IoT Based Unified Approach to Predict Particulate Matter Pollution in Thailand presented in the International Conference on Recent Trends in IoT and Blockchain ICRTIB 2019.

1. IOT Based Unified Approach To
Predict 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
Propose an unified architecture
Predict future PM2.5 concentration based on dominant features to predict Air Quality Index
(AQI)
Study the correlation coefficients

3. Scenario in Indian NCR

4. 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

5. Magnitude
Source: MDPI

6. 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

7. Proposed Methodology

8. 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

9. Bangkok
Samutprakan
Narathiwat
Kanchanaburi
Rayong
Chiang Mai

10. Daily trend over years

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

12. Feature Importance using Linear
Regression

13. Feature Importance using SVR

14. Proposed Architecture

15. Prediction Vs Actual Data in Existing
System

16. Prediction Vs Actual Data in Proposed
Architecture

17. Intranet system at TNI

18. Intranet system at TNI

19. Intranet system at TNI

20. Performance Evaluation
S. No Methodology Accuracy Correlation
1 Linear Regression 57% -0.8713
2 (John Joseph, 2019a) 68% -0.435
3 Proposed Methodology 75.18% +0.316

21. Sources
Code for 172.16.104.2
https://github.com/ferdinjoe

22. References
Alif, Y., Utama, K., Widianto, Y., Hari, Y., & Habiburrahman, M. (2019). Design of Weather Monitoring Sensors and Soil Humidity in
Agriculture Using Internet of Things ( IoT ). Transactions on Machine Intelligence and Artificial Intelligence, 7(1), 10–20.
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using hidden semi-Markov model-based times series data mining. Expert Systems with Applications, 36(2009), 9046–9055.
Hien, P. D., Bac, V. T., Tham, H. C., Nhan, D. D., & Vinh, L. D. (2002). Influence of meteorological conditions on PM 2 . 5 and PM 2 . 5 À 10
concentrations during the monsoon season in Hanoi , Vietnam. Atmospheric Environment, 36, 3473–3484.
John Joseph, F. J. (2019a). Empirical Dominance of Features for Predictive Analytics of Particulate Matter Pollution in Thailand. In 5th Thai-
Nichi Institute of Technology Academic Conference TNIAC 2019 (pp. 385–388).
John Joseph, F. J. (2019b). IoT Based Weather Monitoring System for Effective Analytics. International Journal of Engineering and
Advanced Technology, 8(4), 311–315.
John Joseph, F. J., T, R., & C, J. J. (2011). Classification of correlated subspaces using HoVer representation of Census Data. In 2011
International Conference on Emerging Trends in Electrical and Computer Technology (pp. 906–911). Ieee.
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23. References (contd)
Ordieres, J. B., Vergara, E. P., Capuz, R. S., & Salazar, R. E. (2005). Neural network prediction model for fine particulate matter ( PM 2 . 5 ) on the US e Mexico border in ´ rez (
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Pe, P., Trier, A., & Reyes, J. (2000). Prediction of PM concentrations several hours in advance using neural networks in Santiago , Chile. Atmospheric Environment, 34, 1189–
1196.
Pollution Control Board, T. (n.d.). Thailand’s air quality and situation reports. Retrieved from http://air4thai.pcd.go.th/webV2/index.php
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Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on (pp. 52–55).
Vinitketkumnuen, U., Kalayanamitra, K., Chewonarin, T., & Kamens, R. (2002). Particulate matter, PM 10 & PM 2.5 levels, and airborne mutagenicity in Chiang Mai, Thailand.
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24. Thank You!
Queries if any?