GROUNDWATER LEVEL FITTING OF MONITORING WELLS IN THE BAODING REGION BASED ON LONG SHORT-TERM MEMORY (LSTM) NEURAL NETWORKS

This study applies a Long Short-Term Memory (LSTM) neural network to model daily groundwater level variations at four monitoring wells in the piedmont plain of Baoding City, Hebei Province, China. Using daily data from January 1, 2018 to August 31, 2019, the LSTM model is trained and tested under a one-step rolling prediction framework. The results demonstrate that the LSTM model accurately fits groundwater time series across various hydrogeological conditions, with testing-phase RMSE values ranging from 0.08 to 0.45 meters and R2 values exceeding 0.90. The model performs exceptionally well for both stable and fluctuating groundwater level conditions, capturing seasonal decline and recovery patterns without requiring explicit seasonal indicators. It also reveals the model’s ability to learn long-term trends and nonlinear dynamics inherent in groundwater systems. Despite its high short-term prediction accuracy, challenges remain regarding multi-step forecasting and responses to extreme events outside the training data. The study concludes that LSTM offers strong potential for groundwater simulation in data-limited environments and recommends further integration of hydro-meteorological variables for enhanced model robustness.