Modeling groundwater in a changing climate: A review of advanced machine learning solutions

TIMS Laboratory, FS, Abdelmalek Essaadi University, Tetouan, Morocco

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

The rapid changes in the climate are putting unprecedented stress on worldwide groundwater systems. To make accurate predictions and assessments, improved modeling is necessary. This review critically overviews the application of Machine Learning (ML) and Artificial Intelligence (AI) in evaluating climate change effects on sustainable groundwater management. Recent literature emphasizes the effectiveness of numerous ML algorithms, including sophisticated Deep Learning architectures, innovative data fusion techniques, and feature-selection strategies that improve prediction accuracy and interpretability. For instance, one study in the arid Tarim Basin significantly improved shallow groundwater level prediction by using a combined machine learning model to synergize geophysical EMI and remote sensing data, achieving an R² of 0.73. However, there are still challenges with robustness, explainability, and data availability in new climate scenarios. This work points toward a future outlook, emphasizing practical implementation in decision support systems and improved model explainability. Lastly, this review underscores innovation in advancing ML/AI tools for sustainable groundwater management.