ScholarWorks@성균관대학교

초록

In regions like South Korea, erratic seasonal rainfall creates a dual vulnerability for agricultural reservoirs: rapid storage increases during the rainy season risk flooding and structural damage, while insufficient storage during dry periods leads to inadequate irrigation. Accurate reservoir storage prediction is therefore crucial. It enables pre-emptive storage and release planning, ensuring stable reservoir management and efficient water utilization despite unpredictable weather conditions. AI-based prediction offers a solution to the aforementioned challenges. However, previous studies had two key limitations: (1) they could not account for inflow and outflow variables in reservoirs that do not provide these data, and (2) they relied on Recurrent Neural Network (RNN) models with a recursive prediction mechanism, leading to decreased accuracy as the lead time increased. To overcome this, we propose a framework that simulates reservoir inflow and outflow using a rainfall-runoff hydrological model and utilizes these variables as inputs for time-series AI models. We then predict the storage rate using a Bayesian Model Averaging (BMA) ensemble of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Fusion Transformer (TFT) models, which resulted in a substantial accuracy improvement. The Mean Absolute Error (MAEs) for 1-day, 2-day, and 3-day ahead predictions were 0.820%p, 1.339%p, and 1.766%p, respectively, with corresponding correlation coefficients of 0.994, 0.987, and 0.980. This framework maintains high accuracy even as the lead time increases. The proposed framework can predict reservoir storage rates with high accuracy, even for reservoirs characterized by irregular seasonal rainfall patterns and a lack of explicit inflow/outflow data, thus contributing to more effective reservoir operation.

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