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ISPRS Int. J. Geo-Inf. 2019, 8(2), 99; https://doi.org/10.3390/ijgi8020099

A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran

1
Center of Excellence in Geomatic Engineering. in Disaster Management, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, P.O. Box 1439951154 Tehran, Iran
2
Department of GIS, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, P.O. Box 1439951154 Tehran, Iran
3
Dept. of Transportation Eng., Faculty of Civil & Transportation Engineering, University of Isfahan, P.O. Box 8174673441 Isfahan, Iran
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Environmental Scienecs Research Institute, Shahid Beheshti University, P.O. Box 1983969411 Tehran, Iran
5
APL-Professor of Economics and Econometrics, Karlsruhe Institute of Technology, Institute of Economics Econometrics and Statistics, 76049 Karlsruhe, Germany
6
Environmental Software & Services GmbH., A-2352 Vienna, Austria
*
Author to whom correspondence should be addressed.
Received: 23 January 2019 / Revised: 17 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
(This article belongs to the Special Issue Spatial Analysis of Pollution and Risk in a Changing Climate)
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Abstract

Environmental pollution has mainly been attributed to urbanization and industrial developments across the globe. Air pollution has been marked as one of the major problems of metropolitan areas around the world, especially in Tehran, the capital of Iran, where its administrators and residents have long been struggling with air pollution damage such as the health issues of its citizens. As far as the study area of this research is concerned, a considerable proportion of Tehran air pollution is attributed to PM10 and PM2.5 pollutants. Therefore, the present study was conducted to determine the prediction models to determine air pollutions based on PM10 and PM2.5 pollution concentrations in Tehran. To predict the air-pollution, the data related to day of week, month of year, topography, meteorology, and pollutant rate of two nearest neighbors as the input parameters and machine learning methods were used. These methods include a regression support vector machine, geographically weighted regression, artificial neural network and auto-regressive nonlinear neural network with an external input as the machine learning method for the air pollution prediction. A prediction model was then proposed to improve the afore-mentioned methods, by which the error percentage has been reduced and improved by 57%, 47%, 47% and 94%, respectively. The most reliable algorithm for the prediction of air pollution was autoregressive nonlinear neural network with external input using the proposed prediction model, where its one-day prediction error reached 1.79 µg/m3. Finally, using genetic algorithm, data for day of week, month of year, topography, wind direction, maximum temperature and pollutant rate of the two nearest neighbors were identified as the most effective parameters in the prediction of air pollution. View Full-Text
Keywords: air pollution; prediction; machine learning; regression SVM; geographically weighted regression; artificial neural network; auto-regressive nonlinear neural; interpolation; genetic algorithm air pollution; prediction; machine learning; regression SVM; geographically weighted regression; artificial neural network; auto-regressive nonlinear neural; interpolation; genetic algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Delavar, M.R.; Gholami, A.; Shiran, G.R.; Rashidi, Y.; Nakhaeizadeh, G.R.; Fedra, K.; Hatefi Afshar, S. A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran. ISPRS Int. J. Geo-Inf. 2019, 8, 99.

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