Using an integrated fuzzy inference system and artificial neural network to forecast daily discharge

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

http://hdl.handle.net/10400.5/10014

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Authors

Chang-Shian, Chen

You-Da, Jhong

Chao-Hsien, Yeh

Abstract(s)

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

URI

http://hdl.handle.net/10400.5/10014

Citation

Chang-Shian, Chen, You-Da Jhong e Chao-Hsien Yeh (2007). "Using an integrated fuzzy inference system and artificial neural network to forecast daily discharge". Portuguese Journal of Management Studies, XII(2):81-98