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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/43623

Title: Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model
Authors: Chih-Chieh Young;Wen-Cheng Liu
Contributors: 國立臺灣海洋大學:海洋環境資訊學系
Date: 2015-10
Issue Date: 2017-10-19T03:37:07Z
Publisher: Hydrological Sciences Journal
Abstract: Abstract:The accurate prediction of hourly runoff discharge in a watershed during heavy rainfall events is of critical importance for flood control and management. This study predicts n-h-ahead runoff discharge in the Sandimen basin in southern Taiwan using a novel hybrid approach which combines a physically-based model (HEC-HMS) with an artificial neural network (ANN) model. Hourly runoff discharge data (1200 data sets) during seven heavy rainfall events are collected for the model calibration (training) and validation. Six statistical indicators (i.e. mean absolute error, root mean square error, coefficient of correlation, error of time to peak discharge, error of peak discharge, and coefficient of efficiency) are employed to evaluate the performance. In comparison with the HEC-HMS model, single ANN model, and time series forecasting (ARMAX) model, the developed HEC-HMS-ANN model demonstrates the improved accuracy in recursive n-h-ahead runoff discharge prediction especially for the peak flow discharge and time.
Relation: 128
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/43623
Appears in Collections:[海洋環境資訊系] 期刊論文

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