Review of Air Pollution Hotspots Detection and Identifying the Source Trajectories using ML Techniques

May 2022
Vol-8, Issue-3
Paper ID: 16714
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
Computer Engineering
Keywords
Air Pollution PM2.5 SVM ARIMA SARIMA LSTM time-series analysis Air quality prediction AQI.
Abstract
One of our era’s greatest scourges is air pollution, on account not only of its impact on climate change but also its impact on public and individual health due to increasing morbidity and mortality. Time series AQI data is collected through the CPCB sensors in different stations all over India. Classification of hotspots is done using SVM, and the time series analysis based on pollutants like PM2.5, PM10, CO, NO data samples is done using LSTM, ARIMA and SARIMA. Pollution levels of a day in the future are predicted using the said models. This review paper focuses on the various techniques used for prediction or modeling of Air Quality Index (AQI) and forecasting of future concentration levels of pollutants that may cause the air pollution so that governing bodies can take the actions to reduce the pollution.

Author Information

# Name Institute / Affiliation
1 Ashwini Koshta Modern Education Society's College of Engineering
2 Asma Bekinalkar Modern Education Society's College of Engineering
3 Diksha Tickoo Modern Education Society's College of Engineering
4 Liza Souza Modern Education Society's College of Engineering
5 Prof. Shobha Raskar Modern Education Society's College of Engineering
6 Prof. Jaya Mane Modern Education Society's College of Engineering

How to Cite

Use the following formats to cite this article in your research.

APA Style
Koshta, Ashwini, Bekinalkar, Asma, Tickoo, Diksha, Souza, Liza, Raskar, Prof. Shobha, & Mane, Prof. Jaya (2022). Review of Air Pollution Hotspots Detection and Identifying the Source Trajectories using ML Techniques. International Journal of Advance Research and Innovative Ideas In Education, 8(3), 1081-1086.
MLA Style
Koshta, Ashwini, et al. "Review of Air Pollution Hotspots Detection and Identifying the Source Trajectories using ML Techniques." International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 3, 2022, pp. 1081-1086.
IEEE Style
Ashwini Koshta, Asma Bekinalkar, Diksha Tickoo, Liza Souza, Prof. Shobha Raskar, and Prof. Jaya Mane, "Review of Air Pollution Hotspots Detection and Identifying the Source Trajectories using ML Techniques," International Journal of Advance Research and Innovative Ideas In Education, vol. 8, no. 3, pp. 1081-1086, 2022.
Vancouver Style
Koshta Ashwini, Bekinalkar Asma, Tickoo Diksha, Souza Liza, Raskar Prof. Shobha, Mane Prof. Jaya. Review of Air Pollution Hotspots Detection and Identifying the Source Trajectories using ML Techniques. International Journal of Advance Research and Innovative Ideas In Education. 2022;8(3):1081-1086.
Harvard Style
Koshta, Ashwini, Bekinalkar, Asma, Tickoo, Diksha, Souza, Liza, Raskar, Prof. Shobha, & Mane, Prof. Jaya (2022) 'Review of Air Pollution Hotspots Detection and Identifying the Source Trajectories using ML Techniques', International Journal of Advance Research and Innovative Ideas In Education, 8(3), pp. 1081-1086.
Chicago Style
Koshta, Ashwini, et al. "Review of Air Pollution Hotspots Detection and Identifying the Source Trajectories using ML Techniques." International Journal of Advance Research and Innovative Ideas In Education 8, no. 3 (2022): 1081-1086.
Turabian Style
Koshta, Ashwini, et al. "Review of Air Pollution Hotspots Detection and Identifying the Source Trajectories using ML Techniques." International Journal of Advance Research and Innovative Ideas In Education 8, no. 3 (2022): 1081-1086.

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