Jesse Gilbert , Cyril Dziedzorm Boateng, Jeffrey N.A. Aryee , Marian A. Osei, David D. Wemegah, Solomon S.R. Gidigasu, Akyana Britwum, Samuel K. Afful, Haoulata Touré, Vera Mensah, Prinsca Owusu-Afriyie

Over two billion individuals worldwide rely on subterranean water as their primary reservoir of clean water. Ensuring the sustainable management of this heavily burdened resource necessitates a comprehensive quantitative evaluation of groundwater reserves. This becomes even more critical as water resources face escalating demands resulting from socioeconomic growth, population expansion, and the impacts of climate change. This research paper undertakes an extensive investigation in the context of a special issue dedicated to the utilization of machine learning (ML) algorithms for modeling and predicting groundwater levels (GWL). It offers a concise overview of prevalent Machine Learning(ML) techniques, encompassing their general architecture, key hyper-parameters, methods for fine-tuning, and strategies for optimal feature selection. Drawing insights from the scrutiny of 170 research papers across three prominent online
databases, our findings indicate that well-constructed machine-learning models exhibit a commendable capacity for accurately modeling and predicting groundwater levels. Based on our review we realized that the utilization of machine learning to model GWLs is quite common. Typically, past groundwater levels are used as input data, and artificial neural networks (ANN) are a popular choice for this purpose. Our review of existing research provides a useful guide for researchers interested in applying machine learning algorithms
for groundwater level modeling and forecasting. We also suggest new methods to improve modeling quality and highlight areas for future research in this field.