Application of machine learning in groundwater quality modeling - A comprehensive review

•Reviewed more than 200 papers that used machine learning in groundwater quality modeling.•Neural networks are the most used machine learning model in groundwater quality modeling.•Nitrate is the most modeled contaminants.•Suggestions for further works are proposed. Groundwater is a crucial resource...

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Vydané v:Water research (Oxford) Ročník 233; s. 119745
Hlavní autori: Haggerty, Ryan, Sun, Jianxin, Yu, Hongfeng, Li, Yusong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 15.04.2023
Predmet:
Artificial Intelligence
Deep learning
Environmental Monitoring - methods
Groundwater
groundwater contamination
Groundwater quality
Iran
Machine Learning
Neural Networks, Computer
nitrates
prediction
water quality
Iran
Deep learning
Ai
Groundwater quality
Machine learning
ISSN:0043-1354, 1879-2448, 1879-2448
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Shrnutí:•Reviewed more than 200 papers that used machine learning in groundwater quality modeling.•Neural networks are the most used machine learning model in groundwater quality modeling.•Nitrate is the most modeled contaminants.•Suggestions for further works are proposed. Groundwater is a crucial resource across agricultural, civil, and industrial sectors. The prediction of groundwater pollution due to various chemical components is vital for planning, policymaking, and management of groundwater resources. In the last two decades, the application of machine learning (ML) techniques for groundwater quality (GWQ) modeling has grown exponentially. This review assesses all supervised, semi-supervised, unsupervised, and ensemble ML models implemented to predict any groundwater quality parameter, making this the most extensive modern review on this topic. Neural networks are the most used ML model in GWQ modeling. Their usage has declined in recent years, giving rise to more accurate or advanced techniques such as deep learning or unsupervised algorithms. Iran and the United States lead the world in areas modeled, with a wealth of historical data available. Nitrate has been modeled most exhaustively, targeted by nearly half of all studies. Advancements in future work will be made with further implementation of deep learning and explainable artificial intelligence or other cutting-edge techniques, application of these techniques for sparsely studied variables, the modeling of new or unique study areas, and the implementation of ML techniques for groundwater quality management.
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ISSN:0043-1354
1879-2448
1879-2448
DOI:10.1016/j.watres.2023.119745