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IJMLC 2020 Vol.10(6): 771-776 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.6.1004

On Modeling Smoke-Haze Incidence with Cluster and Regression Analyses

Kacha Chansilp, Paradee Chuaybamroong, Kittisak Kerdprasop, and Nittaya Kerdprasop

Abstract—Thailand and many countries in the Southeast Asia have long been suffered from the regional smoke-haze incidences. Smoke-haze is a kind of air pollution event frequently occurred from forest fires that had been intentionally set for vegetation purpose. The smoke-haze can cause serious health problem from high concentrations of small particulate matters that can retain in the lung or even spread through the whole body to cause obstruction in major organs. In the northern part of Thailand, smoke-haze normally occurs during the dry season from late January to early April with the peak polluted air around March. Controlling burning activity is an obvious solution but impractical when burning areas are in remotely high mountains that are hard to reach by ground officers. Monitoring incidences as well as estimating pollution level are more or less efficient and practical ways to handle the smoke-haze situation. We thus propose the application of machine learning technology to learn smoke-haze patterns from historical events. The specific approach used in our work is the cluster analysis with the k-means and Kohonen self-organizing map algorithms. The cluster with serious pollution effect is then further analysed to induce the predictive regression model using the meteorological factors. The built model can serve as a predictive pattern useful for invoking an early warning sign for air pollution awareness.

Index Terms—Air pollution predictor, cluster analysis, multivariate regression, smoke-haze incidence modeling.

K. Chansilp and K. Kerdprasop are with the School of Computer Engineering, Suranaree University of Technology (SUT), Nakhon Ratchasima 30000, Thailand (e-mail: kacha@sut.ac.th, kerdpras@sut.ac.th).
P. Chuaybamroong is with the Department of Environmental Science, Thammasat University, Rangsit Campus, Thailand (e-mail: paradee@tu.ac.th).
N. Kerdprasop is with the School of Computer Engineering and the Data and Knowledge Engineering Research Unit, SUT, Thailand (e-mail: nittaya@sut.ac.th).


Cite: Kacha Chansilp, Paradee Chuaybamroong, Kittisak Kerdprasop, and Nittaya Kerdprasop, "On Modeling Smoke-Haze Incidence with Cluster and Regression Analyses," International Journal of Machine Learning and Computing vol. 10, no. 6, pp. 771-776, 2020.

Copyright © 2020 by the authors. 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).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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