A hybrid TFN-LSTM model for groundwater level forecasting in Rhode Island, USA

Accurate groundwater level forecasting is essential for effective groundwater resource management; however, it remains challenging due to the complex and nonlinear responses of aquifer systems to climatic forcing. Conceptual models, such as transfer function noise (TFN) models, effectively represent long-term trends and seasonal groundwater dynamics; however, they often fail to capture short-term variability, resulting in temporally correlated simulation residuals. Data-driven approaches can capture nonlinear dynamics but often lack clear physical meaning. This research proposes a hybrid model for forecasting groundwater levels by combining a TFN model with a Long Short-Term Memory (LSTM) network through a residual learning approach. Initially, the TFN model simulates baseline groundwater level responses based on precipitation and potential evapotranspiration. Then, the LSTM model is trained to identify temporal patterns in the TFN residuals and predict corrections. These corrections are added to the baseline simulation to produce the final forecasts. The model is tested using daily groundwater level data from three monitoring wells in Rhode Island, USA. The findings indicate that the hybrid model consistently performs better than both the standalone TFN and LSTM models, especially at locations with complex groundwater behavior. Additionally, the hybrid model exhibits greater temporal stability, with its performance declining more gradually over extended forecast periods. Shapley additive explanations reveal that past TFN residuals play a major role in the correction process, emphasizing the significance of residual persistence in improving prediction accuracy.