Browsing by Author "Firat, M"

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    Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey
    Yurdusev, MA; Firat, 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 including average monthly water bill, population, number of households. gross national product, monthly average temperature observed, monthly total rainfall, monthly average humidity observed and inflation rate. Water consumption modeling in this way will be more consistent than doing it using a single variable as more effective parameter could be incorporated. The ANFIS system is applied to modeling monthly water consumptions of Izmir, Turkey. The results indicated that ANFIS can be successfully applied for monthly water consumption modeling. (c) 2008 Elsevier B.V. All rights reserved.
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    Comparative analysis of fuzzy inference systems for water consumption time series prediction
    Firat, M; Turan, ME; Yurdusev, MA
    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. (C) 2009 Elsevier B.V. All rights reserved.
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    Evaluation of Artificial Neural Network Techniques for Municipal Water Consumption Modeling
    Firat, M; Yurdusev, MA; Turan, ME
    Various Artificial Neural Network techniques such as Generalized Regression Neural Networks (GRNN), Feed Forward Neural Networks (FFNN) and Radial Basis Neural Networks (RBNN) have been evaluated based on their performance in forecasting monthly water consumptions from several socio-economic and climatic factors, which affect water use. The data set including total 108 data records is 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 ANN models in training and testing stages are compared with the observed water consumption values to identify the best fit forecasting model. For this purpose, some performance criteria such as Normalized Root Mean Square Error (NRMSE), efficiency (E) and correlation coefficient (CORR) are calculated for all models. The best fit models are also trained and tested by Multiple Linear Regression (MLR). The results indicated that GRNN outperforms all other methods in modeling monthly water consumptions.
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    Monthly river flow forecasting by an adaptive neuro-fuzzy inference system
    Firat, M; Turan, ME
    In this study, the applicability of an adaptive neuro-fuzzy inference system (ANFIS) to forecast for monthly river flows is investigated. For this, the Goksu river in the Seyhan catchment located in southern Turkey was chosen as a case study. The river flow forecasting models having various input structures are trained and tested by the ANFIS method. The results of ANFIS models for both training and testing are evaluated and the best-fit forecasting model is determined. The best-fit model is also trained and tested by feed forward neural networks (FFNN) and traditional autoregressive (AR) methods; and the performances of the models are compared. Moreover, ANFIS and FFNN models are verified by a validation data set including river flow data records during the time period 1997-2000. The results demonstrate that ANFIS can be applied successfully and provides high accuracy and reliability for monthly river flow forecasting.
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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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    Comparative analysis of neural network techniques for predicting water consumption time series
    Firat, M; Turan, ME; Yurdusev, MA
    Monthly water consumption time series have been predicted using a series of Artificial Neural Network (ANN) techniques including Generalized Regression Neural Networks (GRNN), Cascade Correlation Neural Network (CCNN) and Feed Forward Neural Networks (FFNN). One hundred and eight data sets for the city of Izmir, Turkey are used for a number of ANN modeling exercises. Several ANN models depending on the combination of antecedent values of water consumption records are constructed and the best fit input structure is investigated. The performance of ANN models in training and testing stages are compared with the observed water consumption values to identify the best fit forecasting model based upon a number of selected performance criteria. (C) 2010 Elsevier B.V. All rights reserved.
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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.
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    Silene nemrutensis (Caryophyllaceae), a new species from south-eastern Anatolia
    Yildiz, K; Çirpici, A; Dadandi, MY; Firat, M
    A new perennial species, Silene nemrutensis (Silene sect. Spergulifoliae, Caryophyllaceae), is described and illustrated from SE-Anatolia. A macromorphological comparison with the similar species S. arguta is given as well as the ultrastructure of the seeds and pollen grains. Original photographs, geographical distribution, habitat and IUCN conservation status are also provided.
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    Silene miksensis (Caryophyllaceae), a new species from eastern Anatolia
    Firat, M; Yildiz, K
    A new perennial species, Silene miksensis (Silene sect. Pinifolia, Caryophyllaceae), is described and illustrated from eastern Anatolia, Turkey. A morphological comparison with the morphologically similar species is given as well as the ultrastructure of the seed and pollen grains. S. miksensis is assested as a Critically Endangered species according to IUCN [criterion B2ab (iii)].
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    Silene konuralpii (Sect. Spergulifoliae, Caryophyllaceae), a new species from eastern Anatolia
    Firat, M; Yildiz, K
    A new perennial species, Silene konuralpii sp. nov. (Silene sect. Spergulifoliae) is described and illustrated from eastern Anatolia. A momorphological comparison with the similar species S. stenobotrys, S. spergulifolia, and S. surculosa is given as well as the ultrastructure of the leaf, seed and pollen grain. Original drawings and photographs, distribution map, notes on ecology, and IUCN conservation status are also provided.