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- MakaleAçık Erişim2026Scopus SJRQ2Using 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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İncele - MakaleYalnız künye2025Scopus SJRQ1Median-Linear model for solar radiation estimation by comparison with ANFIS and Angström-Prescott methods
Acar, Ramazan; Saplıoğlu, Kemal; Güçlü, Yavuz Selim
This paper aims to propose a novel approach to estimating solar radiation in relation to sunshine duration. A hybrid Median-Linear (MedLin) model is presented including both the median value and the linear solar radiation equation. First stage is concerned with two-stage alternative of the proposed model with inputs and outputs exhibiting a seasonality structure and it is seasonally adjusted using median values. In the second stage, the linear equation for the difference values is determined with estimations the difference in solar radiation. Finally, the median value and the estimated difference value are added. Monthly total solar radiation values are estimated using monthly total sunshine duration data from Mardin, Adana, Bozova, Hakkari, Şırnak, and Van stations in Southeast of Türkiye for the application. The predictions of the MedLin approach are compared with the results of the Angström-Prescott, ANFIS, and Mean-Linear (MeanLin) models. As for the prediction of monthly total solar radiation data at Mardin and Hakkari stations, the proposed MedLin approach yields the best results compared to other methods with mean absolute percentage error 3.508% and 8.951%, respectively. Angström-Prescott model for Adana, MeanLin method for Bozova, ANFIS model for Şırnak, and MeanLin model for Van provided the best predictions with mean absolute percentage error 3.721%, 3.285%, 4.278%, and 13.101%, respectively. The results show that the MedLin technique proposed in this paper is useful for solar radiation estimation.
AtıfWoS1İncele - Derleme MakaleAçık Erişim2019Scopus SJRQ2ANN and ANFIS Approaches to Calculate the Heating and Cooling Degree Day Values: The Case of Provinces in Turkey
Işık, Erdem; İnallı, Mustafa; Celik, Erkan
Energy analysis plays a vital role in developing an optimum and cost-effective design for the heating, ventilating and air conditioning system of a building. The values of heating degree days (HDD) and cooling degree days (CDD) are used to analyze the demand for energy for buildings. In this study, the HDD and CDD values for Turkey were calculated using estimated temperature values. An artificial neural network (ANN) and an adaptive network-based fuzzy inference system (ANFIS) were applied for the estimation of temperature values. The Levenberg–Marquardt training algorithm was used for the feedforward backpropagation of the ANN, whereas the Sugeno-type fuzzy inference system was used for the ANFIS. Meteorological data from the last 11 years were used for the proposed approaches, and these were applied to different stations (n = 50) of the Turkish State Meteorological Service. In relation to the insulated building design, base temperatures have been considered as 16 °C and 18 °C for the HDD and 22 °C and 24 °C for the CDD based on a literature review. Two novel maps were prepared for the CDD and HDD. The calculated degree days and the maps are consistent with those from the Turkish State Meteorological Service.
AtıfScopus13WoS13OpenAlex18İncele - MakaleYalnız künye2019Scopus SJRQ1Elimination of a measurement problem: A robust prediction model for missing eigenvector value to assess earthquake induced out-of-plane failure of infill wall
Onat, Onur; Yön, Burak
One of the effective methods to assess local or global behavior of structural system is to perform shake table experiment. The biggest challenge of all experiments, either shake table or others, are missing data during the experiments due to removed instruments. This problem is arose especially during the out-of-plane tests. This problem is named as “Measurement” problem. This paper is focused on detection the measurement problem and solving this problem by using Adaptive-Network Based Neuro Inference System (ANFIS). Definition of the problem is briefly missing out-of-plane eigenvector value during the implementation of a shake table experiments on a full scale one bay one storey reinforced concrete structural system. For this aim, two shake table experiments were performed with reinforced concrete frame (RCF)and infill wall at the National Civil Engineering Laboratory (LNEC)Portugal. The out-of-plane failure of infill wall was assessed under combined bidirectional seismic load. Shake table experiments were conducted on two types of specimens. One of them is Unreinforced Brick Infill Wall (URBIF)composed of single layer 22 cm thick brick. The other is single layer 22 cm thick Infill Wall with Bed Joint Reinforcement solution (IwBJR). Both specimens were made of a single-layer brick infill wall enclosure with the RCF. Bed joint reinforcement term refers horizontal bed joint reinforcement as a strengthening technique. Shake table experiments were performed on each specimen at four stages. After third level of earthquake load, the accelerometers were removed on the wall to prevent damage to them. The removal of these instruments results in missing data. The missing eigenvector values were predicted with a robust Adaptive-Network Based Neuro Inference System (ANFIS)model to present failure mode of infill wall. The estimation quality was tested with R2, Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE)and Root Mean Square Error (RMSE)among with available data. The results of the quality indicator of R2 are 0.999 for URBIF model and 0.997 for IwBJR model, respectively. Moreover, rest of the indicator results are less than %1. The proposed approach is reliable on the base of quality indicator. Moreover, this study can be used for seismic engineering to predict eigenvector values to see local behavior of infill wall and mode shape evolution to determine which mod prone for partial and total collapse of infill wall.
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