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Smart city article details

Title	Prediction Of Pollutant Concentrations By Meteorological Data Using Machine Learning Algorithms
ID_Doc	42834
Authors	Alpan K.; Sekeroglu B.
Year	2020
Published	International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 44, 4/W3
DOI	http://dx.doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-21-2020
Abstract	Air pollution, which is one of the biggest problems created by the developing world, reaches severe levels, especially in urban areas. Weather stations established at certain points in countries regularly obtain data and inform people about air quality. In Smart City applications, it is aimed to perform this process with higher speed and accuracy by collecting data with thousands of sensors based on the Internet of Things. At this stage, artificial intelligence and machine learning plays a vital role in analyzing the data to be obtained. In this study, six pollutant concentrations; particulate matters (PM2.5and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO3), Ozone (O3), and carbon monoxide (CO), were predicted using three basic machine learning algorithms, namely, random forest, decision tree and support vector regression, by considering only meteorological data. Experiments on two different datasets showed that the random forest has a high prediction capacity (R2:0.74-0.86), and high-accuracy predictions can be performed on pollutant concentrations using only meteorological data. This and further studies based on meteorological data would help to reduce the number of devices in Smart City applications and will make it more cost-effective. © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved.
Author Keywords	Air Pollution; Machine Learning; Meteorological Data; Pollutant Concentrations; Prediction; Smart City

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