Comparison of fuzzy inference systems for streamflow prediction

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 al...

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Vydáno v:Hydrological sciences journal Ročník 54; číslo 2; s. 261 - 273
Hlavní autor: OZGER, Mehmet
Médium: Journal Article
Jazyk:angličtina
Vydáno: Wallingford Taylor & Francis Group 01.04.2009
IAHS Press
Témata:
algorithmes génétiques
case studies
débit en rivière
Earth sciences
Earth, ocean, space
Euphrate
Euphrates River
Exact sciences and technology
Freshwater
fuzzy logic
genetic algorithms
Hydrology
Hydrology. Hydrogeology
logique floue
Mamdani
prediction
prévision
rain
rivers
stream flow
streamflow
Tagaki-Sugeno
Takagi-Sugeno
Turkey (country)
uncertainty
Middle East
Turkey
Asia
Euphrates River
Asia, Euphrates R
Turkey (country)
algorithms
models
rainfall
correlation
river discharge
fuzzy logic
prediction
uncertainties
errors
streamflow
ISSN:0262-6667, 2150-3435
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Shrnutí: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.
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ISSN:0262-6667
2150-3435
DOI:10.1623/hysj.54.2.261