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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM17979

Air Quality Index Prediction

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM17979

Volume:

7 |

3 |

Mar 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DKP

Dr Kanmani P

SG

Sarthak Gupta

GK

Gurleen Kaur

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Abstract

Air pollution poses a significant threat to public health, particularly in rapidly developing countries like India, where urbanization and industrialization contribute to diverse sources of pollutants. This research employs machine learning techniques to comprehensively analyse and predict air quality levels across different regions of India. Extensive air quality data, encompassing various pollutants and temporal variations, is collected and compiled. State-ofthe-art machine learning models are developed to identify key influencing factors, including industrial emissions, vehicular traffic, agricultural practices, and meteorological conditions. The models are evaluated for accuracy and reliability in predicting air quality levels. The study aims to provide valuable insights into the complex dynamics of air pollution in the Indian context. By understanding the contributing factors and predicting future air quality levels, this research contributes to the development of targeted and effective air quality management strategies.

KEYWORDS

Machine Learning Air Pollution AQI.

Cite This Article

Dr Kanmani P, Sarthak Gupta, Gurleen Kaur, "Air Quality Index Prediction," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 3, Mar 2023. https://doi.org/10.55041/IJSREM17979

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Publication Date

3/23/2024

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Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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