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• W2023984489 abstract "Abstract A comparison of the Mamdani and the Takagi-Sugeno (TS) fuzzy inference systems is presented for predicting streamflow values. The TS fuzzy rule base uses linear functions of inputs to predict the output, whereas the Mamdani version of inference determines outputs through fuzzy sub-sets. A genetic algorithm-trained Mamdani system is applied for streamflow forecasting. All the uncertainties and model complications are treated in linguistic expressions in the form of IF—THEN statements. Fuzzy membership functions, rules and the type of defuzzification method are adjusted until the best correlation between measured and predicted values is reached. The two methods are then applied to flow predictions on the Euphrates River in Turkey, without employing exogenous variables such as rainfall. The advantages and disadvantages of the models are discussed using the case study. It is shown that the Mamdani type of fuzzy inference modelling outperforms the Takagi-Sugeno approach in terms of error criteria comparisons, but neither of the two outperforms a standard ARMA(2, 2) model." @default.
• W2023984489 created "2016-06-24" @default.
• W2023984489 creator A5064405823 @default.
• W2023984489 date "2009-04-01" @default.
• W2023984489 modified "2023-09-27" @default.
• W2023984489 title "Comparison of fuzzy inference systems for streamflow prediction" @default.
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• W2023984489 doi "https://doi.org/10.1623/hysj.54.2.261" @default.
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