Groundwater is the largest liquid freshwater reservoir and a critical resource for drinking water, agriculture, and ecosystem sustainability. In the Rajshahi Division of north-western Bangladesh, intensive groundwater use and recurring droughts necessitate an accurate assessment of groundwater level (GWL). This study presents an integrated framework combining statistical, machine learning, and spatial tools for a comprehensive assessment of GWL fluctuations. A total of 1008 artificial neural network (ANN) models and 768 autoregressive integrated moving average (ARIMA) models were developed and evaluated under univariate (UV) and multivariate (MV) settings to determine optimal model functions and lagged effects. Spatial GWL and trend variations were generated using ArcGIS. Results show that ANN models incorporating rainfall (RF) data improved predictive accuracy (e.g., Bogura, MSE 0.01, Correlation Coefficient R = 0.98, NSE = 0.97, KGE = 0.96, R 2 = 0.97) compared to UV models. ARIMA models slightly outperformed ANN due to linear patterns, while ANN captured nonlinear dynamics. Trend analysis indicated significant GWL increases at Nawabganj. This framework provides a robust, transferable approach for evaluating GWL, enabling sustainable irrigation planning, drought risk management, identification of vulnerable zones, and evidence-based groundwater management in other hydrologically stressed regions.
Hoque et al. (2026) studied this question.