Comparative analysis of fuzzy inference systems for water consumption time series prediction
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Date
2009
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Abstract
Two types of fuzzy inference systems (FIS) are used for predicting municipal water consumption time series. The FISs used include an adaptive neuro-fuzzy inference system (ANFIS) and a Mamdani fuzzy inference systems (MFIS). The prediction models are constructed based on the combination of the antecedent values of water consumptions. The performance of ANFIS and MFIS models in training and testing phases are compared with the observations and the best fit model is identified according to the selected performance criteria. The results demonstrated that the ANFIS model is superior to MFIS models and can be successfully applied for prediction of water consumption time series. © 2009 Elsevier B.V. All rights reserved.
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Keywords
Fuzzy inference , Mathematical models , Time series , Time series analysis , Water analysis , Water management , Water supply , Adaptive neuro-fuzzy inference system , ANFIS model , Best-fit models , Comparative analysis , Fuzzy inference systems , Mamdani , Mamdani fuzzy inference systems , Municipal water , Performance criterion , Prediction model , Time series prediction , Training and testing , Water consumption , Water consumption prediction , comparative study , fuzzy mathematics , hydrological modeling , prediction , time series , water management , water use , Fuzzy systems