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Original Research

EJMS. 2007; 12(2): 81-97


USING AN INTEGRATED FUZZY INFERENCE SYSTEM AND ARTIFICIAL NEURAL NETWORK TO FORECAST DAILY DISCHARGE

Chang-Shian Chen; You-Da Jhong
; Chao-Hsien Yeh.

Abstract
Given the nonlinearity and uncertainty inthe rainfall-runoff process, estimatingor predicting hydrologic data often encounters tremendous difficulty. This study applied fuzzy theory to create a daily flow forecasting model. To improve the time-consuming definition process of membership function, which is usually concluded by a trial-and-error approach, this study designated the membership function by artificial neural network{ANN} with either a supervised or unsupervised learning procedure. The supervised learningwas processed by the adaptive network based fuzzy inference system {ANFIS}, while the unsupervised learning was created by fuzzy and self-organizing map {SOMFIS}. The results indicate that the ANFIS method under increment flow data could provide more precise results for daily flow forecasting.

Key words: Fuzzy Theory, Artificial N.eural Networks, Discharge Forecasting, Self-Organizing Map



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