PM 2.5 Prediction & Air Quality Classification UsinMachine Learning
Issued Date
2024-06-01
Resource Type
ISSN
16860209
Scopus ID
2-s2.0-85200349867
Journal Title
Thai Journal of Mathematics
Volume
22
Issue
2
Start Page
441
End Page
452
Rights Holder(s)
SCOPUS
Bibliographic Citation
Thai Journal of Mathematics Vol.22 No.2 (2024) , 441-452
Suggested Citation
Soontornpipit P., Lekawat L., Tritham C., Tritham C., Pongpaibool P., Prasertsuk N., Jirakitpuwapat W. PM 2.5 Prediction & Air Quality Classification UsinMachine Learning. Thai Journal of Mathematics Vol.22 No.2 (2024) , 441-452. 452. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/100412
Title
PM 2.5 Prediction & Air Quality Classification UsinMachine Learning
Corresponding Author(s)
Other Contributor(s)
Abstract
Forecasting plays a vital role in air pollution alerts and the management of air quality. Studies and observations conducted in Thailand indicate a concerning rise in pollution levels, particularly in the concentration of PM2.5. concentrations. Bangkok, in particular, has been flagged for its alarmingly high PM2.5 By projecting the future PM2.5 concentrations in these urban areas, we can obtain valuable short-term predictive information regarding air quality. After conducting experiments using four different machine learning algorithms, it was found that the LSTM (Long Short-Term Memory) model provides the most accurate forecasts based on various statistical evaluation indicators. These indicators include a Root Mean Square Error (RMSE) of 2.74, Mean Absolute Error (MAE) of 1.97, R-squared value of 0.94, and Mean Absolute Percentage Error (MAPE) of 10.53. Then the classified air quality based on PM2.5 from the LSTM model gives the best performance indicators including accuracy = 0.9072, precision = 0.8466, negative predict value = 0.9403, sensitivity = 0.8144, specificity = 0.9381, and F1-score = 0.8169. The results show that the machine learning model can predict PM2.5 concentration, which is suitable for early warning of pollution and information provision for air quality management systems in Bangkok.