Air Quality Index Prediction with Meteorological Data Using Feature Based Weighted Xgboost
NandigalaVenkatAnurag1, YagnavalkBurra2, S. Sharanya3
1NandigalaVenkatAnurag, Department of Computer Science and Engineering, SRMIST, Chennai, India.
2YagnavalkBurra, Department of Computer Science and Engineering, SRMIST, Chennai, India.
3S. Sharanya, Department of Computer Science and Engineering, SRMIST, Chennai, India.
Manuscript received on 20 April 2019 | Revised Manuscript received on 24 May 2019 | Manuscript published on 30 May 2019 | PP: 1355-1358 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3492058119/19©BEIESP
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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: Over the recent years, air pollution or air contamination has become a concerning threat, being responsible for over 7 million deaths annually according to a survey conducted by “WHO”(World Health Organisation). The four air pollutants which are becoming a concerning threat to human health are namely respirable particulate matter, nitrogen oxides, particulate matter and sulphur dioxide. So efficient air quality prediction will enables mankind to foresee these undesirable changes made in the environment by keeping the pollutant emission under check and control.Machine learning algorithms are boon in these types of applications which demand high degree of human intervention ad computation. This work deploys feature based weighted XGBoost, that uses meteorological data and pollutant level to predict the Air Quality Index (AIQ) This model is tested to predict the AIQ of Velachery, a fast developing commercial station in South India and has shown remarkable decline in the error rate that its rivals.
Keywords: Machine Learning, Air Quality Index, Artificial Intelligence
Scope of the Article: Machine Learning