PM2.5 Concentration Prediction By Data Mining Method
Hung Thuan Nguyen1, Chi Quynh Nguyen2
1Hung Thuan Nguyen, Department of Bachelor of Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
2Chi Quynh Nguyen*, Department of Computer Science, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam.
Manuscript received on November 29, 2021. | Revised Manuscript received on November 27, 2021. | Manuscript published on November 30, 2021. | PP: 64-69 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.B82971210220 | DOI: 10.35940/ijitee.B8297.1111121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The global air pollution is constantly increasing and causing negative effects on human health such as respiratory, cardiovascular diseases and cancers. Recently, pollution in Hanoi has become increasingly worse, especially when PM2.5 concentration is always at high level. Thus, PM2.5 prediction is of more urgency to issue early forecasts. Depending on air data including meteorological indicators and air pollution indicators collected in Hanoi, we have proposed a new characteristic extraction method that gave better results when uing the same algorithm compared to those of old methods. XG Boost algorithm was applied to predict the concentration of PM2.5 and the test showed that the accuracy of this algorithm is higher than that of other data mining algorithms while the training time is significantly lower.
Keywords: air quality forecasting, data mining, PM2.5 prediction, XG Boost.