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

Abdul Gaffar Sheik,

Arvind Kumar,

A. G. Sharanya

и другие.

Environmental Science and Pollution Research, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 25, 2024

Abstract Managed aquifer recharge (MAR) replenishes groundwater by artificially entering water into subsurface aquifers. This technology improves storage, reduces over-extraction, and ensures security in water-scarce or variable environments. MAR systems are complex, encompassing various components such as soil, meteorological factors, management (GWM), receiving bodies. Over the past decade, utilization of machine learning (ML) methodologies for modeling prediction has increased significantly. review evaluates all supervised, semi-supervised, unsupervised, ensemble ML models employed to predict factors parameters, rendering it most comprehensive contemporary on this subject. study presents a concise integrated overview MAR’s effective approaches, focusing design, suitability quality (WQ) applications, GWM. The paper examines performance measures, input specifications, variety functions GWM, highlights prospects. It also offers suggestions utilizing MAR, addressing issues related physical aspects, technical advancements, case studies. Additionally, previous research ML-based data-driven soft sensing techniques is critically evaluated. concludes that integrating holds significant promise optimizing WQ enhancing efficiency replenishment strategies.

Язык: Английский

Enhancing spatial prediction of groundwater-prone areas through optimization of a boosting algorithm with bio-inspired metaheuristic algorithms DOI Creative Commons
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Sani I. Abba

и другие.

Applied Water Science, Год журнала: 2024, Номер 14(11)

Опубликована: Окт. 30, 2024

Язык: Английский

Процитировано

3
Machine learning-based monitoring and design of managed aquifer rechargers for sustainable groundwater management: scope and challenges DOI Creative Commons

Abdul Gaffar Sheik,

Arvind Kumar,

A. G. Sharanya

и другие.

Environmental Science and Pollution Research, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 25, 2024

Abstract Managed aquifer recharge (MAR) replenishes groundwater by artificially entering water into subsurface aquifers. This technology improves storage, reduces over-extraction, and ensures security in water-scarce or variable environments. MAR systems are complex, encompassing various components such as soil, meteorological factors, management (GWM), receiving bodies. Over the past decade, utilization of machine learning (ML) methodologies for modeling prediction has increased significantly. review evaluates all supervised, semi-supervised, unsupervised, ensemble ML models employed to predict factors parameters, rendering it most comprehensive contemporary on this subject. study presents a concise integrated overview MAR’s effective approaches, focusing design, suitability quality (WQ) applications, GWM. The paper examines performance measures, input specifications, variety functions GWM, highlights prospects. It also offers suggestions utilizing MAR, addressing issues related physical aspects, technical advancements, case studies. Additionally, previous research ML-based data-driven soft sensing techniques is critically evaluated. concludes that integrating holds significant promise optimizing WQ enhancing efficiency replenishment strategies.

Язык: Английский

Процитировано

2