PM2.5 Prediction using Machine Learning Hybrid Model for Smart Health
J. Angelin Jebamalar1, A. Sasi Kumar2
1J. Angelin Jebamalar, Ph.D Research Scholor, Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, India.
2Dr. A. Sasi Kumar, Professor, Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Pallavaram, Chennai, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 6500-6505 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1187109119/2019©BEIESP | DOI: 10.35940/ijeat.A1187.109119
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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: Air Pollution is one of the current serious issue attributable to people’s health causing cardiopulmonary deaths, lung cancer and several respiratory problems. Air is polluted by numerous air pollutants, among which Particulate Matter (PM2.5) is considered harmful consists of suspended particles with a diameter less than 2.5 micrometers. This paper aims to acquire PM2.5 data through IoT devices, store it in Cloud and propose an improved hybrid model that predicts the PM2.5 concentration in the air. Finally through forecasting system we alert the public in case of an undesired condition. The experimental result shows that our proposed hybrid model achieve better performance than other regression models.
Keywords: IoT, Cloud, Air pollution, PM2.5, Machine Learning, Prediction, Ensemble, Regression algorithms