Supervised Classification Estimate towards Air Pollutant Quantification of Delhi and Udaipur
PriyanshaJain1, Danish Paliwal2, Aditya Maheshwari3, Yogendra Singh Solanki4
1Priyansha Jain, B.Tech, Department of Computer Science, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
2Danish Paliwal, B.Tech Student, Department of Information Technology, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
3Aditya Maheshwari, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
4Yogendra Singh Solanki, Assistant Professor, Department of Electronics and Communications, Techno India NJR Institute of Technology, Udaipur (Rajasthan), India.
Manuscript received on 24 February 2020 | Revised Manuscript received on 10 March 2020 | Manuscript Published on 18 March 2020 | PP: 191-194 | Volume-8 Issue-6S March 2020 | Retrieval Number: F10320386S20/2020©BEIESP | DOI: 10.35940/ijrte.F1032.0386S20
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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 paper analyses air quality using supervised machine learning classifiers. The factorsconsidered for parameter selection towardsaffecting air quality are Benzene, BP(Barometer Pressure), PM10(Particulate Matter), PM2.5(Particulate Matter), RH(Relative Humidity), CO(Carbon Monoxide), NH3(Ammonia), NO(Nitrogen Oxide), NO2(Nitrogen Dioxide), NOx(Nitrogen Oxides), Ozone, SO2(Sulphur Dioxide).Curve fitting has been applied for analyzingpollutantsin air.
Keywords: Air Quality, Particulate Matter, Nitogen Oxide, Carbon Monoxide, Sulphur Oxides.
Scope of the Article: Classification