Impact of Climate Change on Groundwater Level Changes: An Evaluation Based on Deep Neural Networks

Abstract

Climate change has a substantial influence on groundwater levels (GWLs), which are critical for agriculture, safe drinking water, and ecosystem health, which are essential to successful water resource management and adaptation strategies. Recently, there has been an increase in the use of machine learning (ML) and deep learning (DL) models in hydrogeology to estimate GWL in monitoring wells. This study presents a novel technique for predicting GWL changes that uses three independent datasets: historical GWL and climatic variables (CVs) data such as rainfall and temperature influencing groundwater dynamics. In our experimental research, the models’ prediction output on real‐world datasets ensures that the model’s significant patterns are recorded while taking into account the noise in the data, resulting in a perfect balance of bias and variance. The DL models’ results show a significant score of root mean square error (RMSE) between 2.20 and 12.40 and coefficient of determination ( R ‐squared between 0.84–0.99), showing a significant improvement in RMSE and mean absolute error (MAE) in the testing and validation categories, when compared to the current state‐of‐the‐art methods. This study improves our understanding of GWL modeling and provides decision‐makers with a reliable tool for controlling change. The study advances environmental modeling by exhibiting methodological complexity and emphasizes the importance of comprehensive data analysis in water resource management.