Evaluating Kolmogorov–Arnold Networks (KAN) for Time Series Forecasting: Influence on Interpretability and Accuracy in Groundwater Level Prediction in Normandy, France

Accurate groundwater level (GWL) forecasting is crucial for effective water resource management, particularly under changing climatic conditions. In this study, we investigate the potential of the Kolmogorov–Arnold Network (KAN), an emerging neural architecture, for time series forecasting of GWL across the Normandy region in France. The performance of the KAN model was compared to classical recurrent neural network (RNN) architectures, including the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). 

Using ERA5 precipitation and temperature as predictors, all models were trained to simulate groundwater level variations at multiple monitoring stations. Results indicate that, although the standalone one-layer KAN model underperformed relative to LSTM and GRU in terms of predictive accuracy, it provided valuable interpretability by effectively capturing input importance and nonlinear dependencies between meteorological drivers and groundwater dynamics. Moreover, integrating a KAN layer within LSTM and GRU architectures improved performance at several stations, suggesting that hybrid KAN–RNN frameworks can combine the interpretability of KAN with the sequential learning capability of recurrent models. Based on our findings, we recommend a two-step approach: employing KAN alone for input relevance analysis, followed by applying hybrid KAN–LSTM architectures to enhance predictive accuracy. 

As the KAN-based model architectures continue to evolve with frequent updates and new variants, future research should further explore and benchmark these improved versions for hydrological and, particularly, GWL forecasting tasks. These results highlight the potential of KAN-based hybrid models for interpretable and adaptive groundwater forecasting, opening promising perspectives for data-driven understanding of subsurface processes in data-scarce regions.