Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey
No Thumbnail Available
Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Sci Ltd
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
With the increase in population and industrialization, air pollution has become one of the global problems nowadays. Therefore, air pollutant parameters should be measured at regular intervals, and the necessary measures should be taken by evaluating the results of measurements. In order to prevent air pollution, pollutant parameters must be evaluated within the framework of a model. Recently, in order to obtain objective and more sensitive results with regard to air pollution nowadays, studies, which use machine learning algorithms in artificial intelligence technologies, have been carried out. In this study, PM10 concentrations, which are obtained from 7 stations in Ankara province in Turkey, were trained with machine learning algorithms (LASSO, SVR, RF, kNN, xGBoost, ANN). The PM10 concentrations of the years 2009-2017 of 6 stations in Ankara were given as input, and the PM10 concentrations of the seventh station for the year 2018 were predicted. The model development stage was repeated for each station, and the performance and error rates of the algorithms were determined by comparing the results produced by the algorithms with the actual results. The best results were provided with ANN (R-2 = 0.58, RMSE = 20.8, MAE = 14.4). The spatial distribution of the estimated concentration results was provided through Geographic Information System (GIS), and spatial strategies for improving air pollution over land use were established. (c) 2020 Elsevier Ltd. All rights reserved.
Description
Keywords
Air pollution parameter, Machine learning, Spatial distribution, Artificial intelligence, Predictive modeling
Journal or Series
Environmental Pollution
WoS Q Value
Q1
Scopus Q Value
Q1
Volume
263
