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http://dx.doi.org/10.17663/JWR.2019.21.s-1.61

Application of Artificial Neural Network Ensemble Model Considering Long-term Climate Variability: Case Study of Dam Inflow Forecasting in Han-River Basin  

Kim, Taereem (Department of Civil and Environmental Engineering, Yonsei university)
Joo, Kyungwon (Department of Civil and Environmental Engineering, Yonsei university)
Cho, Wanhee (Integrated River Basin Mnagement Division, K-water)
Heo, Jun-Haeng (Department of Civil and Environmental Engineering, Yonsei university)
Publication Information
Journal of Wetlands Research / v.21, no.spc, 2019 , pp. 61-68 More about this Journal
Abstract
Recently, climate indices represented by quantifying atmospheric-ocean circulation patterns have been widely used to predict hydrologic variables for considering long-term climate variability. Hydrologic forecasting models based on artificial neural networks have been developed to provide accurate and stable forecasting performance. Forecasts of hydrologic variables considering climate variability can be effectively used for long-term management of water resources and environmental preservation. Therefore, identifying significant indicators for hydrologic variables and applying forecasting models still remains as a challenge. In this study, we selected representative climate indices that have significant relationships with dam inflow time series in the Han-River basin, South Korea for applying the dam inflow forecasting model. For this purpose, the ensemble empirical mode decomposition(EEMD) method was used to identify a significance between dam inflow and climate indices and an artificial neural network(ANN) ensemble model was applied to overcome the limitation of a single ANN model. As a result, the forecasting performances showed that the mean correlation coefficient of the five dams in the training period is 0.88, and the test period is 0.68. It can be expected to come out various applications using the relationship between hydrologic variables and climate variability in South Korea.
Keywords
Artificial Neural Network Ensemble; Climate Index; Climate Variability; Dam Inflow Forecasting; Ensemble Empirical Mode Decomposition;
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Times Cited By KSCI : 2  (Citation Analysis)
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