Application of machine learning in groundwater quality modeling - A comprehensive review. (15th April 2023)

• Record Type: Journal Article
• Authors: Haggerty, Ryan; Sun, Jianxin; Yu, Hongfeng; Li, Yusong
• Is Part Of: Water research. Volume 233(2023)
• Publication Date: 2023-04-15
• Subjects: Machine learning -- Deep learning -- Groundwater quality -- Ai; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2023.119745
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: 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. Abstract: 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.