A Review of the Application of Machine Learning Models in Groundwater Resources Management and Quality Assessment

Machine learning (ML) has evolved into an indispensable tool for uncovering hidden patterns and deducing correlations. Currently, ML is having a profound impact on the field of groundwater resources and environment research by enhancing predictive accuracy and optimizing management strategies. In this study, we conducted a bibliometric review using CiteSpace and a global-scale analysis of ML methods applied to groundwater resources and quality based on 1326 records. The findings suggest that ML applications in groundwater resources and water environment research are still in their infancy compared with other environmental science fields. This paper then provides a systematic summary of the specific applications of machine learning methodologies within groundwater research, focusing primarily on the prediction of groundwater levels and water quality, along with the extraction of feature importance. Furthermore, a comparison was made of the pros and cons of several prevalent ML techniques used in groundwater level and water quality studies, with an emphasis on the significance of aligning data with models during the application of ML. Finally, the challenges encountered by ML tools in groundwater research were addressed, along with opportunities for the future. The significant potential of employing ML methodologies in groundwater is proposed to make the invisible visible.