The particulate matter concentration spatial prediction using interpolation techniques with machine learning

© 2019 IEEE. The air pollution problem have become the major global environmental problem. It also impacts to health, economic, traffic, and tourism of the nation. The air quality monitoring stations have been applied to measure the air quality factors in their surrounding area. However, the number...

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Main Authors: Pattaraporn Chuanchai, Paskorn Champrasert, Kitimapond Rattanadoung
Format: Conference Proceeding
Published: 2020
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/67724
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-677242020-04-02T15:01:54Z The particulate matter concentration spatial prediction using interpolation techniques with machine learning Pattaraporn Chuanchai Paskorn Champrasert Kitimapond Rattanadoung Computer Science © 2019 IEEE. The air pollution problem have become the major global environmental problem. It also impacts to health, economic, traffic, and tourism of the nation. The air quality monitoring stations have been applied to measure the air quality factors in their surrounding area. However, the number of monitoring stations in developing countries may not be enough to cover the area. This paper proposes a framework to spatially predict the particulate matter concentration in the area without monitoring station. The proposed framework, called PAMS framework, consists of two components, which are 1) DUSTRY which is a particulate matter monitoring station to be deployed in a reference location, and 2) SPM which is a spatial prediction model to apply spatial interpolation technique and machine learning technique to provide the particulate matter concentration value in the area without monitoring station. This paper also explores the results from the variety of components in the PAMS. Two spatial interpolation techniques (i.e., IDW:Inverse Distance Weigh and Kriging) are compared. The evaluation results show that the the PAMS can spatially predict particulate matter concentration value with the average 10.16% error by using the Kriging technique with seven inputs for machine learning. 2020-04-02T15:01:54Z 2020-04-02T15:01:54Z 2019-07-01 Conference Proceeding 2-s2.0-85073229398 10.1109/ICoICT.2019.8835214 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85073229398&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/67724
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
spellingShingle Computer Science
Pattaraporn Chuanchai
Paskorn Champrasert
Kitimapond Rattanadoung
The particulate matter concentration spatial prediction using interpolation techniques with machine learning
description © 2019 IEEE. The air pollution problem have become the major global environmental problem. It also impacts to health, economic, traffic, and tourism of the nation. The air quality monitoring stations have been applied to measure the air quality factors in their surrounding area. However, the number of monitoring stations in developing countries may not be enough to cover the area. This paper proposes a framework to spatially predict the particulate matter concentration in the area without monitoring station. The proposed framework, called PAMS framework, consists of two components, which are 1) DUSTRY which is a particulate matter monitoring station to be deployed in a reference location, and 2) SPM which is a spatial prediction model to apply spatial interpolation technique and machine learning technique to provide the particulate matter concentration value in the area without monitoring station. This paper also explores the results from the variety of components in the PAMS. Two spatial interpolation techniques (i.e., IDW:Inverse Distance Weigh and Kriging) are compared. The evaluation results show that the the PAMS can spatially predict particulate matter concentration value with the average 10.16% error by using the Kriging technique with seven inputs for machine learning.
format Conference Proceeding
author Pattaraporn Chuanchai
Paskorn Champrasert
Kitimapond Rattanadoung
author_facet Pattaraporn Chuanchai
Paskorn Champrasert
Kitimapond Rattanadoung
author_sort Pattaraporn Chuanchai
title The particulate matter concentration spatial prediction using interpolation techniques with machine learning
title_short The particulate matter concentration spatial prediction using interpolation techniques with machine learning
title_full The particulate matter concentration spatial prediction using interpolation techniques with machine learning
title_fullStr The particulate matter concentration spatial prediction using interpolation techniques with machine learning
title_full_unstemmed The particulate matter concentration spatial prediction using interpolation techniques with machine learning
title_sort particulate matter concentration spatial prediction using interpolation techniques with machine learning
publishDate 2020
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85073229398&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/67724
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