Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://acikerisim2.beykoz.edu.tr/handle/20.500.12879/3

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Feature Selection for the Prediction of Tropospheric Ozone Concentration Using a Wrapper Method
(Taylor & Francis Ltd, 2011) Sakar, C. Okan; Demir, Goksel; Kursun, Olcay; Ozdemir, Huseyin; Altay, Gokmen; Yalcin, Senay
High concentrations of ozone (O-3) in the lower troposphere increase global warming, and thus affect climatic conditions and human health. Especially in metropolitan cities like Istanbul, ozone level approximates to security levels that may threaten human health. Therefore, there are many research efforts on building accurate ozone prediction models to develop public warning strategies. The goal of this study is to construct a tropospheric (ground) ozone prediction model and analyze the effectiveness of air pollutant and meteorological variables in ozone prediction using artificial neural networks (ANNs). The air pollutant and meteorological variables used in ANN modeling are taken from monitoring stations located in Istanbul. The effectiveness of each input feature is determined by using backward elimination method which utilizes the constructed ANN model as an evaluation function. The obtained results point out that outdoor temperature (OT) and solar irradiation (Si) are the most important input features of meteorological variables, and total hydrocarbons (THC), nitrogen dioxide (NO2) and nitric oxide (NO) are those of air pollutant variables. The subset of parameters found by backward elimination feature selection method that provides the maximum prediction accuracy is obtained with six input features which are OT, SI, NO2, THC, NO, and sulfur dioxide (SO2) for both validation and test sets.
Identifying Effective Variables Using Mutual Information and Building Predictive Models of Sulfur Dioxide Concentration with Support Vector Machines
(Foundation Environmental Protection & Research-FEPR, 2010) Sakar, C. Okan; Kursun, Olcay; Ozdemir, Huseyin; Demir, Goksel; Yalcin, Senay
Sulfur dioxide (SO2) is an issue of increasing public concern due to its recognized adverse effects on human health. Therefore, accurate SO2 prediction models are very important tools in developing public warning strategies. The goal of this study is to identify the relevance of meteorological and air pollutant variables using a classical and widely used measure of dependence, Shannon's Mutual Information (MI), and to build an accurate SO, prediction model using the relevant variables as inputs. Specifically, features ranked by MI measure are tested on how much joint predictive power they have of the target using a popular machine learning tool, support vector machines (SVM), and in comparison to multilayer perceptron (MLP), which is the most commonly used machine learning tool in previous studies for the prediction and analysis of air pollutants. It was found that the SVM model gave a higher correlation coefficient (r) and less root mean squared error (RMSE) than MLP for both test and validation sets. The predictive model used 6 input variables for both data sets as the relevant features for maximum SO, concentration prediction at time t+1, which are the average SO,, maximum SO2, outdoor temperature (OT), average nitrogen dioxide (NO2), average ozone (O-3), and average wind speed at time t. The results of this study indicate that MI can be used efficiently in determining the importance of input variables in the prediction of SO2 concentration and SVM is a popular machine learning tool well suited for use in air pollution modeling.
The Prioritisation of Service Dimensions in Logistics Centres: A Fuzzy Quality Function Deployment Methodology
(Taylor & Francis Ltd, 2016) Vural, Ceren Altuntas; Tuna, Okan
This study takes a customer focus that prioritises the service-offering dimensions of logistics centres (LCs) by considering potential LC customer expectations. Applying a survey and a quality function deployment methodology to logistics service providers, the study explores, categorises and prioritises LC customer expectations and LC service characteristics. The results indicate that customer preferences mainly prioritise infrastructure, and warehouse and intermodal dimensions. However, when the cost dimension is included, higher utility values are delivered through soft service dimensions like value-added or standard services. LC investors or undertakers can use these results to guide their design of market offerings by using the same methodology to assess expectations in their target markets.

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