caa a22 4500 606159290 CHVBK 20210128100624.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00521-015-1832-0 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1832-0 Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge [Elektronische Daten] [Chih-Chieh Young, Wen-Cheng Liu, Chung-En Chung] The accurate prediction of hourly runoff discharge in a river basin during typhoon events is of critical importance in operational flood control and management. This study utilizes three model approaches to predict runoff discharge in the Laonong Creek basin in southern Taiwan: the hydrological engineering center hydrological modeling system (HEC-HMS) model and two hybrid models which combine the HEC-HMS model with a genetic algorithm neural network (GANN) and an adaptive neuro-fuzzy inference system approach (ANFIS). Hourly runoff discharge data during seven heavy rainfall/typhoon events were collected for 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 (CE)] were used to evaluate the prediction accuracy. The simulation results indicate that the HEC-HMS model cannot satisfactorily predict hourly runoff discharge during the typhoon events. Both hybrid approaches that use the HEC-HMS model in conjunction with the GANN and ANFIS models can significantly improve the prediction accuracy for the n-h-ahead runoff discharge. The Natural Computing Applications Forum, 2015 Runoff forecasting nationallicence Rainfall-runoff nationallicence HEC-HMS nationallicence Artificial neural network nationallicence GANN nationallicence ANFIS nationallicence Young Chih-Chieh Hydrotech Research Institute, National Taiwan University, 10617, Taipei, Taiwan aut Liu Wen-Cheng Department of Civil and Disaster Prevention Engineering, National United University, 36063, Miao-Li, Taiwan aut Chung Chung-En Department of Civil and Disaster Prevention Engineering, National United University, 36063, Miao-Li, Taiwan aut Neural Computing and Applications Springer London 26/7(2015-10-01), 1631-1643 0941-0643 26:7<1631 2015 26 521 https://doi.org/10.1007/s00521-015-1832-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1832-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Young Chih-Chieh Hydrotech Research Institute, National Taiwan University, 10617, Taipei, Taiwan aut NATIONALLICENCE 700 1- Liu Wen-Cheng Department of Civil and Disaster Prevention Engineering, National United University, 36063, Miao-Li, Taiwan aut NATIONALLICENCE 700 1- Chung Chung-En Department of Civil and Disaster Prevention Engineering, National United University, 36063, Miao-Li, Taiwan aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/7(2015-10-01), 1631-1643 0941-0643 26:7<1631 2015 26 521