*Result*: Simultaneous identification of groundwater contamination source and simulation model parameters based on the rime optimization algorithm.
Title:
Simultaneous identification of groundwater contamination source and simulation model parameters based on the rime optimization algorithm.
Authors:
Wang X; Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China.; Jilin Provincial Key Laboratory of Water Resources and Water Environment, Jilin University, Changchun, 130021, China.; College of New Energy and Environment, Jilin University, Changchun, 130021, China., Lu W; Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China. luwx999@163.com.; Jilin Provincial Key Laboratory of Water Resources and Water Environment, Jilin University, Changchun, 130021, China. luwx999@163.com.; College of New Energy and Environment, Jilin University, Changchun, 130021, China. luwx999@163.com., Wang Z; Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China.; Jilin Provincial Key Laboratory of Water Resources and Water Environment, Jilin University, Changchun, 130021, China.; College of New Energy and Environment, Jilin University, Changchun, 130021, China.
Source:
Environmental monitoring and assessment [Environ Monit Assess] 2025 Aug 02; Vol. 197 (9), pp. 978. Date of Electronic Publication: 2025 Aug 02.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Country of Publication: Netherlands NLM ID: 8508350 Publication Model: Electronic Cited Medium: Internet ISSN: 1573-2959 (Electronic) Linking ISSN: 01676369 NLM ISO Abbreviation: Environ Monit Assess Subsets: MEDLINE
Imprint Name(s):
Publication: 1998- : Dordrecht : Springer
Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981-
Original Publication: Dordrecht, Holland ; Boston : D. Reidel Pub. Co., c1981-
MeSH Terms:
References:
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Ayvaz, M. T. (2010). A linked simulation–optimization model for solving the unknown groundwater pollution source identification problems. Journal of Contaminant Hydrology, 117(1–4), 46–59. https://doi.org/10.1016/j.jconhyd.2010.06.004. (PMID: 10.1016/j.jconhyd.2010.06.004)
Ayvaz, M. T. (2013). A linked simulation–optimization model for simultaneously estimating the Manning’s surface roughness values and their parameter structures in shallow water flows. Journal of Hydrology, 500, 183–199. https://doi.org/10.1016/j.jhydrol.2013.07.019. (PMID: 10.1016/j.jhydrol.2013.07.019)
Ayvaz, M. T. (2016). A hybrid simulation–optimization approach for solving the areal groundwater pollution source identification problems. Journal of Hydrology, 538, 161–176. https://doi.org/10.1016/j.jhydrol.2016.04.008. (PMID: 10.1016/j.jhydrol.2016.04.008)
Ayvaz, M. T., & Karahan, H. (2008). A simulation/optimization model for the identification of unknown groundwater well locations and pumping rates. Journal of Hydrology, 357(1–2), 76–92. https://doi.org/10.1016/j.jhydrol.2008.05.003. (PMID: 10.1016/j.jhydrol.2008.05.003)
Ayvaz, M. T., Karahan, H., & Aral, M. M. (2007). Aquifer parameter and zone structure estimation using kernel-based fuzzy c-means clustering and genetic algorithm. Journal of Hydrology, 343(3–4), 240–253. https://doi.org/10.1016/j.jhydrol.2007.06.018. (PMID: 10.1016/j.jhydrol.2007.06.018)
Ayvaz, M. T. (2009). Identification of groundwater parameter structure using harmony search algorithm. In Z. W. Geem (Ed.), Music-inspired harmony search algorithm: Theory and applications (pp. 129-140). Springer. https://doi.org/10.1007/978-3-642-00185-7_8.
Azizi, M., Aickelin, U., A. Khorshidi, H., & Baghalzadeh Shishehgarkhaneh, M. 2023. Energy valley optimizer: A novel metaheuristic algorithm for global and engineering optimization. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-022-27344-y.
Bashi-Azghadi, S. N., & Kerachian, R. (2010). Locating monitoring wells in groundwater systems using embedded optimization and simulation models. Science of the Total Environment, 408(10), 2189–2198. https://doi.org/10.1016/j.scitotenv.2010.02.004. (PMID: 10.1016/j.scitotenv.2010.02.004)
Bashi-Azghadi, S. N., Kerachian, R., Bazargan-Lari, M. R., & Solouki, K. (2010). Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN. Expert Systems with Applications, 37(10), 7154–7161. https://doi.org/10.1016/j.eswa.2010.04.019. (PMID: 10.1016/j.eswa.2010.04.019)
Bashi-Azghadi, S. N., Kerachian, R., Bazargan-Lari, M. R., & Nikoo, M. R. (2016). Pollution source identification in groundwater systems: Application of regret theory and Bayesian networks. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 40(3), 241–249. https://doi.org/10.1007/s40996-016-0022-3. (PMID: 10.1007/s40996-016-0022-3)
Chang, Z., Lu, W., & Wang, Z. (2021). A differential evolutionary Markov chain algorithm with ensemble smoother initial point selection for the identification of groundwater contaminant sources. Journal of Hydrology, 603, 126918. https://doi.org/10.1016/j.jhydrol.2021.126918. (PMID: 10.1016/j.jhydrol.2021.126918)
Datta, B., Chakrabarty, D., & Dhar, A. (2009). Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters. Journal of Hydrology, 376(1–2), 48–57. https://doi.org/10.1016/j.jhydrol.2009.07.014. (PMID: 10.1016/j.jhydrol.2009.07.014)
Guneshwor, L., Eldho, T. I., & Vinod Kumar, A. (2018). Identification of groundwater contamination sources using meshfree RPCM simulation and particle swarm optimization. Water Resources Management, 32(4), 1517–1538. https://doi.org/10.1007/s11269-017-1885-1. (PMID: 10.1007/s11269-017-1885-1)
Han, K., Zuo, R., Ni, P., Xue, Z., Xu, D., Wang, J., & Zhang, D. (2020). Application of a genetic algorithm to groundwater pollution source identification. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.125343. (PMID: 10.1016/j.jhydrol.2020.125343)
Hou, Z., Lao, W., Wang, Y., & Lu, W. (2021). Hybrid homotopy-PSO global searching approach with multi-kernel extreme learning machine for efficient source identification of DNAPL-polluted aquifer. Computers & Geosciences, 155, 104837. https://doi.org/10.1016/j.cageo.2021.104837. (PMID: 10.1016/j.cageo.2021.104837)
Jha, M., & Datta, B. (2013). Three-dimensional groundwater contamination source identification using adaptive simulated annealing. Journal of Hydrologic Engineering, 18(3), 307–317. https://doi.org/10.1061/(asce)he.1943-5584.0000624. (PMID: 10.1061/(asce)he.1943-5584.0000624)
Jiang, X., Ma, R., Wang, Y. X., Gu, W. L., Lu, W. X., & Na, J. (2021). Two-stage surrogate model-assisted Bayesian framework for groundwater contaminant source identification. Journal of Hydrology, 594, 125955. https://doi.org/10.1016/j.jhydrol.2021.125955. (PMID: 10.1016/j.jhydrol.2021.125955)
Jing, L., Kong, J., Wang, J., Xu, T., Pan, M. J., & Chen, W. L. (2023). Joint identification of contaminant source based on the ensemble Kalman filter integrated with relation coefficient. Journal of Hydrology, 617, Article 129057. https://doi.org/10.1016/j.jhydrol.2022.129057. (PMID: 10.1016/j.jhydrol.2022.129057)
Li, J., Lu, W., Wang, H., Bai, Y., & Fan, Y. (2020). Groundwater contamination sources identification based on kernel extreme learning machine and its effect due to wavelet denoising technique. Environmental Science and Pollution Research, 27(27), 34107–34120. https://doi.org/10.1007/s11356-020-08996-7. (PMID: 10.1007/s11356-020-08996-7)
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Grant Information:
42272283 National Natural Science Foundation of China
Contributed Indexing:
Keywords: Groundwater contamination sources identification; One-dimensional convolutional neural network; Rime optimization algorithm; Simulation optimization methods; Surrogate model
Substance Nomenclature:
0 (Water Pollutants, Chemical)
Entry Date(s):
Date Created: 20250802 Date Completed: 20250802 Latest Revision: 20250915
Update Code:
20260130
DOI:
10.1007/s10661-025-14421-8
PMID:
40751833
Database:
MEDLINE
*Further Information*
*Groundwater contamination source identification (GCSI) can accurately obtain characteristic information of contamination sources such as their locations and release histories, critical for contaminated site remediation and liability attribution. Among existing inversion methods, simulation optimization approaches face challenges including insufficient fitting accuracy of surrogate models and optimization algorithms being prone to fall into local optima. Based on the simulation optimization method, this study innovatively proposes a framework for solving GCSI problems. The proposed framework embeds a one-dimensional convolutional neural network (1DCNN) as an integrated surrogate component within the optimization model. The rime optimization algorithm (RIME) is employed to solve this composite optimization model, achieving simultaneous identification of both contamination source characteristics and aquifer parameters. The feasibility of the method is verified through a hypothetical case study. In a comparative analysis with fully connected neural network (FCNN) and support vector regression (SVR), the 1DCNN achieves an R<sup>2</sup> value of 0.9998. Under ± 20% noise interference, the 1DCNN's R<sup>2</sup> value remains above 0.9993. Compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Energy Valley Optimizer (EVO), RIME demonstrates superior performance with an average relative error of 8.88% for single identification and maintains the average error of 5.88% across 100 repeated experimental trials. This stems from RIME's unique soft rime and hard rime search strategies, which enable effective escape from local minima and convergence to global optima. This method provides a new approach for solving GCSI problems and has potential for extension to problems such as multiphase contaminant transport and heterogeneous aquifer parameter identification.
(© 2025. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)*
*Declarations. Competing interest: The authors declare no competing interests.*