Groundwater Level Prediction in Urban Areas under Data Scarcity Using a Regionalized LSTM Framework

Scarcity of groundwater level (GWL) data poses a significant challenge to effective groundwater resource modeling, particularly in urban and peri-urban regions where anthropogenic influences further complicate hydrological processes. In this study, a machine learning–based framework is developed to predict groundwater levels for Bhubaneswar city, India, using Long Short-Term Memory (LSTM) neural networks. Given the data-driven nature of machine learning models and the limited availability of long-term observations, a regionalized modeling approach is adopted by coalescing GWL measurements from 31 closely located monitoring wells. To enable the model to capture well-specific variability, each well is characterized using static indicators derived from hydrological and socio-environmental datasets. Multiple combinations of predictor variables are evaluated to identify those most effective in representing groundwater level dynamics. The optimal model, trained on aggregated regional data, demonstrates strong predictive performance during testing, with a correlation coefficient (R) of 0.89 and a Nash–Sutcliffe Efficiency (NSE) of 0.79. The proposed regionalized LSTM framework shows promise for reliable groundwater level prediction at individual wells in data-scarce urban settings, offering a practical tool for groundwater assessment and management.