Air Quality Index Prediction using Meteorological Data using Featured Based Weighted Xgboost
Nandigala Venkat Anurag1, Yagnavalk Burra2, S.Sharanya3, Gireeshan MG4
1VNandigala Venkat Anurag, UG Student, Department of Computer Science & Engineering, SRMIST, India.
2Yagnavalk Burra, UG Student, Department of Computer Science & Engineering, SRMIST, India.
3S.Sharanya, Assistant Professor, Department of Computer Science & Engineering, SRMIST, India.
4Dr Gireeshan MG, Scientist, Innovative Research/Patent Consultant, India.
Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1026-1029 | Volume-8 Issue-11S September 2019 | Retrieval Number: K121109811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1211.09811S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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. Hence to tackle this problem, efficient air quality prediction will enable us to foresee these undesirable changes made in the environment keeping the pollutant emission under check and control. Also inclusion of meteorological data for isolating the factors that contributes more to the Air Quality Index (AIQ) prediction is the need of the hour. A feature based weighted XGBoost model is built to predict the AIQ of Velachery, a fast developing commercial station in South India. The model resulted in low RMSE value when compared with other state of art techniques.
Keywords: Machine Learning, Air Quality Index, Artificial Intelligence.
Scope of the Article: Machine Learning