A New Method for Estimating Groundwater Changes Based on Optimized Deep Learning Models—A Case Study of Baiquan Spring Domain in China

Abstract

Estimating groundwater level (GWL) changes is crucial for the sustainable management of water resources in the face of urbanization and population growth. Existing prediction methods for GWL variations have limitations due to their inability to account for the diverse and irregular patterns of change. This paper introduces an innovative approach to GWL prediction that leverages multisource data and offers a comprehensive analysis of influencing factors. Our methodology goes beyond conventional approaches by incorporating historical GWL data, examining the impacts of precipitation and extraction, as well as considering policy-driven influences, especially in nations like China. The main contribution of this study is the development of a novel hierarchical framework (HGP) for GWL prediction, which progressively integrates correlations among different hierarchical information sources. In our experimental analysis, we make a significant discovery: extraction has a more substantial impact on GWL changes compared to precipitation. Building on this insight, our HGP model demonstrates superior predictive performance when evaluated on real-world datasets. The results show that HGP can increase NSE and R2 scores by 2.8% during the test period compared to the current more accurate deep learning method: ANFIS. This innovative model not only enhances GWL prediction accuracy but also provides valuable insight for effective water resource management. By incorporating multisource data and a novel hierarchical framework, our approach advances the state of the art in GWL prediction, contributing to more sustainable and informed decision making in the context of groundwater resource management.