Air Quality Index Prediction using Machine Learning Algorithms
1Dr. S.Shanthi, Assistant Professor (SLG) in the Department of Computer Applications, Kongu Engineering College, Tamil Nadu, India.
2M.Pyingkodi, Assistant Professor in the Department of Computer Applications, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7489-7492 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5326118419/2019©BEIESP | DOI: 10.35940/ijrte.D5326.118419
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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 environment is presently facing a key issue of air pollution. The four air pollutants which are becoming a concerning intimidation to human health are respirble particulate matter, nitrogen oxide, particle matter, and sulfur dioxide. A vast amount of air quality data is collected in different monitoring stations throughout the world. The collected data can be analyzed to forecast the air quality index (AQI) of future. This paper proposes machine learning algorithms such as random forest, support vector machine, self adaptive resource allocation to predict the future AQI. Tamil Nadu Pollution Control Board (TNPCN) deployed air pollution monitoring station in five regions. Air pollutant of PM10, PM2.5, SO2 and NO2 are monitord and AQI is calculated.. The data collected from January 2019 to November 2019 by TNPCN and also AQI of previous five years were used This system attempts to predict the level of pollutant PM,SO2,NO2 in the air to detect the AQI.
Keywords: Air Pollutant, Healthcare
Scope of the Article: Healthcare Informatics.