2023 Volume 4 Issue 3 Pages 547-552
In dam management, it is important to provide appropriate water supply based on the water utilization situation such as agricultural water. In the low water management of the Kinugawa River, in order to secure the required flow rate at the water utilization reference point, the flow rate is adjusted by the integrated operation of the four dams and the dam group cooperation facility. Accurate prediction of inflow is necessary. In order to carry out efficient and effective integrated management, this study builds and verifies the accuracy of low water inflow prediction models based on deep learning and tank models that take into account the effects of non-melting snow and snow melting for four dams. carried out. As a result of the accuracy verification, the tank model was partially superior in the non-melting season, but the deep learning model generally showed higher reproducibility. In the snowmelt season, the snowmelt model showed high reproducibility between the non-melt model and the snowmelt model, confirming the effectiveness of the snowmelt model.
