Data Assimilation for a Simple Hydrological Partitioning Model Using Machine Learning

Predicting streamflow is a core element of efficient water resource management. Traditional hydrological models are constructed based on historical observational data, leading to cumulative prediction errors over time. To address this issue, this study proposes an Artificial Intelligence Filter (AIF) that integrates machine learning (ML) techniques into a data assimilation framework. The AIF learns the relationship between simulated streamflow and state variables (soil moisture, aquifer water level) and updates the state based on observed streamflow. This study applied the Simple Hydrologic Partitioning Model (SHPM) to four dam basins in southeastern Korea (Andong, Hapcheon, Miryang, Namgang). Model parameters were estimated using the Markov Chain Monte Carlo (MCMC) method, and results were compared with Open Loop (OL) simulations. After applying AIF, R2 and NSE increased by an average of approximately 0.02–0.04, representing a 2–5% improvement, achieving enhanced performance in most basins. KGE decreased slightly in some basins but improved by an average of about 2%. These results demonstrate that AIF not only enhances the accuracy of hydrological models but also contributes to securing the reliability of water resource forecasts through data assimilation and supports efficient management decision-making.