Groundwater Level Forecasting Using Spatio-Temporal Graph Convolutional Networks (GCNs) and XGBoost
G. Naga Satish, Vijaya Chandra Jadala, Kolisetti Giridhara Sravani, Sangeetha Allam, M. Shanmuga Sundari, Jyothirmai Thada · 2025
This research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. The periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. These methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. The integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making.