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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/44555


    Title: The strategy of building a flood forecast model by neuro-fuzzy network
    Authors: Chen, Shen-hsien;Lin, Yong-huang;張麗秋;Chang, Li-chiu;Chang, Fi-john
    Contributors: 淡江大學水資源及環境工程學系
    Keywords: flood forecast;neuro-fuzzy;artificial neural network;BPNN;ANFIS
    Date: 2006-04
    Issue Date: 2010-03-26 16:17:59 (UTC+8)
    Publisher: Bognor Regis: John Wiley & Sons Ltd.
    Abstract: A methodology is proposed for constructing a flood forecast model using the adaptive neuro-fuzzy inference system (ANFIS). This is based on a self-organizing rule-base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall-runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self-constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back-propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model.
    Relation: Hydrological processes 20(7), pp.1525-1540
    DOI: 10.1002/hyp.5942
    Appears in Collections:[水資源及環境工程學系暨研究所] 期刊論文

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