Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph-Based Deep Learning

Abstract

Accurate forecasting for groundwater levels is essential for water resource management and sustainable development. Regional variations in groundwater levels exhibit a complex spatial dependency structure due to the physical proximity of monitoring wells, hydrological connectivity, and shared environmental characteristics. However, existing research has mostly overlooked the multiple spatial dependencies between monitoring wells, limiting the understanding of the added value that graph-based models bring to groundwater dynamics prediction. In this study, we characterize spatial dependencies of groundwater from multiple perspectives and investigate the impact on the forecasting results of groundwater dynamics using graph-based deep learning. Characterizing spatial dependencies helps improve the understanding of groundwater dynamics, but its effectiveness in enhancing prediction accuracy depends on the characteristics of spatial interactions. Graph neural networks facilitate learning from diverse spatial associations, allowing for a more comprehensive representation of spatial dependencies and uncovering potential connections between monitoring wells. Results based on real data sets contribute to understanding how multiple spatial dependencies influence groundwater level forecasting. Our research provides insights into exploiting potential spatial dependencies in similarly complex and highly interconnected earth systems.