Machine learning-based monitoring and design of managed aquifer rechargers for sustainable groundwater management: scope and challenges.

Autor: Sheik AG; Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa., Kumar A; Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa., Sharanya AG; Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India., Amabati SR; Department of Chemical Engineering, Indian Institute of Petroleum and Energy, Visakhapatnam - 530 003, , Andhra Pradesh, India., Bux F; Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa., Kumari S; Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa. Sheenak1@dut.ac.za.
Jazyk: angličtina
Zdroj: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Nov 25. Date of Electronic Publication: 2024 Nov 25.
DOI: 10.1007/s11356-024-35529-3
Abstrakt: Managed aquifer recharge (MAR) replenishes groundwater by artificially entering water into subsurface aquifers. This technology improves water storage, reduces over-extraction, and ensures water security in water-scarce or variable environments. MAR systems are complex, encompassing various components such as water storage, soil, meteorological factors, groundwater management (GWM), and receiving bodies. Over the past decade, the utilization of machine learning (ML) methodologies for MAR modeling and prediction has increased significantly. This review evaluates all supervised, semi-supervised, unsupervised, and ensemble ML models employed to predict MAR factors and parameters, rendering it the most comprehensive contemporary review on this subject. This study presents a concise and integrated overview of MAR's most effective ML approaches, focusing on design, suitability for water quality (WQ) applications, and GWM. The paper examines performance measures, input specifications, and the variety of ML functions employed in GWM, and highlights prospects. It also offers suggestions for utilizing ML in MAR, addressing issues related to physical aspects, technical advancements, and case studies. Additionally, previous research on ML-based data-driven and soft sensing techniques for MAR is critically evaluated. The study concludes that integrating ML into MAR systems holds significant promise for optimizing WQ management and enhancing the efficiency of groundwater replenishment strategies.
Competing Interests: Declarations. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Completing interests: The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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