Developing an Hourly Water Level Prediction Model for Small- and Medium-Sized Agricultural Reservoirs Using AutoML: Case Study of Baekhak Reservoir, South Korea

This study focuses on developing an hourly water level prediction model for small- and medium-sized agricultural reservoirs using the Tree-based Pipeline Optimization Tool (TPOT), an automated machine learning (AutoML) technique. The study area is the Baekhak Reservoir in South Korea, and various precipitation-related and reservoir water storage data were collected. Using these collected data, we compared widely used individual machine learning and deep learning models with the pipeline models generated by TPOT. The comparison showed that pipeline models, which included various preprocessing and ensemble techniques, exhibited higher predictive accuracy than individual machine learning and even deep learning models. The optimal pipeline model was evaluated for its performance in predicting water levels during an extreme rainfall event, demonstrating its effectiveness for hourly water level prediction. However, issues such as the overprediction of peak water levels and delays in predicting sudden water level changes were observed, likely due to inaccuracies in the ultra-short-term forecast precipitation data and the lack of information on reservoir operations (e.g., gate openings and drainage plans for agriculture). This study highlights the potential of AutoML techniques for use in hydrological modeling, and demonstrates their contribution to more efficient water management and flood prevention strategies in agricultural reservoirs.