Hybrid deep learning-numerical modeling framework for long-term prediction of groundwater discharge and radionuclide transport.

Accurate long-term prediction of groundwater flow and radionuclide transport is critical for assessing the safety of deep geological disposal systems. This study proposes a hybrid modeling framework that integrates a numerical model with a deep learning approach to improve the predictive accuracy and computational efficiency. Outputs from the adaptive process-based total system performance assessment framework (APro-BIO) model, including water level, surface water flow, groundwater recharge, groundwater discharge (GWD), groundwater flow velocity, groundwater level, and radionuclide transport (RNT), together with van Genuchten parameters, were used as input features. Monthly groundwater discharge and radionuclide transport simulated by HydroGeoSphere (HGS) served as target variables, and a graph convolutional long short-term memory (GC-LSTM) model was trained to capture spatial and temporal dependencies. Model performance was evaluated against that of HGS, representing coupled saturated-unsaturated flow. The GC-LSTM achieved Kling-Gupta Efficiency values of 0.67-0.85 for GWD and 0.60-0.81 for RNT and reduced discrepancies relative to that of APro-BIO by up to 99 %. The model effectively reproduced temporal variability while reducing computational cost. Explainable AI analysis identified the van Genuchten β parameter as the most influential feature. These results demonstrate that the proposed framework provides an efficient and reliable alternative for long-term GWD and RNT prediction under computational constraints.