Browsing by Author "Mermer, M"

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    MONTHLY WATER DEMAND FORECASTING BY ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH
    Firat, M; Yurdusev, MA; Mermer, M
    In this study, an adaptive Neuro-Fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors, which affect water use. Totally 108 data sets are collected and data sets are divided into two subsets, training and testing. The models consisting of the combination of the independent variables are constructed and the best fit input structure is investigated. The performance of ANFIS models in training and testing sets are compared with the observations and the best fit model forecasting model is identified. For this purpose, some criteria of performance evaluation such as, Root Mean Square Error (RMSE), efficiency (E) and correlation coefficient (CORR) are calculated for all models. Then, the best fit models are also trained and tested by Multiple Regression (MR). The results of models are compared to get more reliable comparison. The results indicated that ANFIS can be applied successfully for monthly water demand forecasting
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    Water use prediction by radial and feed-forward neural nets
    Yurdusev, MA; Firat, M; Mermer, M; Turan, ME
    In this study, applicability of feed-forward and radial-basis neural networks for monthly water consumption prediction from several socio-economic and climatic factors affecting water use is investigated. A data set including a total of 108 data records is divided into two subsets: training and testing. Firstly, the models based on a single input variable are trained and tested by feed-forward and radial methods and feed-forward and radial performances of the models are compared. Then, the models based on multiple input variables are constructed according to performances of the models based on a single input variable. The performances of feed-forward and radial models in training and testing phases are compared with the observations and the best-fit model is identified. For this purpose, several criteria such as normalised root mean square error, efficiency and correlation coefficient are calculated for all models. Subsequently, the best-fit models are also trained and tested by multiple linear regression for comparison. The results indicated that feed-forward and radial methods can be applied successfully for monthly water consumption prediction.