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

Chang-Shian Chen () and You-Da Jhong
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Chang-Shian Chen: Department of Water Resources Engineering, Feng Chia University
You-Da Jhong: Graduate Institute of Civil and Hydraulic Engineering, Feng Chia University

Portuguese Journal of Management Studies, 2007, vol. XII, issue 2, 81-97

Abstract: Given the nonlinearity and uncertainty in the rainfall-runoff process, estimating or 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 learning was 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.

Keywords: Fuzzy Theory; Artificial Neural Networks; Discharge Forecasting; Self-Organizing Map. (search for similar items in EconPapers)
Date: 2007
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