Groundwater level forecasting with machine learning models: A review

• Main Authors: Boo, Kenneth Beng Wee, El-Shafie, Ahmed, Othman, Faridah, Khan, Md. Munir Hayet, Birima, Ahmed H., Ahmed, Ali Najah
• Format: Article
• Published: Elsevier 2024
• Subjects: TA Engineering (General). Civil engineering (General); TD Environmental technology. Sanitary engineering
• Online Access: http://eprints.um.edu.my/45651/; https://doi.org/10.1016/j.watres.2024.121249

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.