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http://dx.doi.org/10.3741/JKWRA.2021.54.S-1.1131

Assessment of artificial neural network model for real-time dam inflow prediction  

Heo, Jae-Yeong (Department of Civil & Environmental Engineering, Sejong University)
Bae, Deg-Hyo (Department of Civil & Environmental Engineering, Sejong University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.spc1, 2021 , pp. 1131-1141 More about this Journal
Abstract
In this study, the artificial neural network model is applied for real-time dam inflow prediction and then evaluated for the prediction lead times (1, 3, 6 hr) in dam basins in Korea. For the training and testing the model, hourly precipitation and inflow are used as input data according to average annual inflow. The results show that the model performance for up to 6 hour is acceptable because the NSE is 0.57 to 0.79 or higher. Totally, the predictive performance of the model in dry seasons is weaker than the performance in wet seasons, and this difference in performance increases in the larger basin. For the 6 hour prediction lead time, the model performance changes as the sequence length increases. These changes are significant for the dry season with increasing sequence length compared to the wet season. Also, with increasing the sequence length, the prediction performance of the model improved during the dry season. Comparison of observed and predicted hydrographs for flood events showed that although the shape of the prediction hydrograph is similar to the observed hydrograph, the peak flow tends to be underestimated and the peak time is delayed depending on the prediction lead time.
Keywords
Artificial neural network; Dam inflow; Leadtime; Rainfall-runoff;
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