WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 4Citation - Scopus: 4Selection of Alternative Landfill Location by Using a Geographical Information System. European Side of Istanbul. Case Study(Technical University of Wroclaw, 2016) Demir, Göksel; Ökten, Hatice Eser; Ökten, Hatice Eser; Alyüz, Ümmügülsüm; Bayat, Cuma; 03.07. Department of Environmental Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyOne of the most difficult tasks encountered when implementing waste management practices in Turkey involves the selection of the most suitable area for a landfill. The Geographic Information System (GIS) which possesses the ability to imitate and process economic and environmental constraints, presents itself as a useful and effective decision support tool. This study will utilize the GIS to determine feasible alternative landfill areas on the European side of Istanbul, which has a high density population, showing that accurate selection results can be achieved at lower cost.Article Citation - WoS: 25Citation - Scopus: 34Sample Size Needed for Calibrating Trip Distribution and Behavior of the Gravity Model(Elsevier Ltd., 2010) Çelik, Hüseyin Murat; Çelik, Hüseyin Murat; 02.03. Department of City and Regional Planning; 02. Faculty of Architecture; 01. Izmir Institute of TechnologyConventional calibration algorithms of trip distribution models assume that the analyst has a whole base year trip matrix. To attain a whole trip matrix, the sample size for travel surveys needed to be as large as possible. However, this could be very expensive especially in large cities. Some studies in the past showed a small sized sample would be enough to estimate functional parameters of observed trip length frequency distribution. But the performance of a gravity model with small sized samples has never been addressed. This empirical study has shown that sample sizes as small as 1000 (even smaller for quick response studies) could be as dependable as large sample surveys using a line search calibration algorithm. © 2009 Elsevier Ltd. All rights reserved.Article Citation - WoS: 28Citation - Scopus: 18Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey(John Wiley and Sons Inc., 2010) İnal, Fikret; İnal, Fikret; 03.02. Department of Chemical Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyTropospheric (ground-level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross-validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 μg/m3, 11.15μg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non-linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. Tropospheric ozone has adverse effects on human health and environment. Here, the next-day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron type artificial neural networks (MLP-ANNs). The MLP-ANNs were compared to multiple linear and multiple non-linear regression models. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.