Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model

Authors:
Qin Ju;Zhongbo Yu;Zhenchun Hao;Gengxin Ou;Jian Zhao;Dedong Liu
Affiliations:
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China and Department of Geoscience, University of Nevada Las Vegas, Las Vegas, NV 891 ...;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Venue:
Neurocomputing
Year:
2009

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Abstract

The application of artificial neural network (ANN) to rainfall-runoff simulations has provided promising results in recent years. However, it is difficult to obtain satisfying results by using raw data for the direct prediction of the time series of streamflows. To improve simulating daily streamflow with back-propagation (BP) neural networks, the whole data set in this study is divided into two independent groups, flood period and non-flood period. The approaches and techniques of applying the division-based BP (DBP) in runoff simulation are presented in this paper. A comparison of the DBP model to the primitive BP model and the Xinanjiang model was also conducted to evaluate the effectiveness of the improvement. The numerical experimental results indicate that DBP model still overestimated flow peak, but improved considerably the streamflow simulation in the non-flood period.