Copula 함수 기반의 베이지안 네트워크와 가뭄예보를 결합한 안동댐의 계절별 유입량 예측

Abstract

The higher the accuracy of reservoir inflow forecasting, the more reliable the water supply from a dam is especially for drought periods. The article aims at probabilistic forecasting of seasonal inflow to reservoir and deciding estimates from the probabilistic seasonal inflow according to the drought forecast results. The probabilistic seasonal inflow was forecasted by a copula based Bayesian network employing Gaussian copula function. Drought forecasting was performed by calculation of the standardized streamflow index value. The calendar year is divided by four seasons and the total inflow volume of water to a reservoir for a season means the seasonal inflow. Seasonal inflow forecasting curves conforming to drought stages produce estimates of probabilistic seasonal inflow according to the drought forecast results. The forecasted estimates of seasonal inflow were calculated by using the inflow records of Andong dam in the Republic of Korea. Under the threshold probability of drought occurrence ranging 50 to 60 %, the forecasted seasonal inflows reasonably matched critical drought records. Combining the drought forecasting with the seasonal inflow forecasting may produce reasonable estimates of drought inflow from the probabilistic forecasting of seasonal inflow to a reservoir.