Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Acar, Ramazan(1)
• Demir, Yakup(1)
• Eristi, Belkis(1)
• Eristi, Huseyin(1)
• Tümen, Vedat(1)
• Yildirim, Ozal(1)

• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1158
  Using Decision Tree and K-Means to Improve ANFIS for Predicting Missing Flow Data in Çoruh Basin

  Acar, Ramazan

  Complete and continuous flow data provides contributions to the water resources projects appropriate design. However, both financial and technical factors prevent the regular collection of data and such a situation leads to missing data problems. Input variables are from five stations in the Çoruh Basin in the northeast province of Türkiye, and output variables are from one station. In the study, 75% (171 months of data) of the 228 months flow data between the years 1993–2011 are used in the training phase and 25% (57 months of data) in the testing phase. In the ANFIS models, Decision Tree (DT) and K-means (KM) methods are used in selecting the input variables and determining the number of membership functions. In addition, artificial neural network (ANN), multiple linear regression (MLR), and ANFIS models with randomly determined cluster numbers are also used to compare the model performance of these methods. The results show that using DT and KM methods, the ANFIS models generate more reliable results than other models. During the study, the regression coefficient (R2), weighted mean square error (WMSE), and Wilcoxon (Z) values are taken into account for reliability measure. The DT–K-means–ANFIS model achieved the highest accuracy with R2 = 0.98 and WMSE = 5.89 during the testing phase, demonstrating superior performance to the ANN and MLR models. It is shown that the most successful models can be determined in a shorter time by using DT and KM methods before generating ANFIS models.

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  AtıfScopus1·WoS1·OpenAlex1

  İncele
• MakaleAçık Erişim2015Scopus SJRQ2/r/munzur/4187
  Automatic classification of harmonic data using k-means and least square support vector machine

  Eristi, Huseyin; Tümen, Vedat; Yildirim, Ozal; Eristi, Belkis; Demir, Yakup

  In this paper, an effective classification approach to classify harmonic data has been proposed. In the proposed classifier approach, harmonic data obtained through a 3-phase system have been classified by using k-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a k-means clustering algorithm has been applied to these data first. The training of the LS-SVM model has been realized with the class details obtained through the k-means algorithm. To increase the efficiency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classifiers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classifier system. The obtained results and comparisons suggest that the proposed classifier system approach is quite efficient at classifying harmonic data.

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  AtıfWoS1·OpenAlex3

  İncele