, no. 10: 1846. https://doi.org/10.3390/W15101846.
Chang, Z., Z. Guo, K. Chen, et al., 2024. “A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity.” https://doi.org/10.1029/2023WR036051.
Chapman, D. V., S. Warner, and C. Dickens 2021. “Approaches to Water Monitoring.” 1–11. https://doi.org/10.1007/978‐3‐319‐70061‐8_96‐1.
Chapman, D. V., S. Warner, and C. Dickens 2022. “Approaches to Water Monitoring.” 36–46. https://doi.org/10.1007/978‐3‐319‐95846‐0_96.
Chen, W., D. Nover, H. Yen, et al. 2020. “Exploring the Multiscale Hydrologic Regulation of Multipond Systems in a Humid Agricultural Catchment.” Water Research 184: 115987. https://doi.org/10.1016/J.WATRES.2020.115987.
Chen, Y., L. Song, Y. Liu, L. Yang, and D. Li. 2020. “A Review of the Artificial Neural Network Models for Water Quality Prediction.” Applied Sciences 10, no. 17: 5776. https://doi.org/10.3390/APP10175776.
Clausen, H. S., N. H. Ebdrup, I. M. Barsøe, et al. 2020. “Association Between Drinking Water Nitrate and Adverse Reproductive Outcomes: A Systematic PRISMA Review.” Water 12, no. 8: 2287. https://doi.org/10.3390/W12082287.
Coelho, A. C., J. W. Labadie, and D. G. Fontane. 2012. “Multicriteria Decision Support System for Regionalization of Integrated Water Resources Management.” Water Resources Management 26, no. 5: 1325–1346. https://doi.org/10.1007/S11269‐011‐9961‐4/FIGURES/11.
Cosgrove, W. J., and D. P. Loucks. 2015. “Water Management: Current and Future Challenges and Research Directions.” Water Resources Research 51, no. 6: 4823–4839. https://doi.org/10.1002/2014WR016869.
da Silveira Barcellos, D., and F. T. de Souza. 2022. “Optimization of Water Quality Monitoring Programs by Data Mining.” Water Research 221: 118805. https://doi.org/10.1016/J.WATRES.2022.118805.
Datta B. 2005. “(16) (PDF) Optimization Applications in Water Resources Systems Engineering.” https://www.researchgate.net/publication/242115216_Optimization_Applications_in_Water_Resources_Systems_Engineering.
Deb, C., F. Zhang, J. Yang, S. E. Lee, and K. W. Shah. 2017. “A Review on Time Series Forecasting Techniques for Building Energy Consumption.” Renewable and Sustainable Energy Reviews 74: 902–924. https://doi.org/10.1016/J.RSER.2017.02.085.
Deb, K., S. Bandaru, D. Greiner, A. Gaspar‐Cunha, and C. C. Tutum. 2014. “An Integrated Approach to Automated Innovization for Discovering Useful Design Principles: Case Studies From Engineering.” Applied Soft Computing Journal 15: 42–56. https://doi.org/10.1016/J.ASOC.2013.10.011.
Deb, K., and R. Datta. 2012. “Hybrid Evolutionary Multi‐Objective Optimization and Analysis of Machining Operations.” Engineering Optimization 44, no. 6: 685–706. https://doi.org/10.1080/0305215X.2011.604316.
DeCastro‐García, N., Á. L. Muñoz Castañeda, D. Escudero García, and M. V. Carriegos. 2019. “Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase Over the Efficiency of a Machine Learning Algorithm.” Complexity 2019: 1–16. https://doi.org/10.1155/2019/6278908.
Dehghani, A., H. M. Z. H. Moazam, F. Mortazavizadeh, et al. 2023. “Comparative Evaluation of LSTM, CNN, and ConvLSTM for Hourly Short‐Term Streamflow Forecasting Using Deep Learning Approaches.” Ecological Informatics 75: 102119. https://doi.org/10.1016/J.ECOINF.2023.102119.
Deng, W., and G. Wang. 2017. “A Novel Water Quality Data Analysis Framework Based on Time‐Series Data Mining.” Journal of Environmental Management 196: 365–375. https://doi.org/10.1016/J.JENVMAN.2017.03.024.
Diaz, G. I., A. Fokoue‐Nkoutche, G. Nannicini, and H. Samulowitz. 2017. “An Effective Algorithm for Hyperparameter Optimization of Neural Networks.” IBM Journal of Research and Development 61, no. 4: 9:1–9:11. https://doi.org/10.1147/JRD.2017.2709578.
Dilmi, S., and M. Ladjal. 2021. “A Novel Approach for Water Quality Classification Based on the Integration of Deep Learning and Feature Extraction Techniques.” Chemometrics and Intelligent Laboratory Systems 214: 104329. https://doi.org/10.1016/J.CHEMOLAB.2021.104329.
Dong, F., J. Li, C. Dai, et al. 2022. “Understanding Robustness in Multiscale Nutrient Abatement: Probabilistic Simulation‐Optimization Using Bayesian Network Emulators.” Journal of Cleaner Production 378: 134394. https://doi.org/10.1016/J.JCLEPRO.2022.134394.
Dorigo, M., V. Maniezzo, and A. Colorni. 1996. “Ant System: Optimization by a Colony of Cooperating Agents.” IEEE Transactions on Systems, man, and Cybernetics, Part B: Cybernetics 26, no. 1: 29–41. https://doi.org/10.1109/3477.484436.
Duobiene, S., K. Ratautas, R. Trusovas, et al. 2022. “Development of Wireless Sensor Network for Environment Monitoring and Its Implementation Using SSAIL Technology.” Sensors 22, no. 14: 5343. https://doi.org/10.3390/S22145343.
Duvvuri, S. P., and J. Anmala. 2019. “Fecal Coliform Predictive Model Using Genetic Algorithm‐Based Radial Basis Function Neural Networks (GA‐RBFNNs).” Neural Computing and Applications 31, no. 12: 8393–8409. https://doi.org/10.1007/S00521‐019‐04520‐2/TABLES/1.
Ehteram, M., A. Ferdowsi, M. Faramarzpour, et al. 2021. “Hybridization of Artificial Intelligence Models With Nature Inspired Optimization Algorithms for Lake Water Level Prediction and Uncertainty Analysis.” Alexandria Engineering Journal 60, no. 2: 2193–2208. https://doi.org/10.1016/J.AEJ.2020.12.034.
Eini, M. R., M. A. Olyaei, T. Kamyab, J. Teymoori, L. Brocca, and M. Piniewski. 2021. “Evaluating Three non‐gauge‐Corrected Satellite Precipitation Estimates by a Regional Gauge Interpolated Dataset Over Iran.” Journal of Hydrology: Regional Studies 38: 100942. https://doi.org/10.1016/J.EJRH.2021.100942.
Elshaikh, A., E. Elsheikh, and J. Mabrouki. 2024. “Applications of Artificial Intelligence in Precision Irrigation.” Journal of Environment and Earth Science 6, no. 2: 176–186 10.30564/JEES.V6I2.6679.
Elshorbagy, A., G. Corzo, S. Srinivasulu, and D. P. Solomatine. 2010. “Experimental Investigation of the Predictive Capabilities of Data Driven Modeling Techniques in Hydrology ‐ Part 1: Concepts and Methodology.” Hydrology and Earth System Sciences 14, no. 10: 1931–1941. https://doi.org/10.5194/HESS‐14‐1931‐2010.
Gardezi, M., B. Joshi, D. M. Rizzo, et al. 2023. “Artificial Intelligence in Farming: Challenges and Opportunities for Building Trust.” Agronomy Journal 116, no. 3: 1217–1228. https://doi.org/10.1002/AGJ2.21353.
Garzón, A., Z. Kapelan, J. Langeveld, and R. Taormina. 2022. “Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions.” Water Resources Research 58, no. 5: e2021WR031808. https://doi.org/10.1029/2021WR031808.
Gaur, A., and K. Deb 2017. “Effect of Size and Order of Variables in Rules for Multi‐Objective Repair‐Based Innovization Procedure.” 2017 IEEE Congress on Evolutionary Computation, CEC 2017 ‐ Proceedings, 2177–2184. https://doi.org/10.1109/CEC.2017.7969568.
Ge, Y., F. Han, F. Wu, et al. 2024. “Sustainable Decision Making Based on Systems Integration and Decision Support System Promoting Endorheic Basin Sustainability.” https://doi.org/10.1016/j.dss.2024.114169.
Ghahramani, Z. 2015. “Probabilistic Machine Learning and Artificial Intelligence.” Nature 521, no. 7553: 452–459 10.1038/nature14541.
Giri, S., and A. P. Nejadhashemi. 2014. “Application of Analytical Hierarchy Process for Effective Selection of Agricultural Best Management Practices.” Journal of Environmental Management 132: 165–177. https://doi.org/10.1016/J.JENVMAN.2013.10.021.
Giupponi, C., and A. Sgobbi. 2013. “Decision Support Systems for Water Resources Management in Developing Countries: Learning From Experiences in Africa.” Water 5, no. 2: 798–818. https://doi.org/10.3390/W5020798.
Goap, A., D. Sharma, A. K. Shukla, and C. Rama Krishna. 2018. “An IoT Based Smart Irrigation Management System Using Machine Learning and Open Source Technologies.” Computers and Electronics in Agriculture 155: 41–49. https://doi.org/10.1016/J.COMPAG.2018.09.040.
González Perea, R., E. Camacho Poyato, and J. A. Rodríguez Díaz 2024. “Attention Is all Water Need: Multistep Time Series Irrigation Water Demand Forecasting in Irrigation Districts.” https://doi.org/10.1016/j.compag.2024.108723.
Guo, D., A. Lintern, J. A. Webb, et al. 2019. “Key Factors Affecting Temporal Variability in Stream Water Quality.” Water Resources Research 55, no. 1: 112–129. https://doi.org/10.1029/2018WR023370.
Guo, X., J. Luo, W. Lu, G. Dong, and Z. Pan. 2024. “Optimal Design of Groundwater Pollution Monitoring Network Based on a Back‐Propagation Neural Network Surrogate Model and Grey Wolf Optimizer Algorithm Under Uncertainty.” Environmental Monitoring and Assessment 196, no. 2: 1–18. https://doi.org/10.1007/S10661‐023‐12276‐5/FIGURES/7.
Gupta, S., and S. K. Gupta. 2024. “Water resource mapping, monitoring, and modeling using geospatial approaches.” Developments in Environmental Science 16: 575–602. https://doi.org/10.1016/B978‐0‐443‐23665‐5.00025‐9.
Hajgató, G., G. Paál, and B. Gyires‐Tóth. 2020. “Deep Reinforcement Learning for Real‐Time Optimization of Pumps in Water Distribution Systems.” Journal of Water Resources Planning and Management 146, no. 11: 04020079.
Hassija, V., V. Chamola, A. Mahapatra, et al. 2024. “Interpreting Black‐Box Models: A Review on Explainable Artificial Intelligence.” Cognitive Computation 16, no. 1: 45–74. https://doi.org/10.1007/S12559‐023‐10179‐8/FIGURES/13.
Haxton, T., R. Murray, W. Hart, K. Klise, and C. Phillips 2011. “Formulation of Chlorine and Decontamination Booster Station Optimization Problem.” World Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability ‐ Proceedings of the 2011 World Environmental and Water Resources Congress, 199–205. https://doi.org/10.1061/41173(414)21.
He, G., T. Zhang, F. Zheng, and Q. Zhang 2018. “An Efficient Multi‐Objective Optimization Method for Water Quality Sensor Placement Within Water Distribution Systems Considering Contamination Probability Variations.” https://doi.org/10.1016/j.watres.2018.06.041.
Hou, Z., W. Lao, Y. Wang, and W. Lu. 2021. “Homotopy‐Based Hyper‐Heuristic Searching Approach for Reciprocal Feedback Inversion of Groundwater Contamination Source and Aquifer Parameters.” Applied Soft Computing 104: 107191. https://doi.org/10.1016/J.ASOC.2021.107191.
Huang, C., Y. Chen, S. Zhang, and J. Wu. 2018. “Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review.” Reviews of Geophysics 56, no. 2: 333–360. https://doi.org/10.1029/2018RG000598.
Hutter F., L. Kotthoff, and J. Vanschoren. 2019. “Automated Machine Learning.” 219. https://doi.org/10.1007/978‐3‐030‐05318‐5.
Injadat, M. N., A. Moubayed, A. B. Nassif, and A. Shami. 2020. “Systematic Ensemble Model Selection Approach for Educational Data Mining.” Knowledge‐Based Systems 200: 105992. https://doi.org/10.1016/J.KNOSYS.2020.105992.
Injadat, M., F. Salo, A. B. Nassif, A. Essex, and A. Shami 2018. “Bayesian Optimization With Machine Learning Algorithms Towards Anomaly Detection.” 2018 IEEE Global Communications Conference, GLOBECOM 2018 ‐ Proceedings. https://doi.org/10.1109/GLOCOM.2018.8647714.
Jain, A., S. Rallapalli, and D. Kumar. 2022. “Cloud‐Based Neuro‐Fuzzy Hydro‐Climatic Model for Water Quality Assessment Under Uncertainty and Sensitivity.” Environmental Science and Pollution Research 29, no. 43: 65259–65275. https://doi.org/10.1007/s11356‐022‐20385‐w.
Jaiswal, R. K., A. K. Lohani, and H. L. Tiwari. 2021. “A Decision Support System Framework for Strategic Water Resources Planning and Management Under Projected Climate Scenarios for a Reservoir Complex.” Journal of Hydrology 603: 127051. https://doi.org/10.1016/J.JHYDROL.2021.127051.
James, E. G. 2012. “Fixed‐Point Drift and Hysteresis in Frequency‐Scaled Unimanual Coordination.” Journal of Motor Behavior 44, no. 4: 281–288. https://doi.org/10.1080/00222895.2012.702141.
Javidi Sabbaghian, R., M. Zarghami, A. P. Nejadhashemi, M. B. Sharifi, M. R. Herman, and F. Daneshvar. 2016. “Application of Risk‐Based Multiple Criteria Decision Analysis for Selection of the Best Agricultural Scenario for Effective Watershed Management.” Journal of Environmental Management 168: 260–272. https://doi.org/10.1016/j.jenvman.2015.11.038.
Ji, C., Y. Cao, X. Li, et al. 2024. “A Review of the Satellite Remote Sensing Techniques for Assessment of Runoff and Sediment in Soil Erosion.” Journal Of Hydrology And Hydromechanics 72: 252–267. https://doi.org/10.2478/johh‐2024‐0009.
Jin, Y., H. Wang, and C. Sun. 2021. “Data‐Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science.” In Studies in Computational Intelligence, 1st ed., Vol. 975. Springer.
Jordan, M. I., and T. M. Mitchell. 2015. “Machine Learning: Trends, Perspectives, and Prospects.” Science 349, no. 6245: 255–260. https://doi.org/10.1126/SCIENCE.AAA8415/ASSET/AB2EF18A‐576D‐464D‐B1B6‐1301159EE29A/ASSETS/GRAPHIC/349_255_F5.JPEG.
Kamali, F., and R. Kerachian. 2025. “Applications of Behavioral Simulation Models in Water Resources Management: A Systematic State‐of‐the‐Art Review.” Water Resources Management 39, no. 13: 6715–6736. https://doi.org/10.1007/s11269‐025‐04287‐3.
Kamyab, H., T. Khademi, S. Chelliapan, et al. 2023. “The Latest Innovative Avenues for the Utilization of Artificial Intelligence and big Data Analytics in Water Resource Management.” Results in Engineering 20: 101566. https://doi.org/10.1016/J.RINENG.2023.101566.
Karthick, K. 2024. “Comprehensive Overview of Optimization Techniques in Machine Learning Training.” Control Systems and Optimization Letters 2, no. 1: 23–27 10.59247/CSOL.V2I1.69.
Kaufman, D. E., G. W. Shenk, G. Bhatt, et al. 2021. “Supporting Cost‐Effective Watershed Management Strategies for Chesapeake Bay Using a Modeling and Optimization Framework.” Environmental Modelling & Software 144: 105141. https://doi.org/10.1016/J.ENVSOFT.2021.105141.
Kennedy, J., and R. Eberhart. 1995. “Particle Swarm Optimization.” Proceedings of ICNN’95 ‐ International Conference on Neural Networks 4: 1942–1948. https://doi.org/10.1109/ICNN.1995.488968.
Khan, R., M. Zakarya, V. Balasubramanian, M. A. Jan, and V. G. Menon. 2021. “Smart Sensing‐Enabled Decision Support System for Water Scheduling in Orange Orchard.” IEEE Sensors Journal 21, no. 16: 17492–17499. https://doi.org/10.1109/JSEN.2020.3012511.
Kim, M., Y. Kim, H. Kim, W. Piao, and C. Kim. 2016. “Enhanced Monitoring of Water Quality Variation in Nakdong River Downstream Using Multivariate Statistical Techniques.” Desalination and Water Treatment 57, no. 27: 12508–12517. https://doi.org/10.1080/19443994.2015.1049963.
Koch, P., B. Wujek, O. Golovidov, J. Griffin, S. Gardner, and Y. Xu 2018. “Autotune: A Derivative‐Free Optimization Framework for Hyperparameter Tuning.” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 443–452. https://doi.org/10.1145/3219819.3219837.
Koulamas, C., and M. T. Lazarescu. 2020. “Real‐Time Sensor Networks and Systems for the Industrial IOT: What Next?” Sensors (Switzerland) 20, no. 18: 1–3. https://doi.org/10.3390/S20185023.
Krishnan, S. R., M. K. Nallakaruppan, R. Chengoden, et al. 2022. “Smart Water Resource Management Using Artificial Intelligence—A Review.” Sustainability 14, no. 20: 13384. https://doi.org/10.3390/SU142013384.
Kuhn, M., and K. Johnson 2016. “Applied Predictive Modeling”.
Kumar, D., S. Sukrit, M. Kalyanmoy, D. Erik, and D. Goodman 2021. “Genetic and Evolutionary Computation Machine Learning Assisted Evolutionary Multi‐and Many‐Objective Optimization.” https://www.springer.com/series/6008.
Le, X. H. V. Ho, G. Lee, and S. Jung. 2019. “Application of Long Short‐Term Memory (LSTM) Neural Network for Flood Forecasting.” Water 11, no. 7: 1387. https://doi.org/10.3390/w11071387.
Li, E., and X. Ma. 2019. “Dynamic Multi‐objective Optimization Algorithm based on Transfer Learning for Environmental Protection.” Ekoloji 28, no. 107: 2509–2519.
Li, G., P. Shi, S. Qu, et al. 2025. “Adaptive Surrogate Model Assisted Swarm Intelligence for Parameter Inversion of Complex Hydrological Models.” Environmental Modelling & Software 186: 106353. https://doi.org/10.1016/J.ENVSOFT.2025.106353.
Li, P. H., H. H. Kwon, L. Sun, U. Lall, and J. J. Kao. 2010. “A Modified Support Vector Machine Based Prediction Model on Streamflow at the Shihmen Reservoir, Taiwan.” International Journal of Climatology 30, no. 8: 1256–1268. https://doi.org/10.1002/JOC.1954.
Lim, B., and S. Zohren. 2021. “Time‐Series Forecasting With Deep Learning: A Survey.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379: 2194. https://doi.org/10.1098/RSTA.2020.0209.
Lintern, A., J. A. Webb, D. Ryu, et al. 2018. “What Are the key Catchment Characteristics Affecting Spatial Differences in Riverine Water Quality?” Water Resources Research 54, no. 10: 7252–7272. https://doi.org/10.1029/2017WR022172.
Liu, Y., J. Luo, Y. Xiong, Y. Ji, and X. Xin. 2022. “Inversion of Hydrogeological Parameters Based on an Adaptive Dynamic Surrogate Model.” Hydrogeology Journal 30, no. 5: 1513–1527. https://doi.org/10.1007/S10040‐022‐02493‐6/FIGURES/11.
Lorenzo, P. R., J. Nalepa, M. Kawulok, L. S. Ramos, and J. R. Pastor. 2017. “Particle Swarm Optimization for Hyper‐Parameter Selection in Deep Neural Networks.” Proceedings of the Genetic and Evolutionary Computation Conference 8: 481–488. https://doi.org/10.1145/3071178.3071208.
Luo, G. 2016. “A Review of Automatic Selection Methods for Machine Learning Algorithms and Hyper‐Parameter Values.” Network Modeling Analysis in Health Informatics and Bioinformatics 5, no. 1: 1–16. https://doi.org/10.1007/S13721‐016‐0125‐6/FIGURES/2.
Luo, Q., D. Peng, W. Shang, et al. 2023. “Water Quality Analysis Based on LSTM and BP Optimization With a Transfer Learning Model.” Environmental Science and Pollution Research 30, no. 59: 124341–124352. https://doi.org/10.1007/s11356‐023‐31068‐5.
Maclaurin, D., D. Duvenaud, and R. P. Adams 2015. “Gradient‐Based Hyperparameter Optimization Through Reversible Learning.” https://arxiv.org/abs/1502.03492v3.
Maier, H. R., A. Jain, G. C. Dandy, and K. P. Sudheer. 2010. “Methods Used for the Development of Neural Networks for the Prediction of Water Resource Variables in River Systems: Current Status and Future Directions.” Environmental Modelling & Software 25, no. 8: 891–909. https://doi.org/10.1016/J.ENVSOFT.2010.02.003.
Maier, H. R., Z. Kapelan, J. Kasprzyk, et al. 2014. “Evolutionary Algorithms and Other Metaheuristics in Water Resources: Current Status, Research Challenges and Future Directions.” Environmental Modelling and Software 62: 271–299. https://doi.org/10.1016/j.envsoft.2014.09.013.
Malitsky, Y., D. Mehta, B. O'Sullivan, and H. Simonis 2013. “Tuning Parameters of Large Neighborhood Search for the Machine Reassignment Problem.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7874 LNCS, 176–192. https://doi.org/10.1007/978‐3‐642‐38171‐3_12/COVER.
Mirghani, B. Y., E. M. Zechman, R. S. Ranjithan, and G. Mahinthakumar. 2012. “Enhanced Simulation‐Optimization Approach Using Surrogate Modeling for Solving Inverse Problems.” Environmental Forensics 13, no. 4: 348–363. https://doi.org/10.1080/15275922.2012.702333.
Mirzaei, M., H. Yu, A. Dehghani, et al. 2021. “A Novel Stacked Long Short‐Term Memory Approach of Deep Learning for Streamflow Simulation.” Sustainability 13, no. 23: 13384. https://doi.org/10.3390/SU132313384.
Mittal, S., D. K. Saxena, K. Deb, and E. D. Goodman. 2020. “Enhanced Innovized Repair Operator for Evolutionary Multi‐ and Many‐Objective Optimization.” IEEE Transactions on Evolutionary Computation 26, no. 5: 961–975. https://doi.org/10.1109/TEVC.2021.3131952.
Mittal, S., D. K. Saxena, K. Deb, and E. D. Goodman. 2021. “A Learning‐Based Innovized Progress Operator for Faster Convergence in Evolutionary Multi‐Objective Optimization.” ACM Transactions on Evolutionary Learning and Optimization (TELO) 2, no. 1: 1–29. https://doi.org/10.1145/3474059.
Mittal, S., D. K. Saxena, K. Deb, and E. D. Goodman. 2023. “A Unified Innovized Progress Operator for Performance Enhancement in Evolutionary Multi‐ and Many‐Objective Optimization.” IEEE Transactions on Evolutionary Computation 28, no. 6: 1605–1619. https://doi.org/10.1109/TEVC.2023.3321603.
Moher, D., A. Liberati, J. Tetzlaff, and D. G. Altman. 2010. “Preferred Reporting Items for Systematic Reviews and meta‐Analyses: The PRISMA Statement.” International Journal of Surgery 8, no. 5: 336–341. https://doi.org/10.1016/J.IJSU.2010.02.007.
Moro‐Visconti, R., S. Cruz Rambaud, and J. López Pascual. 2023. “Artificial Intelligence‐Driven Scalability and Its Impact on the Sustainability and Valuation of Traditional Firms.” Humanities and Social Sciences Communications 10, no. 1: 1–14 10.1057/s41599‐023‐02214‐8.
Nakhaei, M., A. Ahmadi, M. Gheibi, B. Chahkandi, M. Hajiaghaei‐Keshteli, and K. Behzadian. 2023. “A Smart Sustainable Decision Support System for Water Management of Power Plants in Water Stress Regions.” Expert Systems With Applications 230: 120752. https://doi.org/10.1016/J.ESWA.2023.120752.
Nguyen, T. H., B. Helm, H. Hettiarachchi, S. Caucci, and P. Krebs. 2020. “Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany.” Water 12, no. 2: 420. https://doi.org/10.3390/w12020420.
Ni, Q., L. Wang, R. Ye, F. Yang, and M. Sivakumar 2010. “Evolutionary Modeling for Streamflow Forecasting With Minimal Datasets: A Case Study in the West Malian River, China.” www.liebertpub.com.
Noori, N., and L. Kalin. 2016. “Coupling SWAT and ANN Models for Enhanced Daily Streamflow Prediction.” Journal of Hydrology 533: 141–151. https://doi.org/10.1016/J.JHYDROL.2015.11.050.
Nourani, V., A. Hosseini Baghanam, J. Adamowski, and O. Kisi. 2014. “Applications of Hybrid Wavelet–Artificial Intelligence Models in Hydrology: A Review.” Journal of Hydrology 514: 358–377. https://doi.org/10.1016/J.JHYDROL.2014.03.057.
Nova, K. 2023. “AI‐Enabled Water Management Systems: An Analysis of System Components and Interdependencies for Water Conservation.” Eigenpub Review of Science and Technology 7, no. 1: 105–124. https://studies.eigenpub.com/index.php/erst/article/view/12.
Nwabueze, M. O., A. Aliyu, K. J. Adegbo, and C. D. Ikemefuna. 2024. “Enhancing Machine Optimization Through AI‐Driven Data Analysis and Gathering: Leveraging Integrated Systems and Hybrid Technology for Industrial Efficiency.” World Journal of Advanced Research and Reviews 23, no. 3: 1919–1943. https://doi.org/10.30574/WJARR.2024.23.3.2882.
Olatinwo, S. O., and T. H. Joubert. 2023. “Resource Allocation Optimization in IoT‐Enabled Water Quality Monitoring Systems.” Sensors 23, no. 21: 8963. https://doi.org/10.3390/S23218963.
Page, M. J., J. E. McKenzie, P. M. Bossuyt, et al. 2021. “The PRISMA 2020 Statement: an Updated Guideline for Reporting Systematic Reviews.” BMJ (Clinical Research Ed.) 372: n71. https://doi.org/10.1136/bmj.n71.
Pallottino, S., G. M. Sechi, and P. Zuddas. 2005. “A DSS for Water Resources Management Under Uncertainty by Scenario Analysis.” Environmental Modelling & Software 20, no. 8: 1031–1042. https://doi.org/10.1016/J.ENVSOFT.2004.09.012.
Park, Y., H. K. Lee, J. K. Shin, et al. 2021. “A Machine Learning Approach for Early Warning of Cyanobacterial Bloom Outbreaks in a Freshwater Reservoir.” Journal of Environmental Management 288: 112415. https://doi.org/10.1016/J.JENVMAN.2021.112415.
Peña‐Guzmán, C., L. Soto, and A. Diaz. 2019. “A Proposal for Redesigning the Water Quality Network of the Tunjuelo River in Bogotá, Colombia Through a Spatio‐Temporal Analysis.” Resources 8, no. 2: 64. https://doi.org/10.3390/resources8020064.
Pérez, C. J., M. A. Vega‐Rodríguez, K. Reder, and M. Flörke. 2017. “A Multi‐Objective Artificial bee Colony‐Based Optimization Approach to Design Water Quality Monitoring Networks in River Basins.” Journal of Cleaner Production 166: 579–589. https://doi.org/10.1016/J.JCLEPRO.2017.08.060.
Preite, L., F. Solari, and G. Vignali. 2023. “Technologies to Optimize the Water Consumption in Agriculture: A Systematic Review.” Sustainability 15, no. 7: 5975. https://doi.org/10.3390/SU15075975.
PRISMA. 2025. “Www.Prisma‐Statement.Org.” http://www.prisma‐statement.org.
Probst, P., and B. Bischl. 2019. “Tunability: Importance of Hyperparameters of Machine Learning Algorithms.” Journal of Machine Learning Research 20: 1–32. http://jmlr.org/papers/v20/18‐444.html.
Puri, D., K. Borel, C. Vance, and R. Karthikeyan. 2017. “Optimization of a Water Quality Monitoring Network Using a Spatially Referenced Water Quality Model and a Genetic Algorithm.” Water 9, no. 9: 704. https://doi.org/10.3390/w9090704.
Qolomany, B., M. Maabreh, A. Al‐Fuqaha, A. Gupta, and D. Benhaddou 2017. “Parameters Optimization of Deep Learning Models Using Particle Swarm Optimization.” 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 1285–1290. https://doi.org/10.1109/IWCMC.2017.7986470.
Rao, R. V., and V. J. Savsani. 2012. “Advanced Optimization Techniques.” In Springer Series in Advanced Manufacturing, 5–34. Springer Nature. https://doi.org/10.1007/978‐1‐4471‐2748‐2_2.
Razavi, S., B. A. Tolson, and D. H. Burn. 2012. “Review of Surrogate Modeling in Water Resources.” Water Resources Research 48, no. 7: 7401. https://doi.org/10.1029/2011WR011527.
Rbaibi, O., and A. SahibEddine. 2024. “Sustainability in Integrated Water Resources Management: Systematic Literature Review.” World Sustainability Series, Part F2854: 195–202. https://doi.org/10.1007/978‐3‐031‐56292‐1_15/FIGURES/1.
Reed, P. M., D. Hadka, J. D. Herman, J. R. Kasprzyk, and J. B. Kollat. 2013. “Evolutionary Multiobjective Optimization in Water Resources: The Past, Present, and Future.” Advances in Water Resources 51: 438–456. https://doi.org/10.1016/J.ADVWATRES.2012.01.005.
Regona, M., T. Yigitcanlar, B. Xia, and R. Y. M. Li. 2022. “Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review.” Journal of Open Innovation: Technology, Market, and Complexity 8, no. 1: 45. https://doi.org/10.3390/JOITMC8010045.
Reynolds, K. N., T. D. Loecke, A. J. Burgin, et al. 2016. “Optimizing Sampling Strategies for Riverine Nitrate Using High‐Frequency Data in Agricultural Watersheds.” Environmental Science and Technology 50, no. 12: 6406–6414. https://doi.org/10.1021/ACS.EST.5B05423.
Rode, M., G. Arhonditsis, D. Balin, et al. 2010. “New Challenges in Integrated Water Quality Modelling.” Hydrological Processes 24, no. 24: 3447–3461. https://doi.org/10.1002/HYP.7766.
Roy, D. K., and B. Datta. 2018. “A Review of Surrogate Models and Their Ensembles to Develop Saltwater Intrusion Management Strategies in Coastal Aquifers.” Earth Systems and Environment 2: 193–211. https://doi.org/10.1007/s41748‐018‐0069‐3.
Roy, D. K., and B. Datta. 2025. “Deep Learning‐Based Surrogates for Multi‐Objective Optimization of the Groundwater Abstraction Schemes to Manage Seawater Intrusion Into Coastal Aquifers.” Journal of Environmental Management 377: 124592. https://doi.org/10.1016/J.JENVMAN.2025.124592.
Safari, S., S. Sharghi, R. Kerachian, and H. Noory. 2023. “A Market‐Based Mechanism for Long‐Term Groundwater Management Using Remotely Sensed Data.” Journal of Environmental Management 332: 117409. https://doi.org/10.1016/J.JENVMAN.2023.117409.
Samantaray, S., A. Sahoo, and D. K. Ghose. 2020. “Assessment of Sediment Load Concentration Using SVM, SVM‐FFA and PSR‐SVM‐FFA in Arid Watershed, India: A Case Study.” KSCE Journal of Civil Engineering 24, no. 6: 1944–1957. https://doi.org/10.1007/s12205‐020‐1889‐x.
Samantaray, S., A. Sahoo, S. Paul, and D. K. Ghose. 2022. “Prediction of Bed‐Load Sediment Using Newly Developed Support‐Vector Machine Techniques.” Journal of Irrigation and Drainage Engineering 148, no. 10: 04022034. https://doi.org/10.1061/(ASCE)IR.1943‐4774.0001689.
Saravi, B., A. P. Nejadhashemi, P. Jha, and B. Tang. 2021. “Reducing Deep Learning Network Structure Through Variable Reduction Methods in Crop Modeling.” Artificial Intelligence in Agriculture 5: 196–207. https://doi.org/10.1016/J.AIIA.2021.09.001.
Sayers, W., D. Savic, and Z. Kapelan. 2019. “Performance of LEMMO With Artificial Neural Networks for Water Systems Optimisation.” Urban Water Journal 16, no. 1: 21–32. https://doi.org/10.1080/1573062X.2019.1611886.
Shaikh, M., and F. Birajdar. 2024. “Artificial Intelligence in Groundwater Management: Innovations, Challenges, and Future Prospects.” International Journal of Science and Research Archive 11, no. 1: 502–512. https://doi.org/10.30574/IJSRA.2024.11.1.0105.
Shao, H., W. Yang, J. Lindsay, Y. Liu, Z. Yu, and A. Oginskyy. 2017. “An Open Source GIS‐Based Decision Support System for Watershed Evaluation of Best Management Practices.” Journal of the American Water Resources Association 53, no. 3: 521–531. https://doi.org/10.1111/1752‐1688.12521.
Shaughnessy, A. R., X. Gu, T. Wen, and S. L. Brantley. 2021. “Machine Learning Deciphers CO2 Sequestration and Subsurface Flowpaths From Stream Chemistry.” Hydrology and Earth System Sciences 25, no. 6: 3397–3409. https://doi.org/10.5194/HESS‐25‐3397‐2021.
Shaw, A. R., H. Smith Sawyer, E. J. LeBoeuf, M. P. McDonald, and B. Hadjerioua. 2017. “Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High‐Fidelity Hydrodynamics and Water Quality Model.” Water Resources Research 53, no. 11: 9444–9461. https://doi.org/10.1002/2017WR021039.
Shen, S., C. Z. Qin, L. J. Zhu, and A. X. Zhu. 2023. “From Scenario to Roadmap: Design and Evaluation of a Web‐Based Participatory Watershed Planning System for Optimizing Multistage Implementation Plans of Management Practices Under Stepwise Investment.” Journal of Environmental Management 342: 118280. https://doi.org/10.1016/J.JENVMAN.2023.118280.
Sholagberu, A. T., M. R. Mustafa, K. W. Yusof, and A. M. Hashim. 2017. “Geostatistical Based Susceptibility Mapping of Soil Erosion and Optimization of Its Causative Factors: A Conceptual Framework.” Journal of Engineering Science and Technology 12, no. 11: 2880–2895.
Silva, J. A. 2022. “Implementation and Integration of Sustainability in the Water Industry: A Systematic Literature Review.” Sustainability 14, no. 23: 15919. https://doi.org/10.3390/SU142315919.
Singh, K. P., N. Basant, and S. Gupta. 2011. “Support Vector Machines in Water Quality Management.” Analytica Chimica Acta 703, no. 2: 152–162. https://doi.org/10.1016/J.ACA.2011.07.027.
Singh, V. P., and D. K. Frevert. 2010. Watershed Models. 1st ed. CRC Press, Taylor & Francis Group.
Sit, M., B. Z. Demiray, Z. Xiang, G. J. Ewing, Y. Sermet, and I. Demir. 2020. “A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources.” Water Science and Technology 82, no. 12: 2635–2670. https://doi.org/10.2166/wst.2020.369.
Song, C., L. Yao, C. Hua, and Q. Ni. 2021. “Comprehensive Water Quality Evaluation Based on Kernel Extreme Learning Machine Optimized With the Sparrow Search Algorithm in Luoyang River Basin, China.” Environmental Earth Sciences 80: 521. https://doi.org/10.1007/s12665‐021‐09879‐x.
Sreekanth, J., and B. Datta. 2011. “Coupled Simulation‐Optimization Model for Coastal Aquifer Management Using Genetic Programming‐Based Ensemble Surrogate Models and Multiple‐Realization Optimization.” Water Resources Research 47, no. 4: W04516. https://doi.org/10.1029/2010WR009683.
Sreekanth, J., B. Datta, and B. Datta 2014. “Stochastic and Robust Multi‐Objective Optimal Management of Pumping From Coastal Aquifers Under Parameter Uncertainty.” https://doi.org/10.1007/s11269‐014‐0591‐5.
Steefel, C. I. 2019. “Reactive Transport at the Crossroads.” Reviews in Mineralogy and Geochemistry 85: 1–26. https://doi.org/10.2138/rmg.2019.85.1.
Stevenson, M., and C. Bravo. 2019. “Advanced Turbidity Prediction for Operational Water Supply Planning.” Decision Support Systems 119: 72–84. https://doi.org/10.1016/J.DSS.2019.02.009.
Su, Q., R. Srinivasan, and R. Karthikeyan. 2024. “SC.HAWQS: A User‐Friendly Web‐Based Decision Support System for Regional Water Resources Management Under a Changing Climate.” Water Resources Management 38, no. 4: 1–18. https://doi.org/10.1007/s11269‐023‐03719‐2.
Suresh, A., and K. Deb. 2023. “Machine Learning Based Prediction of New Pareto‐Optimal Solutions From Pseudo‐Weights.” IEEE Transactions on Evolutionary Computation 28, no. 5: 1351–1365. https://doi.org/10.1109/TEVC.2023.3319494.
Syed, I., S. Ahmed, B. B. Radhakrishnan, et al. 2021. “To Cite This Article: Yagyanath Rimal et al 2021.” Journal de Physique 1804, no. 1: 12054. https://doi.org/10.1088/1742‐6596/1804/1/012054.
Tayfur, G. 2017. “Modern Optimization Methods in Water Resources Planning, Engineering and Management.” Water Resources Management 31, no. 10: 3205–3233. https://doi.org/10.1007/S11269‐017‐1694‐6/TABLES/1.
Tekinerdogan, B., and O. Ozcan 2017. “Architectural Perspective for Design and Analysis of Scalable Software as a Service Architectures.” Managing Trade‐Offs in Adaptable Software Architectures, 223–245. https://doi.org/10.1016/B978‐0‐12‐802855‐1.00010‐1.
Tiyasha, T. M. Tung, and Z. M. Yaseen. 2020. “A Survey on River Water Quality Modelling Using Artificial Intelligence Models: 2000–2020.” Journal of Hydrology 585: 124670. https://doi.org/10.1016/j.jhydrol.2020.124670.
Toscano, G., H. Razavi, A. P. Nejadhashemi, K. Deb, and Linker, L. (2023). “Utilizing Innovization to Solve Large‐Scale Multi‐Objective Chesapeake Bay Watershed Problem.” 2023 IEEE Congress on Evolutionary Computation, CEC 2023. https://doi.org/10.1109/CEC53210.2023.10254161.
Toscano‐Pulido, G., H. Razavi, A. P. Nejadhashemi, K. Deb, and L. Linker. 2024. “Large‐Scale Multiobjective Optimization for Watershed Planning and Assessment.” IEEE Transactions on Systems, Man, and Cybernetics: Systems 54, no. 6: 3471–3483. https://doi.org/10.1109/TSMC.2024.3361679.
US EPA. 2023. “Overview of Watershed Monitoring|Watershed Academy Web|US EPA.” https://cfpub.epa.gov/watertrain/moduleFrame.cfm?parent_object_id=916.
Varadharajan, C., A. P. Appling, B. Arora, et al. 2022. “Can Machine Learning Accelerate Process Understanding and Decision‐Relevant Predictions of River Water Quality?” Hydrological Processes 36, no. 4: e14565. https://doi.org/10.1002/HYP.14565.
Volk, M., S. Lautenbach, H. van Delden, L. T. H. Newham, and R. Seppelt. 2010. “How Can We Make Progress With Decision Support Systems in Landscape and River Basin Management? Lessons Learned From a Comparative Analysis of Four Different Decision Support Systems.” Environmental Management 46, no. 6: 834–849. https://doi.org/10.1007/s00267‐009‐9417‐2.
Wang, S., Y. Wang, Y. Wang, and Z. Wang. 2022. “Comparison of Multi‐Objective Evolutionary Algorithms Applied to Watershed Management Problem.” Journal of Environmental Management 324: 116255. https://doi.org/10.1016/J.JENVMAN.2022.116255.
Wardropper, C., and A. Brookfield. 2022. “Decision‐Support Systems for Water Management.” Journal of Hydrology 610: 127928. https://doi.org/10.1016/J.JHYDROL.2022.127928.
Wellen, C., A. R. Kamran‐Disfani, and G. B. Arhonditsis. 2015. “Evaluation of the Current State of Distributed Watershed Nutrient Water Quality Modeling.” Environmental Science and Technology 49, no. 6: 3278–3290. https://doi.org/10.1021/ES5049557/SUPPL_FILE/ES5049557_SI_001.PDF.
Weng, S. Q., G. H. Huang, and Y. P. Li. 2010. “An Integrated Scenario‐Based Multi‐Criteria Decision Support System for Water Resources Management and Planning—A Case Study in the Haihe River Basin.” Expert Systems With Applications 37, no. 12: 8242–8254. https://doi.org/10.1016/J.ESWA.2010.05.061.
Clemson University, William Byars, B., B. William, and S Modeling. 2023. “Surrogate Modeling of Nonlinear Components and Circuits Surrogate Modeling of Nonlinear Components and Circuits Recommended Citation Recommended Citation.” https://tigerprints.clemson.edu/all_theses.
Williams, B., and S. Cremaschi (2021). “Novel Tool for Selecting Surrogate Modeling Techniques for Surface Approximation.” pp. 451–456. https://doi.org/10.1016/B978‐0‐323‐88506‐5.50071‐1.
Wu, B., Y. Zheng, X. Wu, et al. 2015. “Optimizing Water Resources Management in Large River Basins With Integrated Surface Water‐Groundwater Modeling: A Surrogate‐Based Approach.” Water Resources Research 51, no. 4: 2153–2173. https://doi.org/10.1002/2014WR016653.
Wu, J., X. Y. Chen, H. Zhang, L. D. Xiong, H. Lei, and S. H. Deng. 2019. “Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization.” Journal of Electronic Science and Technology 17, no. 1: 26–40 10.11989/JEST.1674‐862X.80904120.
Xiang, X., Q. Li, S. Khan, and O. I. Khalaf. 2021. “Urban Water Resource Management for Sustainable Environment Planning Using Artificial Intelligence Techniques.” Environmental Impact Assessment Review 86: 106515. https://doi.org/10.1016/J.EIAR.2020.106515.
Xu, T., and F. Liang. 2021. “Machine Learning for Hydrologic Sciences: An Introductory Overview.” Wiley Interdisciplinary Reviews: Water 8, no. 5: e1533. https://doi.org/10.1002/WAT2.1533.
Yang, L., and A. Shami. 2020. “On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice.” Neurocomputing 415: 295–316. https://doi.org/10.1016/J.NEUCOM.2020.07.061.
Yao, Q., M. Wang, Y. Chen, et al. 2018). “Taking Human Out of Learning Applications: A Survey on Automated Machine Learning.” http://arxiv.org/abs/1810.13306.
Ye, Z., J. Yang, N. Zhong, X. Tu, J. Jia, and J. Wang. 2020. “Tackling Environmental Challenges in Pollution Controls Using Artificial Intelligence: A Review.” Science of the Total Environment 699: 134279. https://doi.org/10.1016/J.SCITOTENV.2019.134279.
Yin, J., F. T. C. Tsai, and C. Lu. 2022. “Bi‐Objective Extraction–Injection Optimization Modeling for Saltwater Intrusion Control Considering Surrogate Model Uncertainty.” Water Resources Management 36: 6017–6042. https://doi.org/10.1007/s11269‐022‐03340‐9.
Yoon, H., S. C. Jun, Y. Hyun, G. O. Bae, and K. K. Lee. 2011. “A Comparative Study of Artificial Neural Networks and Support Vector Machines for Predicting Groundwater Levels in a Coastal Aquifer.” Journal of Hydrology 396, no. 1–2: 128–138. https://doi.org/10.1016/J.JHYDROL.2010.11.002.
Yunkai, L., T. Yingjie, O. Zhiyun, et al. 2010. “Analysis of Soil Erosion Characteristics in Small Watersheds With Particle Swarm Optimization, Support Vector Machine, and Artificial Neuronal Networks.” Environmental Earth Sciences 60, no. 7: 1559–1568. https://doi.org/10.1007/S12665‐009‐0292‐1/FIGURES/4.
Zamani, M. G., M. R. Nikoo, S. Jahanshahi, R. Barzegar, and A. Meydani. 2023. “Forecasting Water Quality Variable Using Deep Learning and Weighted Averaging Ensemble Models.” Environmental Science and Pollution Research 30, no. 59: 124316–124340. https://doi.org/10.1007/S11356‐023‐30774‐4/FIGURES/12.
Zanganeh, M. 2020. “Improvement of the ANFIS‐Based Wave Predictor Models by the Particle Swarm Optimization.” Journal of Ocean Engineering and Science 5, no. 1: 84–99. https://doi.org/10.1016/J.JOES.2019.09.002.
Zeng, Y., Y. Cai, P. Jia, and H. Jee. 2012. “Development of a Web‐Based Decision Support System for Supporting Integrated Water Resources Management in Daegu City, South Korea.” Expert Systems With Applications 39, no. 11: 10091–10102. https://doi.org/10.1016/J.ESWA.2012.02.065.
Zhai, Z., J. F. Martínez, V. Beltran, and N. L. Martínez. 2020. “Decision Support Systems for Agriculture 4.0: Survey and Challenges.” Computers and Electronics in Agriculture 170: 105256. https://doi.org/10.1016/J.COMPAG.2020.105256.
Zhu, F., P. A. Zhong, Q. Cao, J. Chen, Y. Sun, and J. Fu. 2019. “A Stochastic Multi‐Criteria Decision Making Framework for Robust Water Resources Management Under Uncertainty.” Journal of Hydrology 576: 287–298. https://doi.org/10.1016/J.JHYDROL.2019.06.049.
Alemu, E. T., R. N. Palmer, A. Polebitski, and B. Meaker. 2011. “Decision Support System for Optimizing Reservoir Operations Using Ensemble Streamflow Predictions.” Journal of Water Resources Planning and Management 137, no. 1: 72–82. https://doi.org/10.1061/(ASCE)WR.1943‐5452.0000088/ASSET/79388153‐EC79‐4123‐A441‐3258C4F7E7BE/ASSETS/IMAGES/LARGE/7.JPG.
Alsabt, R., Y. A. Adenle, and H. M. Alshuwaikhat. 2024. “Exploring the Roles, Future Impacts, and Strategic Integration of Artificial Intelligence in the Optimization of Smart City—From Systematic Literature Review to Conceptual Model.” Sustainability 2024 16, no. 8: 3389. https://doi.org/10.3390/SU16083389.
Arora, B., D. Dwivedi, S. S. Hubbard, C. I. Steefel, and K. H. Williams. 2016. “Identifying Geochemical hot Moments and Their Controls on a Contaminated River Floodplain System Using Wavelet and Entropy Approaches.” Environmental Modelling & Software 85: 27–41. https://doi.org/10.1016/J.ENVSOFT.2016.08.005.
AWS. 2023. “What is Hyperparameter Tuning? Hyperparameter Tuning Methods Explained—AWS.” https://aws.amazon.com/what‐is/hyperparameter‐tuning/.
Azad, A., H. Karami, S. Farzin, S. F. Mousavi, and O. Kisi. 2019. “Modeling River Water Quality Parameters Using Modified Adaptive Neuro Fuzzy Inference System.” Water Science and Engineering 12, no. 1: 45–54. https://doi.org/10.1016/j.wse.2018.11.001.
Balson, T., and A. S. Ward. 2022. “A Machine Learning Approach to Water Quality Forecasts and Sensor Network Expansion: Case Study in the Wabash River Basin, United States.” Hydrological Processes 36, no. 6: e14619. https://doi.org/10.1002/HYP.14619.
Barcellos, D. D. S., G. M. K. Fernandes, and F. T. de Souza. 2021. “Data Based Model for Predicting COVID‐19 Morbidity and Mortality in Metropolis.” Scientific Reports 11, no. 1: 1–11. https://doi.org/10.1038/s41598‐021‐04029‐6.
Behmel, S., M. Damour, R. Ludwig, and M. J. Rodriguez. 2016. “Water Quality Monitoring Strategies — A Review and Future Perspectives.” Science of the Total Environment 571: 1312–1329. https://doi.org/10.1016/J.SCITOTENV.2016.06.235.
Behmel, S., M. Damour, R. Ludwig, and M. J. Rodriguez. 2021. “Intelligent Decision‐Support System to Plan, Manage and Optimize Water Quality Monitoring Programs: Design of a Conceptual Framework.” Journal of Environmental Planning and Management 64, no. 4: 703–733. https://doi.org/10.1080/09640568.2020.1782858.
Bender, M. J., and S. P. Simonovic. 1996. “Systems Approach for Collaborative Decision Support in Water Resources Planning.” International Symposium on Technology and Society: 357–363. https://doi.org/10.1109/ISTAS.1996.541172.
Bernhardt, E. S., J. R. Blaszczak, C. D. Ficken, M. L. Fork, K. E. Kaiser, and E. C. Seybold. 2017. “Control Points in Ecosystems: Moving Beyond the hot Spot hot Moment Concept.” Ecosystems 20, no. 4: 665–682. https://doi.org/10.1007/S10021‐016‐0103‐Y/METRICS.
Bhosekar, A., and M. Ierapetritou. 2018. “Advances in Surrogate Based Modeling, Feasibility Analysis, and Optimization: A Review.” Computers and Chemical Engineering 108: 250–267. https://doi.org/10.1016/j.compchemeng.2017.09.017.
Bilgin, A., and M. U. Konanç. 2016. “Evaluation of Surface Water Quality and Heavy Metal Pollution of Coruh River Basin (Turkey) by Multivariate Statistical Methods.” Environmental Earth Sciences 75, no. 12: 1029. https://doi.org/10.1007/s12665‐016‐5821‐0.
Bischl, B., M. Binder, M. Lang, et al. 2021. “Hyperparameter Optimization: Foundations, Algorithms, Best Practices, and Open Challenges.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13, no. 2: e1484. http://arxiv.org/abs/2107.05847.
Bishop, C. M. 1995. “Neural Networks for Pattern Recognition.” https://doi.org/10.1093/OSO/9780198538493.001.0001.
Bouguettaya, A., H. Zarzour, A. Kechida, and A. M. Taberkit. 2022. “Deep Learning Techniques to Classify Agricultural Crops Through UAV Imagery: A Review.” Neural Computing and Applications 34, no. 12: 9511–9536 10.1007/S00521‐022‐07104‐9.
Brentan, B., S. Carpitella, D. Barros, G. Meirelles, A. Certa, and J. Izquierdo. 2021. “Water Quality Sensor Placement: A Multi‐Objective and Multi‐Criteria Approach.” Water Resources Management 35, no. 1: 225–241. https://doi.org/10.1007/S11269‐020‐02720‐3.
Budamala, V., and A. Baburao Mahindrakar. 2020. “Integration of Adaptive Emulators and Sensitivity Analysis for Enhancement of Complex Hydrological Models.” Environmental Processes 7, no. 4: 1235–1253. https://doi.org/10.1007/S40710‐020‐00468‐X/TABLES/2.
Bui, D. T., K. Khosravi, J. Tiefenbacher, H. Nguyen, and N. Kazakis. 2020. “Improving Prediction of Water Quality Indices Using Novel Hybrid Machine‐Learning Algorithms.” Science of the Total Environment 721: 137612. https://doi.org/10.1016/J.SCITOTENV.2020.137612.
Cai, X., L. Wu, T. Zhao, D. Wu, W. Zhang, and J. Chen. 2024. “Dynamic Adaptive Multi‐Objective Optimization Algorithm Based on Type Detection.” Information Sciences 654: 119867. https://doi.org/10.1016/J.INS.2023.119867.
Calazans, G. M., C. C. Pinto, E. P. da Costa, A. F. Perini, and S. C. Oliveira. 2018a. “The use of Multivariate Statistical Methods for Optimization of the Surface Water Quality Network Monitoring in the Paraopeba River Basin, Brazil.” Environmental Monitoring and Assessment 190, no. 8: 491. https://doi.org/10.1007/s10661‐018‐6873‐2.
Calazans, G. M., C. C. Pinto, E. P. da Costa, A. F. Perini, and S. C. Oliveira. 2018b. “Using Multivariate Techniques as a Strategy to Guide Optimization Projects for the Surface Water Quality Network Monitoring in the Velhas River Basin, Brazil.” Environmental Monitoring and Assessment 190, no. 12: 1–15. https://doi.org/10.1007/S10661‐018‐7099‐Z/TABLES/4.
Camara, M., N. R. Jamil, A. F. Bin Abdullah, R. binti Hashim, and A. G. Aliyu. 2020. “Economic and Efficiency Based Optimisation of Water Quality Monitoring Network for Land Use Impact Assessment.” Science of the Total Environment 737: 139800. https://doi.org/10.1016/J.SCITOTENV.2020.139800.
Castelletti, A., F. Pianosi, M. Restelli, A. Castelletti, F. Pianosi, and M. Restelli. 2013. “A Multiobjective Reinforcement Learning Approach to Water Resources Systems Operation: Pareto Frontier Approximation in a Single run.” WRR 49, no. 6: 3476–3486. https://doi.org/10.1002/WRCR.20295.
Cawley, G. C., and N. L. C. Talbot. 2010. “On Over‐Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation.” Journal of Machine Learning Research 11: 2079–2107.
Chang, F.‐J., L.‐C. Chang, J. Chen, F.‐J. Chang, L.‐C. Chang, and J.‐F. Chen. 2023. “Artificial Intelligence Techniques in Hydrology and Water Resources Management.” Water 15, no. 10: 1846. https://doi.org/10.3390/W15101846.
Chang, Z., Z. Guo, K. Chen, et al., 2024. “A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity.” https://doi.org/10.1029/2023WR036051.
Chapman, D. V., S. Warner, and C. Dickens 2021. “Approaches to Water Monitoring.” 1–11. https://doi.org/10.1007/978‐3‐319‐70061‐8_96‐1.
Chapman, D. V., S. Warner, and C. Dickens 2022. “Approaches to Water Monitoring.” 36–46. https://doi.org/10.1007/978‐3‐319‐95846‐0_96.
Chen, W., D. Nover, H. Yen, et al. 2020. “Exploring the Multiscale Hydrologic Regulation of Multipond Systems in a Humid Agricultural Catchment.” Water Research 184: 115987. https://doi.org/10.1016/J.WATRES.2020.115987.
Chen, Y., L. Song, Y. Liu, L. Yang, and D. Li. 2020. “A Review of the Artificial Neural Network Models for Water Quality Prediction.” Applied Sciences 10, no. 17: 5776. https://doi.org/10.3390/APP10175776.
Clausen, H. S., N. H. Ebdrup, I. M. Barsøe, et al. 2020. “Association Between Drinking Water Nitrate and Adverse Reproductive Outcomes: A Systematic PRISMA Review.” Water 12, no. 8: 2287. https://doi.org/10.3390/W12082287.
Coelho, A. C., J. W. Labadie, and D. G. Fontane. 2012. “Multicriteria Decision Support System for Regionalization of Integrated Water Resources Management.” Water Resources Management 26, no. 5: 1325–1346. https://doi.org/10.1007/S11269‐011‐9961‐4/FIGURES/11.
Cosgrove, W. J., and D. P. Loucks. 2015. “Water Management: Current and Future Challenges and Research Directions.” Water Resources Research 51, no. 6: 4823–4839. https://doi.org/10.1002/2014WR016869.
da Silveira Barcellos, D., and F. T. de Souza. 2022. “Optimization of Water Quality Monitoring Programs by Data Mining.” Water Research 221: 118805. https://doi.org/10.1016/J.WATRES.2022.118805.
Datta B. 2005. “(16) (PDF) Optimization Applications in Water Resources Systems Engineering.” https://www.researchgate.net/publication/242115216_Optimization_Applications_in_Water_Resources_Systems_Engineering.
Deb, C., F. Zhang, J. Yang, S. E. Lee, and K. W. Shah. 2017. “A Review on Time Series Forecasting Techniques for Building Energy Consumption.” Renewable and Sustainable Energy Reviews 74: 902–924. https://doi.org/10.1016/J.RSER.2017.02.085.
Deb, K., S. Bandaru, D. Greiner, A. Gaspar‐Cunha, and C. C. Tutum. 2014. “An Integrated Approach to Automated Innovization for Discovering Useful Design Principles: Case Studies From Engineering.” Applied Soft Computing Journal 15: 42–56. https://doi.org/10.1016/J.ASOC.2013.10.011.
Deb, K., and R. Datta. 2012. “Hybrid Evolutionary Multi‐Objective Optimization and Analysis of Machining Operations.” Engineering Optimization 44, no. 6: 685–706. https://doi.org/10.1080/0305215X.2011.604316.
DeCastro‐García, N., Á. L. Muñoz Castañeda, D. Escudero García, and M. V. Carriegos. 2019. “Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase Over the Efficiency of a Machine Learning Algorithm.” Complexity 2019: 1–16. https://doi.org/10.1155/2019/6278908.
Dehghani, A., H. M. Z. H. Moazam, F. Mortazavizadeh, et al. 2023. “Comparative Evaluation of LSTM, CNN, and ConvLSTM for Hourly Short‐Term Streamflow Forecasting Using Deep Learning Approaches.” Ecological Informatics 75: 102119. https://doi.org/10.1016/J.ECOINF.2023.102119.
Deng, W., and G. Wang. 2017. “A Novel Water Quality Data Analysis Framework Based on Time‐Series Data Mining.” Journal of Environmental Management 196: 365–375. https://doi.org/10.1016/J.JENVMAN.2017.03.024.
Diaz, G. I., A. Fokoue‐Nkoutche, G. Nannicini, and H. Samulowitz. 2017. “An Effective Algorithm for Hyperparameter Optimization of Neural Networks.” IBM Journal of Research and Development 61, no. 4: 9:1–9:11. https://doi.org/10.1147/JRD.2017.2709578.
Dilmi, S., and M. Ladjal. 2021. “A Novel Approach for Water Quality Classification Based on the Integration of Deep Learning and Feature Extraction Techniques.” Chemometrics and Intelligent Laboratory Systems 214: 104329. https://doi.org/10.1016/J.CHEMOLAB.2021.104329.
Dong, F., J. Li, C. Dai, et al. 2022. “Understanding Robustness in Multiscale Nutrient Abatement: Probabilistic Simulation‐Optimization Using Bayesian Network Emulators.” Journal of Cleaner Production 378: 134394. https://doi.org/10.1016/J.JCLEPRO.2022.134394.
Dorigo, M., V. Maniezzo, and A. Colorni. 1996. “Ant System: Optimization by a Colony of Cooperating Agents.” IEEE Transactions on Systems, man, and Cybernetics, Part B: Cybernetics 26, no. 1: 29–41. https://doi.org/10.1109/3477.484436.
Duobiene, S., K. Ratautas, R. Trusovas, et al. 2022. “Development of Wireless Sensor Network for Environment Monitoring and Its Implementation Using SSAIL Technology.” Sensors 22, no. 14: 5343. https://doi.org/10.3390/S22145343.
Duvvuri, S. P., and J. Anmala. 2019. “Fecal Coliform Predictive Model Using Genetic Algorithm‐Based Radial Basis Function Neural Networks (GA‐RBFNNs).” Neural Computing and Applications 31, no. 12: 8393–8409. https://doi.org/10.1007/S00521‐019‐04520‐2/TABLES/1.
Ehteram, M., A. Ferdowsi, M. Faramarzpour, et al. 2021. “Hybridization of Artificial Intelligence Models With Nature Inspired Optimization Algorithms for Lake Water Level Prediction and Uncertainty Analysis.” Alexandria Engineering Journal 60, no. 2: 2193–2208. https://doi.org/10.1016/J.AEJ.2020.12.034.
Eini, M. R., M. A. Olyaei, T. Kamyab, J. Teymoori, L. Brocca, and M. Piniewski. 2021. “Evaluating Three non‐gauge‐Corrected Satellite Precipitation Estimates by a Regional Gauge Interpolated Dataset Over Iran.” Journal of Hydrology: Regional Studies 38: 100942. https://doi.org/10.1016/J.EJRH.2021.100942.
Elshaikh, A., E. Elsheikh, and J. Mabrouki. 2024. “Applications of Artificial Intelligence in Precision Irrigation.” Journal of Environment and Earth Science 6, no. 2: 176–186 10.30564/JEES.V6I2.6679.
Elshorbagy, A., G. Corzo, S. Srinivasulu, and D. P. Solomatine. 2010. “Experimental Investigation of the Predictive Capabilities of Data Driven Modeling Techniques in Hydrology ‐ Part 1: Concepts and Methodology.” Hydrology and Earth System Sciences 14, no. 10: 1931–1941. https://doi.org/10.5194/HESS‐14‐1931‐2010.
Gardezi, M., B. Joshi, D. M. Rizzo, et al. 2023. “Artificial Intelligence in Farming: Challenges and Opportunities for Building Trust.” Agronomy Journal 116, no. 3: 1217–1228. https://doi.org/10.1002/AGJ2.21353.
Garzón, A., Z. Kapelan, J. Langeveld, and R. Taormina. 2022. “Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions.” Water Resources Research 58, no. 5: e2021WR031808. https://doi.org/10.1029/2021WR031808.
Gaur, A., and K. Deb 2017. “Effect of Size and Order of Variables in Rules for Multi‐Objective Repair‐Based Innovization Procedure.” 2017 IEEE Congress on Evolutionary Computation, CEC 2017 ‐ Proceedings, 2177–2184. https://doi.org/10.1109/CEC.2017.7969568.
Ge, Y., F. Han, F. Wu, et al. 2024. “Sustainable Decision Making Based on Systems Integration and Decision Support System Promoting Endorheic Basin Sustainability.” https://doi.org/10.1016/j.dss.2024.114169.
Ghahramani, Z. 2015. “Probabilistic Machine Learning and Artificial Intelligence.” Nature 521, no. 7553: 452–459 10.1038/nature14541.
Giri, S., and A. P. Nejadhashemi. 2014. “Application of Analytical Hierarchy Process for Effective Selection of Agricultural Best Management Practices.” Journal of Environmental Management 132: 165–177. https://doi.org/10.1016/J.JENVMAN.2013.10.021.
Giupponi, C., and A. Sgobbi. 2013. “Decision Support Systems for Water Resources Management in Developing Countries: Learning From Experiences in Africa.” Water 5, no. 2: 798–818. https://doi.org/10.3390/W5020798.
Goap, A., D. Sharma, A. K. Shukla, and C. Rama Krishna. 2018. “An IoT Based Smart Irrigation Management System Using Machine Learning and Open Source Technologies.” Computers and Electronics in Agriculture 155: 41–49. https://doi.org/10.1016/J.COMPAG.2018.09.040.
González Perea, R., E. Camacho Poyato, and J. A. Rodríguez Díaz 2024. “Attention Is all Water Need: Multistep Time Series Irrigation Water Demand Forecasting in Irrigation Districts.” https://doi.org/10.1016/j.compag.2024.108723.
Guo, D., A. Lintern, J. A. Webb, et al. 2019. “Key Factors Affecting Temporal Variability in Stream Water Quality.” Water Resources Research 55, no. 1: 112–129. https://doi.org/10.1029/2018WR023370.
Guo, X., J. Luo, W. Lu, G. Dong, and Z. Pan. 2024. “Optimal Design of Groundwater Pollution Monitoring Network Based on a Back‐Propagation Neural Network Surrogate Model and Grey Wolf Optimizer Algorithm Under Uncertainty.” Environmental Monitoring and Assessment 196, no. 2: 1–18. https://doi.org/10.1007/S10661‐023‐12276‐5/FIGURES/7.
Gupta, S., and S. K. Gupta. 2024. “Water resource mapping, monitoring, and modeling using geospatial approaches.” Developments in Environmental Science 16: 575–602. https://doi.org/10.1016/B978‐0‐443‐23665‐5.00025‐9.
Hajgató, G., G. Paál, and B. Gyires‐Tóth. 2020. “Deep Reinforcement Learning for Real‐Time Optimization of Pumps in Water Distribution Systems.” Journal of Water Resources Planning and Management 146, no. 11: 04020079.
Hassija, V., V. Chamola, A. Mahapatra, et al. 2024. “Interpreting Black‐Box Models: A Review on Explainable Artificial Intelligence.” Cognitive Computation 16, no. 1: 45–74. https://doi.org/10.1007/S12559‐023‐10179‐8/FIGURES/13.
Haxton, T., R. Murray, W. Hart, K. Klise, and C. Phillips 2011. “Formulation of Chlorine and Decontamination Booster Station Optimization Problem.” World Environmental and Water Resources Congress 2011: Bearing Knowledge for Sustainability ‐ Proceedings of the 2011 World Environmental and Water Resources Congress, 199–205. https://doi.org/10.1061/41173(414)21.
He, G., T. Zhang, F. Zheng, and Q. Zhang 2018. “An Efficient Multi‐Objective Optimization Method for Water Quality Sensor Placement Within Water Distribution Systems Considering Contamination Probability Variations.” https://doi.org/10.1016/j.watres.2018.06.041.
Hou, Z., W. Lao, Y. Wang, and W. Lu. 2021. “Homotopy‐Based Hyper‐Heuristic Searching Approach for Reciprocal Feedback Inversion of Groundwater Contamination Source and Aquifer Parameters.” Applied Soft Computing 104: 107191. https://doi.org/10.1016/J.ASOC.2021.107191.
Huang, C., Y. Chen, S. Zhang, and J. Wu. 2018. “Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review.” Reviews of Geophysics 56, no. 2: 333–360. https://doi.org/10.1029/2018RG000598.
Hutter F., L. Kotthoff, and J. Vanschoren. 2019. “Automated Machine Learning.” 219. https://doi.org/10.1007/978‐3‐030‐05318‐5.
Injadat, M. N., A. Moubayed, A. B. Nassif, and A. Shami. 2020. “Systematic Ensemble Model Selection Approach for Educational Data Mining.” Knowledge‐Based Systems 200: 105992. https://doi.org/10.1016/J.KNOSYS.2020.105992.
Injadat, M., F. Salo, A. B. Nassif, A. Essex, and A. Shami 2018. “Bayesian Optimization With Machine Learning Algorithms Towards Anomaly Detection.” 2018 IEEE Global Communications Conference, GLOBECOM 2018 ‐ Proceedings. https://doi.org/10.1109/GLOCOM.2018.8647714.
Jain, A., S. Rallapalli, and D. Kumar. 2022. “Cloud‐Based Neuro‐Fuzzy Hydro‐Climatic Model for Water Quality Assessment Under Uncertainty and Sensitivity.” Environmental Science and Pollution Research 29, no. 43: 65259–65275. https://doi.org/10.1007/s11356‐022‐20385‐w.
Jaiswal, R. K., A. K. Lohani, and H. L. Tiwari. 2021. “A Decision Support System Framework for Strategic Water Resources Planning and Management Under Projected Climate Scenarios for a Reservoir Complex.” Journal of Hydrology 603: 127051. https://doi.org/10.1016/J.JHYDROL.2021.127051.
James, E. G. 2012. “Fixed‐Point Drift and Hysteresis in Frequency‐Scaled Unimanual Coordination.” Journal of Motor Behavior 44, no. 4: 281–288. https://doi.org/10.1080/00222895.2012.702141.
Javidi Sabbaghian, R., M. Zarghami, A. P. Nejadhashemi, M. B. Sharifi, M. R. Herman, and F. Daneshvar. 2016. “Application of Risk‐Based Multiple Criteria Decision Analysis for Selection of the Best Agricultural Scenario for Effective Watershed Management.” Journal of Environmental Management 168: 260–272. https://doi.org/10.1016/j.jenvman.2015.11.038.
Ji, C., Y. Cao, X. Li, et al. 2024. “A Review of the Satellite Remote Sensing Techniques for Assessment of Runoff and Sediment in Soil Erosion.” Journal Of Hydrology And Hydromechanics 72: 252–267. https://doi.org/10.2478/johh‐2024‐0009.
Jin, Y., H. Wang, and C. Sun. 2021. “Data‐Driven Evolutionary Optimization: Integrating Evolutionary Computation, Machine Learning and Data Science.” In Studies in Computational Intelligence, 1st ed., Vol. 975. Springer.
Jordan, M. I., and T. M. Mitchell. 2015. “Machine Learning: Trends, Perspectives, and Prospects.” Science 349, no. 6245: 255–260. https://doi.org/10.1126/SCIENCE.AAA8415/ASSET/AB2EF18A‐576D‐464D‐B1B6‐1301159EE29A/ASSETS/GRAPHIC/349_255_F5.JPEG.
Kamali, F., and R. Kerachian. 2025. “Applications of Behavioral Simulation Models in Water Resources Management: A Systematic State‐of‐the‐Art Review.” Water Resources Management 39, no. 13: 6715–6736. https://doi.org/10.1007/s11269‐025‐04287‐3.
Kamyab, H., T. Khademi, S. Chelliapan, et al. 2023. “The Latest Innovative Avenues for the Utilization of Artificial Intelligence and big Data Analytics in Water Resource Management.” Results in Engineering 20: 101566. https://doi.org/10.1016/J.RINENG.2023.101566.
Karthick, K. 2024. “Comprehensive Overview of Optimization Techniques in Machine Learning Training.” Control Systems and Optimization Letters 2, no. 1: 23–27 10.59247/CSOL.V2I1.69.
Kaufman, D. E., G. W. Shenk, G. Bhatt, et al. 2021. “Supporting Cost‐Effective Watershed Management Strategies for Chesapeake Bay Using a Modeling and Optimization Framework.” Environmental Modelling & Software 144: 105141. https://doi.org/10.1016/J.ENVSOFT.2021.105141.
Kennedy, J., and R. Eberhart. 1995. “Particle Swarm Optimization.” Proceedings of ICNN’95 ‐ International Conference on Neural Networks 4: 1942–1948. https://doi.org/10.1109/ICNN.1995.488968.
Khan, R., M. Zakarya, V. Balasubramanian, M. A. Jan, and V. G. Menon. 2021. “Smart Sensing‐Enabled Decision Support System for Water Scheduling in Orange Orchard.” IEEE Sensors Journal 21, no. 16: 17492–17499. https://doi.org/10.1109/JSEN.2020.3012511.
Kim, M., Y. Kim, H. Kim, W. Piao, and C. Kim. 2016. “Enhanced Monitoring of Water Quality Variation in Nakdong River Downstream Using Multivariate Statistical Techniques.” Desalination and Water Treatment 57, no. 27: 12508–12517. https://doi.org/10.1080/19443994.2015.1049963.
Koch, P., B. Wujek, O. Golovidov, J. Griffin, S. Gardner, and Y. Xu 2018. “Autotune: A Derivative‐Free Optimization Framework for Hyperparameter Tuning.” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 443–452. https://doi.org/10.1145/3219819.3219837.
Koulamas, C., and M. T. Lazarescu. 2020. “Real‐Time Sensor Networks and Systems for the Industrial IOT: What Next?” Sensors (Switzerland) 20, no. 18: 1–3. https://doi.org/10.3390/S20185023.
Krishnan, S. R., M. K. Nallakaruppan, R. Chengoden, et al. 2022. “Smart Water Resource Management Using Artificial Intelligence—A Review.” Sustainability 14, no. 20: 13384. https://doi.org/10.3390/SU142013384.
Kuhn, M., and K. Johnson 2016. “Applied Predictive Modeling”.
Kumar, D., S. Sukrit, M. Kalyanmoy, D. Erik, and D. Goodman 2021. “Genetic and Evolutionary Computation Machine Learning Assisted Evolutionary Multi‐and Many‐Objective Optimization.” https://www.springer.com/series/6008.
Le, X. H. V. Ho, G. Lee, and S. Jung. 2019. “Application of Long Short‐Term Memory (LSTM) Neural Network for Flood Forecasting.” Water 11, no. 7: 1387. https://doi.org/10.3390/w11071387.
Li, E., and X. Ma. 2019. “Dynamic Multi‐objective Optimization Algorithm based on Transfer Learning for Environmental Protection.” Ekoloji 28, no. 107: 2509–2519.
Li, G., P. Shi, S. Qu, et al. 2025. “Adaptive Surrogate Model Assisted Swarm Intelligence for Parameter Inversion of Complex Hydrological Models.” Environmental Modelling & Software 186: 106353. https://doi.org/10.1016/J.ENVSOFT.2025.106353.
Li, P. H., H. H. Kwon, L. Sun, U. Lall, and J. J. Kao. 2010. “A Modified Support Vector Machine Based Prediction Model on Streamflow at the Shihmen Reservoir, Taiwan.” International Journal of Climatology 30, no. 8: 1256–1268. https://doi.org/10.1002/JOC.1954.
Lim, B., and S. Zohren. 2021. “Time‐Series Forecasting With Deep Learning: A Survey.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379: 2194. https://doi.org/10.1098/RSTA.2020.0209.
Lintern, A., J. A. Webb, D. Ryu, et al. 2018. “What Are the key Catchment Characteristics Affecting Spatial Differences in Riverine Water Quality?” Water Resources Research 54, no. 10: 7252–7272. https://doi.org/10.1029/2017WR022172.
Liu, Y., J. Luo, Y. Xiong, Y. Ji, and X. Xin. 2022. “Inversion of Hydrogeological Parameters Based on an Adaptive Dynamic Surrogate Model.” Hydrogeology Journal 30, no. 5: 1513–1527. https://doi.org/10.1007/S10040‐022‐02493‐6/FIGURES/11.
Lorenzo, P. R., J. Nalepa, M. Kawulok, L. S. Ramos, and J. R. Pastor. 2017. “Particle Swarm Optimization for Hyper‐Parameter Selection in Deep Neural Networks.” Proceedings of the Genetic and Evolutionary Computation Conference 8: 481–488. https://doi.org/10.1145/3071178.3071208.
Luo, G. 2016. “A Review of Automatic Selection Methods for Machine Learning Algorithms and Hyper‐Parameter Values.” Network Modeling Analysis in Health Informatics and Bioinformatics 5, no. 1: 1–16. https://doi.org/10.1007/S13721‐016‐0125‐6/FIGURES/2.
Luo, Q., D. Peng, W. Shang, et al. 2023. “Water Quality Analysis Based on LSTM and BP Optimization With a Transfer Learning Model.” Environmental Science and Pollution Research 30, no. 59: 124341–124352. https://doi.org/10.1007/s11356‐023‐31068‐5.
Maclaurin, D., D. Duvenaud, and R. P. Adams 2015. “Gradient‐Based Hyperparameter Optimization Through Reversible Learning.” https://arxiv.org/abs/1502.03492v3.
Maier, H. R., A. Jain, G. C. Dandy, and K. P. Sudheer. 2010. “Methods Used for the Development of Neural Networks for the Prediction of Water Resource Variables in River Systems: Current Status and Future Directions.” Environmental Modelling & Software 25, no. 8: 891–909. https://doi.org/10.1016/J.ENVSOFT.2010.02.003.
Maier, H. R., Z. Kapelan, J. Kasprzyk, et al. 2014. “Evolutionary Algorithms and Other Metaheuristics in Water Resources: Current Status, Research Challenges and Future Directions.” Environmental Modelling and Software 62: 271–299. https://doi.org/10.1016/j.envsoft.2014.09.013.
Malitsky, Y., D. Mehta, B. O'Sullivan, and H. Simonis 2013. “Tuning Parameters of Large Neighborhood Search for the Machine Reassignment Problem.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7874 LNCS, 176–192. https://doi.org/10.1007/978‐3‐642‐38171‐3_12/COVER.
Mirghani, B. Y., E. M. Zechman, R. S. Ranjithan, and G. Mahinthakumar. 2012. “Enhanced Simulation‐Optimization Approach Using Surrogate Modeling for Solving Inverse Problems.” Environmental Forensics 13, no. 4: 348–363. https://doi.org/10.1080/15275922.2012.702333.
Mirzaei, M., H. Yu, A. Dehghani, et al. 2021. “A Novel Stacked Long Short‐Term Memory Approach of Deep Learning for Streamflow Simulation.” Sustainability 13, no. 23: 13384. https://doi.org/10.3390/SU132313384.
Mittal, S., D. K. Saxena, K. Deb, and E. D. Goodman. 2020. “Enhanced Innovized Repair Operator for Evolutionary Multi‐ and Many‐Objective Optimization.” IEEE Transactions on Evolutionary Computation 26, no. 5: 961–975. https://doi.org/10.1109/TEVC.2021.3131952.
Mittal, S., D. K. Saxena, K. Deb, and E. D. Goodman. 2021. “A Learning‐Based Innovized Progress Operator for Faster Convergence in Evolutionary Multi‐Objective Optimization.” ACM Transactions on Evolutionary Learning and Optimization (TELO) 2, no. 1: 1–29. https://doi.org/10.1145/3474059.
Mittal, S., D. K. Saxena, K. Deb, and E. D. Goodman. 2023. “A Unified Innovized Progress Operator for Performance Enhancement in Evolutionary Multi‐ and Many‐Objective Optimization.” IEEE Transactions on Evolutionary Computation 28, no. 6: 1605–1619. https://doi.org/10.1109/TEVC.2023.3321603.
Moher, D., A. Liberati, J. Tetzlaff, and D. G. Altman. 2010. “Preferred Reporting Items for Systematic Reviews and meta‐Analyses: The PRISMA Statement.” International Journal of Surgery 8, no. 5: 336–341. https://doi.org/10.1016/J.IJSU.2010.02.007.
Moro‐Visconti, R., S. Cruz Rambaud, and J. López Pascual. 2023. “Artificial Intelligence‐Driven Scalability and Its Impact on the Sustainability and Valuation of Traditional Firms.” Humanities and Social Sciences Communications 10, no. 1: 1–14 10.1057/s41599‐023‐02214‐8.
Nakhaei, M., A. Ahmadi, M. Gheibi, B. Chahkandi, M. Hajiaghaei‐Keshteli, and K. Behzadian. 2023. “A Smart Sustainable Decision Support System for Water Management of Power Plants in Water Stress Regions.” Expert Systems With Applications 230: 120752. https://doi.org/10.1016/J.ESWA.2023.120752.
Nguyen, T. H., B. Helm, H. Hettiarachchi, S. Caucci, and P. Krebs. 2020. “Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany.” Water 12, no. 2: 420. https://doi.org/10.3390/w12020420.
Ni, Q., L. Wang, R. Ye, F. Yang, and M. Sivakumar 2010. “Evolutionary Modeling for Streamflow Forecasting With Minimal Datasets: A Case Study in the West Malian River, China.” www.liebertpub.com.
Noori, N., and L. Kalin. 2016. “Coupling SWAT and ANN Models for Enhanced Daily Streamflow Prediction.” Journal of Hydrology 533: 141–151. https://doi.org/10.1016/J.JHYDROL.2015.11.050.
Nourani, V., A. Hosseini Baghanam, J. Adamowski, and O. Kisi. 2014. “Applications of Hybrid Wavelet–Artificial Intelligence Models in Hydrology: A Review.” Journal of Hydrology 514: 358–377. https://doi.org/10.1016/J.JHYDROL.2014.03.057.
Nova, K. 2023. “AI‐Enabled Water Management Systems: An Analysis of System Components and Interdependencies for Water Conservation.” Eigenpub Review of Science and Technology 7, no. 1: 105–124. https://studies.eigenpub.com/index.php/erst/article/view/12.
Nwabueze, M. O., A. Aliyu, K. J. Adegbo, and C. D. Ikemefuna. 2024. “Enhancing Machine Optimization Through AI‐Driven Data Analysis and Gathering: Leveraging Integrated Systems and Hybrid Technology for Industrial Efficiency.” World Journal of Advanced Research and Reviews 23, no. 3: 1919–1943. https://doi.org/10.30574/WJARR.2024.23.3.2882.
Olatinwo, S. O., and T. H. Joubert. 2023. “Resource Allocation Optimization in IoT‐Enabled Water Quality Monitoring Systems.” Sensors 23, no. 21: 8963. https://doi.org/10.3390/S23218963.
Page, M. J., J. E. McKenzie, P. M. Bossuyt, et al. 2021. “The PRISMA 2020 Statement: an Updated Guideline for Reporting Systematic Reviews.” BMJ (Clinical Research Ed.) 372: n71. https://doi.org/10.1136/bmj.n71.
Pallottino, S., G. M. Sechi, and P. Zuddas. 2005. “A DSS for Water Resources Management Under Uncertainty by Scenario Analysis.” Environmental Modelling & Software 20, no. 8: 1031–1042. https://doi.org/10.1016/J.ENVSOFT.2004.09.012.
Park, Y., H. K. Lee, J. K. Shin, et al. 2021. “A Machine Learning Approach for Early Warning of Cyanobacterial Bloom Outbreaks in a Freshwater Reservoir.” Journal of Environmental Management 288: 112415. https://doi.org/10.1016/J.JENVMAN.2021.112415.
Peña‐Guzmán, C., L. Soto, and A. Diaz. 2019. “A Proposal for Redesigning the Water Quality Network of the Tunjuelo River in Bogotá, Colombia Through a Spatio‐Temporal Analysis.” Resources 8, no. 2: 64. https://doi.org/10.3390/resources8020064.
Pérez, C. J., M. A. Vega‐Rodríguez, K. Reder, and M. Flörke. 2017. “A Multi‐Objective Artificial bee Colony‐Based Optimization Approach to Design Water Quality Monitoring Networks in River Basins.” Journal of Cleaner Production 166: 579–589. https://doi.org/10.1016/J.JCLEPRO.2017.08.060.
Preite, L., F. Solari, and G. Vignali. 2023. “Technologies to Optimize the Water Consumption in Agriculture: A Systematic Review.” Sustainability 15, no. 7: 5975. https://doi.org/10.3390/SU15075975.
PRISMA. 2025. “Www.Prisma‐Statement.Org.” http://www.prisma‐statement.org.
Probst, P., and B. Bischl. 2019. “Tunability: Importance of Hyperparameters of Machine Learning Algorithms.” Journal of Machine Learning Research 20: 1–32. http://jmlr.org/papers/v20/18‐444.html.
Puri, D., K. Borel, C. Vance, and R. Karthikeyan. 2017. “Optimization of a Water Quality Monitoring Network Using a Spatially Referenced Water Quality Model and a Genetic Algorithm.” Water 9, no. 9: 704. https://doi.org/10.3390/w9090704.
Qolomany, B., M. Maabreh, A. Al‐Fuqaha, A. Gupta, and D. Benhaddou 2017. “Parameters Optimization of Deep Learning Models Using Particle Swarm Optimization.” 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 1285–1290. https://doi.org/10.1109/IWCMC.2017.7986470.
Rao, R. V., and V. J. Savsani. 2012. “Advanced Optimization Techniques.” In Springer Series in Advanced Manufacturing, 5–34. Springer Nature. https://doi.org/10.1007/978‐1‐4471‐2748‐2_2.
Razavi, S., B. A. Tolson, and D. H. Burn. 2012. “Review of Surrogate Modeling in Water Resources.” Water Resources Research 48, no. 7: 7401. https://doi.org/10.1029/2011WR011527.
Rbaibi, O., and A. SahibEddine. 2024. “Sustainability in Integrated Water Resources Management: Systematic Literature Review.” World Sustainability Series, Part F2854: 195–202. https://doi.org/10.1007/978‐3‐031‐56292‐1_15/FIGURES/1.
Reed, P. M., D. Hadka, J. D. Herman, J. R. Kasprzyk, and J. B. Kollat. 2013. “Evolutionary Multiobjective Optimization in Water Resources: The Past, Present, and Future.” Advances in Water Resources 51: 438–456. https://doi.org/10.1016/J.ADVWATRES.2012.01.005.
Regona, M., T. Yigitcanlar, B. Xia, and R. Y. M. Li. 2022. “Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review.” Journal of Open Innovation: Technology, Market, and Complexity 8, no. 1: 45. https://doi.org/10.3390/JOITMC8010045.
Reynolds, K. N., T. D. Loecke, A. J. Burgin, et al. 2016. “Optimizing Sampling Strategies for Riverine Nitrate Using High‐Frequency Data in Agricultural Watersheds.” Environmental Science and Technology 50, no. 12: 6406–6414. https://doi.org/10.1021/ACS.EST.5B05423.
Rode, M., G. Arhonditsis, D. Balin, et al. 2010. “New Challenges in Integrated Water Quality Modelling.” Hydrological Processes 24, no. 24: 3447–3461. https://doi.org/10.1002/HYP.7766.
Roy, D. K., and B. Datta. 2018. “A Review of Surrogate Models and Their Ensembles to Develop Saltwater Intrusion Management Strategies in Coastal Aquifers.” Earth Systems and Environment 2: 193–211. https://doi.org/10.1007/s41748‐018‐0069‐3.
Roy, D. K., and B. Datta. 2025. “Deep Learning‐Based Surrogates for Multi‐Objective Optimization of the Groundwater Abstraction Schemes to Manage Seawater Intrusion Into Coastal Aquifers.” Journal of Environmental Management 377: 124592. https://doi.org/10.1016/J.JENVMAN.2025.124592.
Safari, S., S. Sharghi, R. Kerachian, and H. Noory. 2023. “A Market‐Based Mechanism for Long‐Term Groundwater Management Using Remotely Sensed Data.” Journal of Environmental Management 332: 117409. https://doi.org/10.1016/J.JENVMAN.2023.117409.
Samantaray, S., A. Sahoo, and D. K. Ghose. 2020. “Assessment of Sediment Load Concentration Using SVM, SVM‐FFA and PSR‐SVM‐FFA in Arid Watershed, India: A Case Study.” KSCE Journal of Civil Engineering 24, no. 6: 1944–1957. https://doi.org/10.1007/s12205‐020‐1889‐x.
Samantaray, S., A. Sahoo, S. Paul, and D. K. Ghose. 2022. “Prediction of Bed‐Load Sediment Using Newly Developed Support‐Vector Machine Techniques.” Journal of Irrigation and Drainage Engineering 148, no. 10: 04022034. https://doi.org/10.1061/(ASCE)IR.1943‐4774.0001689.
Saravi, B., A. P. Nejadhashemi, P. Jha, and B. Tang. 2021. “Reducing Deep Learning Network Structure Through Variable Reduction Methods in Crop Modeling.” Artificial Intelligence in Agriculture 5: 196–207. https://doi.org/10.1016/J.AIIA.2021.09.001.
Sayers, W., D. Savic, and Z. Kapelan. 2019. “Performance of LEMMO With Artificial Neural Networks for Water Systems Optimisation.” Urban Water Journal 16, no. 1: 21–32. https://doi.org/10.1080/1573062X.2019.1611886.
Shaikh, M., and F. Birajdar. 2024. “Artificial Intelligence in Groundwater Management: Innovations, Challenges, and Future Prospects.” International Journal of Science and Research Archive 11, no. 1: 502–512. https://doi.org/10.30574/IJSRA.2024.11.1.0105.
Shao, H., W. Yang, J. Lindsay, Y. Liu, Z. Yu, and A. Oginskyy. 2017. “An Open Source GIS‐Based Decision Support System for Watershed Evaluation of Best Management Practices.” Journal of the American Water Resources Association 53, no. 3: 521–531. https://doi.org/10.1111/1752‐1688.12521.
Shaughnessy, A. R., X. Gu, T. Wen, and S. L. Brantley. 2021. “Machine Learning Deciphers CO2 Sequestration and Subsurface Flowpaths From Stream Chemistry.” Hydrology and Earth System Sciences 25, no. 6: 3397–3409. https://doi.org/10.5194/HESS‐25‐3397‐2021.
Shaw, A. R., H. Smith Sawyer, E. J. LeBoeuf, M. P. McDonald, and B. Hadjerioua. 2017. “Hydropower Optimization Using Artificial Neural Network Surrogate Models of a High‐Fidelity Hydrodynamics and Water Quality Model.” Water Resources Research 53, no. 11: 9444–9461. https://doi.org/10.1002/2017WR021039.
Shen, S., C. Z. Qin, L. J. Zhu, and A. X. Zhu. 2023. “From Scenario to Roadmap: Design and Evaluation of a Web‐Based Participatory Watershed Planning System for Optimizing Multistage Implementation Plans of Management Practices Under Stepwise Investment.” Journal of Environmental Management 342: 118280. https://doi.org/10.1016/J.JENVMAN.2023.118280.
Sholagberu, A. T., M. R. Mustafa, K. W. Yusof, and A. M. Hashim. 2017. “Geostatistical Based Susceptibility Mapping of Soil Erosion and Optimization of Its Causative Factors: A Conceptual Framework.” Journal of Engineering Science and Technology 12, no. 11: 2880–2895.
Silva, J. A. 2022. “Implementation and Integration of Sustainability in the Water Industry: A Systematic Literature Review.” Sustainability 14, no. 23: 15919. https://doi.org/10.3390/SU142315919.
Singh, K. P., N. Basant, and S. Gupta. 2011. “Support Vector Machines in Water Quality Management.” Analytica Chimica Acta 703, no. 2: 152–162. https://doi.org/10.1016/J.ACA.2011.07.027.
Singh, V. P., and D. K. Frevert. 2010. Watershed Models. 1st ed. CRC Press, Taylor & Francis Group.
Sit, M., B. Z. Demiray, Z. Xiang, G. J. Ewing, Y. Sermet, and I. Demir. 2020. “A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources.” Water Science and Technology 82, no. 12: 2635–2670. https://doi.org/10.2166/wst.2020.369.
Song, C., L. Yao, C. Hua, and Q. Ni. 2021. “Comprehensive Water Quality Evaluation Based on Kernel Extreme Learning Machine Optimized With the Sparrow Search Algorithm in Luoyang River Basin, China.” Environmental Earth Sciences 80: 521. https://doi.org/10.1007/s12665‐021‐09879‐x.
Sreekanth, J., and B. Datta. 2011. “Coupled Simulation‐Optimization Model for Coastal Aquifer Management Using Genetic Programming‐Based Ensemble Surrogate Models and Multiple‐Realization Optimization.” Water Resources Research 47, no. 4: W04516. https://doi.org/10.1029/2010WR009683.
Sreekanth, J., B. Datta, and B. Datta 2014. “Stochastic and Robust Multi‐Objective Optimal Management of Pumping From Coastal Aquifers Under Parameter Uncertainty.” https://doi.org/10.1007/s11269‐014‐0591‐5.
Steefel, C. I. 2019. “Reactive Transport at the Crossroads.” Reviews in Mineralogy and Geochemistry 85: 1–26. https://doi.org/10.2138/rmg.2019.85.1.
Stevenson, M., and C. Bravo. 2019. “Advanced Turbidity Prediction for Operational Water Supply Planning.” Decision Support Systems 119: 72–84. https://doi.org/10.1016/J.DSS.2019.02.009.
Su, Q., R. Srinivasan, and R. Karthikeyan. 2024. “SC.HAWQS: A User‐Friendly Web‐Based Decision Support System for Regional Water Resources Management Under a Changing Climate.” Water Resources Management 38, no. 4: 1–18. https://doi.org/10.1007/s11269‐023‐03719‐2.
Suresh, A., and K. Deb. 2023. “Machine Learning Based Prediction of New Pareto‐Optimal Solutions From Pseudo‐Weights.” IEEE Transactions on Evolutionary Computation 28, no. 5: 1351–1365. https://doi.org/10.1109/TEVC.2023.3319494.
Syed, I., S. Ahmed, B. B. Radhakrishnan, et al. 2021. “To Cite This Article: Yagyanath Rimal et al 2021.” Journal de Physique 1804, no. 1: 12054. https://doi.org/10.1088/1742‐6596/1804/1/012054.
Tayfur, G. 2017. “Modern Optimization Methods in Water Resources Planning, Engineering and Management.” Water Resources Management 31, no. 10: 3205–3233. https://doi.org/10.1007/S11269‐017‐1694‐6/TABLES/1.
Tekinerdogan, B., and O. Ozcan 2017. “Architectural Perspective for Design and Analysis of Scalable Software as a Service Architectures.” Managing Trade‐Offs in Adaptable Software Architectures, 223–245. https://doi.org/10.1016/B978‐0‐12‐802855‐1.00010‐1.
Tiyasha, T. M. Tung, and Z. M. Yaseen. 2020. “A Survey on River Water Quality Modelling Using Artificial Intelligence Models: 2000–2020.” Journal of Hydrology 585: 124670. https://doi.org/10.1016/j.jhydrol.2020.124670.
Toscano, G., H. Razavi, A. P. Nejadhashemi, K. Deb, and Linker, L. (2023). “Utilizing Innovization to Solve Large‐Scale Multi‐Objective Chesapeake Bay Watershed Problem.” 2023 IEEE Congress on Evolutionary Computation, CEC 2023. https://doi.org/10.1109/CEC53210.2023.10254161.
Toscano‐Pulido, G., H. Razavi, A. P. Nejadhashemi, K. Deb, and L. Linker. 2024. “Large‐Scale Multiobjective Optimization for Watershed Planning and Assessment.” IEEE Transactions on Systems, Man, and Cybernetics: Systems 54, no. 6: 3471–3483. https://doi.org/10.1109/TSMC.2024.3361679.
US EPA. 2023. “Overview of Watershed Monitoring|Watershed Academy Web|US EPA.” https://cfpub.epa.gov/watertrain/moduleFrame.cfm?parent_object_id=916.
Varadharajan, C., A. P. Appling, B. Arora, et al. 2022. “Can Machine Learning Accelerate Process Understanding and Decision‐Relevant Predictions of River Water Quality?” Hydrological Processes 36, no. 4: e14565. https://doi.org/10.1002/HYP.14565.
Volk, M., S. Lautenbach, H. van Delden, L. T. H. Newham, and R. Seppelt. 2010. “How Can We Make Progress With Decision Support Systems in Landscape and River Basin Management? Lessons Learned From a Comparative Analysis of Four Different Decision Support Systems.” Environmental Management 46, no. 6: 834–849. https://doi.org/10.1007/s00267‐009‐9417‐2.
Wang, S., Y. Wang, Y. Wang, and Z. Wang. 2022. “Comparison of Multi‐Objective Evolutionary Algorithms Applied to Watershed Management Problem.” Journal of Environmental Management 324: 116255. https://doi.org/10.1016/J.JENVMAN.2022.116255.
Wardropper, C., and A. Brookfield. 2022. “Decision‐Support Systems for Water Management.” Journal of Hydrology 610: 127928. https://doi.org/10.1016/J.JHYDROL.2022.127928.
Wellen, C., A. R. Kamran‐Disfani, and G. B. Arhonditsis. 2015. “Evaluation of the Current State of Distributed Watershed Nutrient Water Quality Modeling.” Environmental Science and Technology 49, no. 6: 3278–3290. https://doi.org/10.1021/ES5049557/SUPPL_FILE/ES5049557_SI_001.PDF.
Weng, S. Q., G. H. Huang, and Y. P. Li. 2010. “An Integrated Scenario‐Based Multi‐Criteria Decision Support System for Water Resources Management and Planning—A Case Study in the Haihe River Basin.” Expert Systems With Applications 37, no. 12: 8242–8254. https://doi.org/10.1016/J.ESWA.2010.05.061.
Clemson University, William Byars, B., B. William, and S Modeling. 2023. “Surrogate Modeling of Nonlinear Components and Circuits Surrogate Modeling of Nonlinear Components and Circuits Recommended Citation Recommended Citation.” https://tigerprints.clemson.edu/all_theses.
Williams, B., and S. Cremaschi (2021). “Novel Tool for Selecting Surrogate Modeling Techniques for Surface Approximation.” pp. 451–456. https://doi.org/10.1016/B978‐0‐323‐88506‐5.50071‐1.
Wu, B., Y. Zheng, X. Wu, et al. 2015. “Optimizing Water Resources Management in Large River Basins With Integrated Surface Water‐Groundwater Modeling: A Surrogate‐Based Approach.” Water Resources Research 51, no. 4: 2153–2173. https://doi.org/10.1002/2014WR016653.
Wu, J., X. Y. Chen, H. Zhang, L. D. Xiong, H. Lei, and S. H. Deng. 2019. “Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization.” Journal of Electronic Science and Technology 17, no. 1: 26–40 10.11989/JEST.1674‐862X.80904120.
Xiang, X., Q. Li, S. Khan, and O. I. Khalaf. 2021. “Urban Water Resource Management for Sustainable Environment Planning Using Artificial Intelligence Techniques.” Environmental Impact Assessment Review 86: 106515. https://doi.org/10.1016/J.EIAR.2020.106515.
Xu, T., and F. Liang. 2021. “Machine Learning for Hydrologic Sciences: An Introductory Overview.” Wiley Interdisciplinary Reviews: Water 8, no. 5: e1533. https://doi.org/10.1002/WAT2.1533.
Yang, L., and A. Shami. 2020. “On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice.” Neurocomputing 415: 295–316. https://doi.org/10.1016/J.NEUCOM.2020.07.061.
Yao, Q., M. Wang, Y. Chen, et al. 2018). “Taking Human Out of Learning Applications: A Survey on Automated Machine Learning.” http://arxiv.org/abs/1810.13306.
Ye, Z., J. Yang, N. Zhong, X. Tu, J. Jia, and J. Wang. 2020. “Tackling Environmental Challenges in Pollution Controls Using Artificial Intelligence: A Review.” Science of the Total Environment 699: 134279. https://doi.org/10.1016/J.SCITOTENV.2019.134279.
Yin, J., F. T. C. Tsai, and C. Lu. 2022. “Bi‐Objective Extraction–Injection Optimization Modeling for Saltwater Intrusion Control Considering Surrogate Model Uncertainty.” Water Resources Management 36: 6017–6042. https://doi.org/10.1007/s11269‐022‐03340‐9.
Yoon, H., S. C. Jun, Y. Hyun, G. O. Bae, and K. K. Lee. 2011. “A Comparative Study of Artificial Neural Networks and Support Vector Machines for Predicting Groundwater Levels in a Coastal Aquifer.” Journal of Hydrology 396, no. 1–2: 128–138. https://doi.org/10.1016/J.JHYDROL.2010.11.002.
Yunkai, L., T. Yingjie, O. Zhiyun, et al. 2010. “Analysis of Soil Erosion Characteristics in Small Watersheds With Particle Swarm Optimization, Support Vector Machine, and Artificial Neuronal Networks.” Environmental Earth Sciences 60, no. 7: 1559–1568. https://doi.org/10.1007/S12665‐009‐0292‐1/FIGURES/4.
Zamani, M. G., M. R. Nikoo, S. Jahanshahi, R. Barzegar, and A. Meydani. 2023. “Forecasting Water Quality Variable Using Deep Learning and Weighted Averaging Ensemble Models.” Environmental Science and Pollution Research 30, no. 59: 124316–124340. https://doi.org/10.1007/S11356‐023‐30774‐4/FIGURES/12.
Zanganeh, M. 2020. “Improvement of the ANFIS‐Based Wave Predictor Models by the Particle Swarm Optimization.” Journal of Ocean Engineering and Science 5, no. 1: 84–99. https://doi.org/10.1016/J.JOES.2019.09.002.
Zeng, Y., Y. Cai, P. Jia, and H. Jee. 2012. “Development of a Web‐Based Decision Support System for Supporting Integrated Water Resources Management in Daegu City, South Korea.” Expert Systems With Applications 39, no. 11: 10091–10102. https://doi.org/10.1016/J.ESWA.2012.02.065.
Zhai, Z., J. F. Martínez, V. Beltran, and N. L. Martínez. 2020. “Decision Support Systems for Agriculture 4.0: Survey and Challenges.” Computers and Electronics in Agriculture 170: 105256. https://doi.org/10.1016/J.COMPAG.2020.105256.
Zhu, F., P. A. Zhong, Q. Cao, J. Chen, Y. Sun, and J. Fu. 2019. “A Stochastic Multi‐Criteria Decision Making Framework for Robust Water Resources Management Under Uncertainty.” Journal of Hydrology 576: 287–298. https://doi.org/10.1016/J.JHYDROL.2019.06.049.
Grant Information:
96373801 US Environmental Protection Agency; 1019654 National Institute of Food and Agriculture
Contributed Indexing:
Keywords: artificial intelligence; decision support systems; monitoring; optimization; surrogate models; watershed models
Entry Date(s):
Date Created: 20260110 Date Completed: 20260110 Latest Revision: 20260110
Update Code:
20260130
DOI:
10.1002/wer.70246
PMID:
41518068
Database:
MEDLINE
*Further Information*
*This review explores the links, challenges, and gaps among six key elements of water management: watershed models, optimization algorithms, artificial intelligence, surrogate models, monitoring, and decision support systems. The main goals of this review are twofold: (1) to examine the established interrelationships among these key elements and analyze how these connections contribute to improved management effectiveness and (2) to identify and explore potential, yet unexplored, synergies among these elements that could lead to enhanced management practices. This study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, following steps for identification, screening, eligibility assessment, and selection while applying exclusion criteria and cross-referencing. The findings highlight that while advanced watershed models leveraging high-resolution datasets offer valuable insights, they face scalability challenges in capturing spatial and temporal variations. Additionally, the adaptability and performance of machine learning approaches are constrained by data limitations, including insufficiencies and inconsistencies across diverse sources. Overall, this synthesis provides actionable insights for advancing water quality protection and resource recovery by integrating emerging technologies with established management frameworks.
(© 2026 Water Environment Federation.)*