Extraction and Processing Urban Data for Modeling Particulate Matter Concentrations in Tehran Using Probabilistic Neural Network, M.Sc. Thesis Sharif University of Technology ; Arhami, Mohammad (Supervisor) ; Amini, Zahra (Co-Supervisor)
Abstract
The hourly concentrations of particulate matter in Tehran are modelled in this study. High levels of particles are one of the main air pollution challenges in this metropolis, especially in the colder seasons. A probabilistic neural network is used for modelling. The model uses Bayes' theorem which has a very high ability to tackle the complexities and uncertainties. Traffic, meteorology, land use, baseline concentration (at 5 am), vegetation, along with other data including the location of each station, time of recording each concentration data, area and population of the municipal district of each station are considered. This research introduced a cheap and accurate method for collecting...
Cataloging briefExtraction and Processing Urban Data for Modeling Particulate Matter Concentrations in Tehran Using Probabilistic Neural Network, M.Sc. Thesis Sharif University of Technology ; Arhami, Mohammad (Supervisor) ; Amini, Zahra (Co-Supervisor)
Abstract
The hourly concentrations of particulate matter in Tehran are modelled in this study. High levels of particles are one of the main air pollution challenges in this metropolis, especially in the colder seasons. A probabilistic neural network is used for modelling. The model uses Bayes' theorem which has a very high ability to tackle the complexities and uncertainties. Traffic, meteorology, land use, baseline concentration (at 5 am), vegetation, along with other data including the location of each station, time of recording each concentration data, area and population of the municipal district of each station are considered. This research introduced a cheap and accurate method for collecting...
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