Using an integrated fuzzy inference system and artificial neural network to forecast daily discharge - Chang-Shian, Chen (2015)
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.