DocumentCode :
478137
Title :
Rainfall-Runoff Modeling at Daily Scale with Artificial Neural Networks
Author :
Xu, Qin ; Ren, Liliang ; Yu, Zhongbo ; Yang, Bang ; Wang, Guizuo
Author_Institution :
State Key Lab. of Hydrol.-Water Resources & Hydraulic Eng., Hohai Univ., Nanjing
Volume :
2
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
504
Lastpage :
508
Abstract :
The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates a back-propagation (BP) neural networks model and a distributed hydrologic model for rainfall-runoff modeling in the upper area of Huai River, China. Methodologies and techniques of the two models are presented in this paper and a comparison of the simulated results between them is also conducted. The simulated results of the BP model indicate a satisfactory performance in the daily-scale simulation. The conclusions also indicate that the ANN-hydrologic models can be considered as an alternate and practical tool for hydrologic simulations in hydrologic science domain.
Keywords :
backpropagation; neural nets; artificial neural networks; back-propagation; distributed hydrologic model; rainfall-runoff modeling; Artificial neural networks; Computational modeling; Computer networks; Demand forecasting; Laboratories; Mathematical model; Power system modeling; Predictive models; Rivers; Water resources; ANN; BTOPMC; rainfall-runoff modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.559
Filename :
4667046
Link To Document :
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