Modeling Gaseous Air Pollutants Concentration in Tehran Using Artificial Neural Network and Land Use Regression, M.Sc. Thesis Sharif University of Technology ; Mohammad Arhami (Supervisor) ; Amini, Zahra (Co-Supervisor)
Abstract
In this thesis the hourly concentration of different gaseous air pollutants in Tehran is modeled using Land Use Regression (LUR) and Artificial Neural Network, separately. Both models are provided with the same set of input data; the first step is to find these data. Since traffic affects air pollution, information about traffic conditions is one of the main inputs in air pollution modeling. Therefore, to obtain traffic information, in this thesis, first a novel method is developed to extract and analyze Google Maps traffic data. In this method, image processing is used along with the Geographic Information System (GIS) to count the number of pixels of different traffic colors for each road...
Cataloging briefModeling Gaseous Air Pollutants Concentration in Tehran Using Artificial Neural Network and Land Use Regression, M.Sc. Thesis Sharif University of Technology ; Mohammad Arhami (Supervisor) ; Amini, Zahra (Co-Supervisor)
Abstract
In this thesis the hourly concentration of different gaseous air pollutants in Tehran is modeled using Land Use Regression (LUR) and Artificial Neural Network, separately. Both models are provided with the same set of input data; the first step is to find these data. Since traffic affects air pollution, information about traffic conditions is one of the main inputs in air pollution modeling. Therefore, to obtain traffic information, in this thesis, first a novel method is developed to extract and analyze Google Maps traffic data. In this method, image processing is used along with the Geographic Information System (GIS) to count the number of pixels of different traffic colors for each road...
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