Classified real-time flood forecasting by coupling fuzzy clustering and neural network |
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| Authors: | Minglei REN Bende WANG Qiuhua LIANG Guangtao FU |
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| Institution: | 1. Dr., School of Civil & Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China;2. Prof., School of Civil & Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China;3. Lecture, School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;4. Lecture, School of Engineering, Computing and Mathematics, University of Exeter, Exeter EX4 4QF, UK |
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| Abstract: | 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. |
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| Keywords: | Real-time flood forecasting Flood classification Conceptual hydrological model Fuzzyclustering Neural networks |
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