This document discusses using machine learning techniques to analyze factors that influence particulate matter (PM2.5) pollution levels in Thailand. It aims to consolidate PM2.5 data and identify dominant features for predicting air quality index (AQI) values. The document outlines data sources on PM2.5 levels and weather parameters from 2016-2019 in various Thai regions. It proposes using support vector regression instead of linear regression to determine the most important features for AQI prediction, and shows SVR results in predicting actual PM2.5 data more accurately than linear regression.

1. Empirical Dominance of Features
for Predictive Analytics of
Particulate Matter Pollution in
Thailand
FERDIN JOE JOHN JOSEPH
FACULTY OF INFORMATION TECHNOLOGY
THAI-NICHI INSTITUTE OF TECHNOLOGY

2. Objective
Consolidate data required for PM2.5 Forecast
Find dominant features to predict Air Quality Index (AQI)

3. PM 2.5 – Domain Knowledge
Particulate Matter 2.5
Carbon particles of size equal or less than 2.5 x 106
Measured in
Responsible for severe health hazards
Can mix into blood and not easy to excrete
Source: Purlife

4. Magnitude
Source: MDPI

5. Mortality Map
Source: Aungkulanon, S., Tangcharoensathien, V.,
Shibuya, K. et al. Post universal health coverage trend and
geographical inequalities of mortality in Thailand Int J
Equity Health (2016) 15: 190.
https://doi.org/10.1186/s12939-016-0479-5

6. Proposed Methodology

7. Data Sources
PM 2.5 – Berkeley Earth, Air4Thai
Other weather parameters – Weather Channel API (Collected data from Pathumwan
Demonstration School)
Timestamp of data: 2016– 19

8. Bangkok
Samutprakan
Narathiwat
Kanchanaburi
Rayong
Chiang Mai

9. Daily trend over years

10. Proposed Methodology
Support Vector Regression instead of linear regression
70% training and 30% testing
Justification of SVR using IRIS dataset

11. Feature Importance using Linear
Regression (Existing)

12. Feature Importance using SVR

13. Prediction Vs Actual Data in Linear
Regression

14. Prediction Vs Actual Data in SVR

15. http://172.16.104.3 from TNI Officer SSID

16. http://172.16.104.3 from TNI Officer SSID

17. http://172.16.104.3 from TNI Officer SSID

18. Sources
Code for 172.16.104.3
https://github.com/ferdinjoe

19. References
[1] J. B. Ordieres, E. P. Vergara, R. S. Capuz, and R. E. Salazar, “Neural network prediction model for fine particulate matter ( PM 2 .
5 ) on the US e Mexico border in ´ rez ( Chihuahua ) El Paso ( Texas ) and Ciudad Jua,” Environ. Model. Softw., vol. 20, pp. 547–559, 2005.
[2] P. Pe, A. Trier, and J. Reyes, “Prediction of PM concentrations several hours in advance using neural networks in Santiago ,
Chile,” Atmos. Environ., vol. 34, pp. 1189–1196, 2000.
[3] P. D. Hien, V. T. Bac, H. C. Tham, D. D. Nhan, and L. D. Vinh, “Influence of meteorological conditions on PM 2 . 5 and PM 2 . 5 À
10 concentrations during the monsoon season in Hanoi , Vietnam,” Atmos. Environ., vol. 36, pp. 3473–3484, 2002.
[4] T. Kanabkaew, “Prediction of Hourly Particulate Matter Concentrations in Chiangmai, Thailand Using MODIS Aerosol Optical
Depth and Ground-Based Meteorological Data.,” Environ. Asia, vol. 6, no. 2, pp. 65–70, 2013.
[5] M. Dong, D. Yang, Y. Kuang, D. He, S. Erdal, and D. Kenski, “Expert Systems with Applications PM 2 . 5 concentration prediction
using hidden semi-Markov model-based times series data mining,” Expert Syst. Appl., vol. 36, no. 2009, pp. 9046–9055, 2009.
[6] T. Pollution Control Board, “Thailand’s air quality and situation reports,” Air Quality and Noise Management Bureau. [Online].
Available: http://air4thai.pcd.go.th/webV2/index.php.
[7] R. Muller and E. Muller, “Berkeley Earth.” [Online]. Available: http://berkeleyearth.lbl.gov/.
[8] S. Boonvanich, “PM 2.5 and Data Science,” GitHub, 2019. [Online]. Available: https://github.com/gain9999/PM2.5.
[9] C. Hsu, C. Chang, and C. Lin, “A Practical Guide to Support Vector Classification,” 2010.

20. Thank You!
Queries if any?