*Result*: Environmental resilience through artificial intelligence: innovations in monitoring and management.
Title:
Environmental resilience through artificial intelligence: innovations in monitoring and management.
Authors:
Wani AK; School of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, (144411), India. atifkhurshid61200216@gmail.com., Rahayu F; Research Center for Genetic Engineering, National Research and Innovation Agency, Bogor, 16911, Indonesia., Ben Amor I; Department of Process Engineering and Petrochemical, Faculty of Technology, University of El Oued, 39000, El Oued, Algeria., Quadir M; Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia., Murianingrum M; Research Center for Estate Crops, National Research and Innovation Agency, Bogor, (16911), Indonesia., Parnidi P; Research Center for Estate Crops, National Research and Innovation Agency, Bogor, (16911), Indonesia., Ayub A; School of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, (144411), India., Supriyadi S; Research Center for Behavioral and Circular Economics, National Research and Innovation Agency, Gatot, Subroto, Jakarta, (12710), Indonesia., Sakiroh S; Research Center for Estate Crops, National Research and Innovation Agency, Bogor, (16911), Indonesia., Saefudin S; Research Center for Estate Crops, National Research and Innovation Agency, Bogor, (16911), Indonesia., Kumar A; Department of Nuclear and Renewable Energy, Ural Federal University, Ekaterinburg, (620002), Russia., Latifah E; Research Center for Horticulture, National Research and Innovation Agency, Bogor, (16911), Indonesia.
Source:
Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Mar; Vol. 31 (12), pp. 18379-18395. Date of Electronic Publication: 2024 Feb 15.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE
Imprint Name(s):
Publication: <2013->: Berlin : Springer
Original Publication: Landsberg, Germany : Ecomed
Original Publication: Landsberg, Germany : Ecomed
MeSH Terms:
References:
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Contributed Indexing:
Keywords: Air quality; Artificial intelligence; Environment; Remote sensing; Sustainability; Water pollution
Entry Date(s):
Date Created: 20240215 Date Completed: 20240311 Latest Revision: 20240704
Update Code:
20260130
DOI:
10.1007/s11356-024-32404-z
PMID:
38358626
Database:
MEDLINE
*Further Information*
*The rapid rise of artificial intelligence (AI) technology has revolutionized numerous fields, with its applications spanning finance, engineering, healthcare, and more. In recent years, AI's potential in addressing environmental concerns has garnered significant attention. This review paper provides a comprehensive exploration of the impact that AI has on addressing and mitigating critical environmental concerns. In the backdrop of AI's remarkable advancement across diverse disciplines, this study is dedicated to uncovering its transformative potential in the realm of environmental monitoring. The paper initiates by tracing the evolutionary trajectory of AI technologies and delving into the underlying design principles that have catalysed its rapid progression. Subsequently, it delves deeply into the nuanced realm of AI applications in the analysis of remote sensing imagery. This includes an intricate breakdown of challenges and solutions in per-pixel analysis, object detection, shape interpretation, texture evaluation, and semantic understanding. The crux of the review revolves around AI's pivotal role in environmental control, examining its specific implementations in wastewater treatment and solid waste management. Moreover, the study accentuates the significance of AI-driven early-warning systems, empowering proactive responses to environmental threats. Through a meticulous analysis, the review underscores AI's unparalleled capacity to enhance accuracy, adaptability, and real-time decision-making, effectively positioning it as a cornerstone in shaping a sustainable and resilient future for environmental monitoring and preservation.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)*