Hybrid Hydrological Forecasting Through a Physical Model and a Weather-Informed Transformer Model: A Case Study in Greek Watershed

Abstract

This study explores a hybrid AI framework for streamflow forecasting that integrates physically based hydrological modeling, bias correction, and deep learning. HEC-HMS simulations generate synthetic discharge, which a machine learning-based bias correction model adjusts for irrigation-induced discrepancies—improving the Nash–Sutcliffe Efficiency (NSE) from 0.55 to 0.84, the Kling–Gupta Efficiency (KGE) from 0.67 to 0.89, and reducing the RMSE from 1.084 to 0.301 m3/s. The corrected discharge is used as input to a Temporal Fusion Transformer (TFT) trained on hourly meteorological data to predict streamflow at 24-, 48-, and 72-h horizons. In a semi-arid, irrigated basin in Northern Greece, the TFT achieves NSEs of 0.84, 0.78, and 0.71 and RMSEs of 0.301, 0.743, and 0.980 m3/s, respectively. Probabilistic forecasts deliver uncertainty bounds with coverage near nominal levels. In addition, the model’s built-in interpretability reveals temporal and meteorological influences—such as precipitation—that enhance predictive performance. This framework demonstrates the synergistic benefits of combining physically based modeling with state-of-the-art deep learning to support robust, multi-horizon forecasts in irrigation-influenced, data-scarce environments.