Study on the Evolution of Groundwater Level in Hebei Plain to the South of Beijing and Tianjin Based on LSTM Model

Wei Guo, Huifeng Yang (), Zeyan Li, Ruifang Meng, Xilin Bao and Hua Bai
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Wei Guo: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, China
Huifeng Yang: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, China
Zeyan Li: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, China
Ruifang Meng: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, China
Xilin Bao: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, China
Hua Bai: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geosciences, Shijiazhuang 050061, China

Sustainability, 2025, vol. 17, issue 10, 1-21

Abstract: This study addresses the limitations of machine learning in regional groundwater dynamics research, particularly the insufficient integration of the hydrogeological background and low simulation accuracy. Focusing on the shallow groundwater in the Hebei Plain south of Beijing and Tianjin, we integrate static data, including hydrogeological parameters, with the commonly used time-series data. A novel regionalization strategy based on depositional systems is proposed to enhance the model’s spatial adaptability. The Long Short-Term Memory (LSTM) model, augmented with an attention mechanism, adjusts the dynamic model weights using static data to reflect geological impacts on groundwater dynamics. Comparative results show that the refined regionalization and the inclusion of static data significantly improve the accuracy of the model. Based on the fitting results, the comparison of shallow groundwater level prediction between 2023 and 2040 under two mining conditions shows that the continuous implementation of the pressure mining policy has accelerated the recovery of water level, and the rise in groundwater level is obviously different between regions. The alluvial fan in the piedmont has the largest rise, and the marine sedimentary plain has the smallest rise. This study provides a new method for analyzing groundwater dynamics under complex hydrogeological conditions and provides a basis for regional groundwater management and sustainable utilization.

Keywords: groundwater level prediction; deep learning model; hydrogeological parameters; depositional systems (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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