Browse by author
Lookup NU author(s): Professor Qiuhua Liang
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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.
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
URL: http://dx.doi.org/10.1016/S1001-6279(10)60033-9
DOI: 10.1016/S1001-6279(10)60033-9
Altmetrics provided by Altmetric
