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http://dx.doi.org/10.3741/JKWRA.2017.50.7.489

Development and evaluation of dam inflow prediction method based on Bayesian method  

Kim, Seon-Ho (Department of Civil & Environmental Engineering, Sejong University)
So, Jae-Min (Department of Civil & Environmental Engineering, Sejong University)
Kang, Shin-Uk (National Drought Information Analysis Center, Korea Water Resources Cooperation)
Bae, Deg-Hyo (Department of Civil & Environmental Engineering, Sejong University)
Publication Information
Journal of Korea Water Resources Association / v.50, no.7, 2017 , pp. 489-502 More about this Journal
Abstract
The objective of this study is to propose and evaluate the BAYES-ESP, which is a dam inflow prediction method based on Ensemble Streamflow Prediction method (ESP) and Bayesian theory. ABCD rainfall-runoff model was used to predict monthly dam inflow. Monthly meteorological data collected from KMA, MOLIT and K-water and dam inflow data collected from K-water were used for the model calibration and verification. To estimate the performance of ABCD model, ESP and BAYES-ESP method, time series analysis and skill score (SS) during 1986~2015 were used. In time series analysis monthly ESP dam inflow prediction values were nearly similar for every years, particularly less accurate in wet and dry years. The proposed BAYES-ESP improved the performance of ESP, especially in wet year. The SS was used for quantitative analysis of monthly mean of observed dam inflows, predicted values from ESP and BAYES-ESP. The results indicated that the SS values of ESP were relatively high in January, February and March but negative values in the other months. It also showed that the BAYES-ESP improved ESP when the values from ESP and observation have a relatively apparent linear relationship. We concluded that the existing ESP method has a limitation to predict dam inflow in Korea due to the seasonality of precipitation pattern and the proposed BAYES-ESP is meaningful for improving dam inflow prediction accuracy of ESP.
Keywords
ESP; Dam inflow prediction; Bayesian Theory; ABCD Rainfall Runoff model;
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