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Classified real-time flood forecasting by coupling fuzzy clustering and neural network

Lookup NU author(s): Professor Qiuhua Liang


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This paper presented a new classified real-time flood forecasting framework by integrating a fuzzy clustering model and neural network with a conceptual hydrological model. A fuzzy clustering model was used to classify historical floods in terms of flood peak and runoff depth, and the conceptual hydrological model was calibrated for each class of floods. A back-propagation (BP) neural network was trained by using real-time rainfall data and outputs from the fuzzy clustering model. BP neural network provided a rapid on-line classification for real-time flood events. Based on the on-line classification, an appropriate parameter set of hydrological model was automatically chosen to produce real-time flood forecasting. Different parameter sets was continuously used in the flood forecasting process because of the changes of real-time rainfall data and on-line classification results. The proposed methodology was applied to a large catchment in Liaoning province, China. Results show that the classified framework provided a more accurate prediction than the traditional non-classified method. Furthermore, the effects of different index weights in fuzzy clustering were also discussed. © 2010 International Research and Training Centre on Erosion and Sedimentation and the World Association for Sedimentation and Erosion Research.

Publication metadata

Author(s): Ren M, Wang B, Liang Q, Fu G

Publication type: Article

Publication status: Published

Journal: International Journal of Sediment Research

Year: 2010

Volume: 25

Issue: 2

Pages: 134-148

Print publication date: 07/07/2010

ISSN (print): 1001-6279

ISSN (electronic):

Publisher: Guoji Nisha Yanjiu Zhongxin


DOI: 10.1016/S1001-6279(10)60033-9


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