PREDICTION OF AIR POLLUTION BY USING MACHINE LEARNING

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Mr. A.Damodar Rao
Mr. A.Damodar Rao
T. Renuka
Y. Bhargavi

Abstract

Defensive and in charge Nowadays, in many developing and urban areas, the greater air quality has become one of the most important factors in everything. The magnificence of the air is negatively affecting collectibles due to the many tainting methods caused by power consumption, transportation, and other factors. Population growth is a major issue in our nation as it is happening at a rapid pace. This, along with economic expansion, is causing environmental issues in cities, such as water and air pollution. in a portion of the air.


Air pollution and pollutants have a direct effect on human health. As is well known, the main sources of pollution include carbon monoxide, nitrogen oxide, particulate matter (PM), so2; etc. A propellant such as gasoline, petroleum, etc. that has not been properly oxidized is producing carbon monoxide. The burning of thermal fuel releases nitrogen oxide (NO), but sulfur dioxide (So2), one of the main air pollutants, is more prevalent and has a greater impact on human health. Multidimensional collisions with location, time, and imprecise boundaries augment the air's dominance. To examine AI-based approaches for air quality prediction is the aim of this enhancement. In this research, we will use a machine learning system to forecast air pollution.

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How to Cite
Damodar Rao, A., Soumya, S., Renuka, T., & Bhargavi, Y. . (2024). PREDICTION OF AIR POLLUTION BY USING MACHINE LEARNING . Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(3), 292–300. https://doi.org/10.61841/turcomat.v15i3.14803
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References

Ni, X.Y.; Huang, H.; Du, W.P. “Relevance analysis and short-term prediction of PM 2.5 concentrations in Beijing based on multi-source data.” Atmos. Environ. 2017, 150, 146-161.

G. Corani and M. Scanagatta, "Air pollution prediction via multi-label classification," Environ. Model. Softw., vol. 80, pp. 259-264,2016.

Mrs. A. GnanaSoundariMtech, (Phd) ,Mrs. J. GnanaJeslin M.E, (Phd), Akshaya A.C. “Indian Air Quality Prediction And Analysis Using Machine Learning”. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 11, 2019 (Special Issue).

Suhasini V. Kottur , Dr. S. S. Mantha. “An Integrated Model Using Artificial Neural Network

RuchiRaturi, Dr. J.R. Prasad .“Recognition Of Future Air Quality Index Using Artificial Neural Network”.International Research Journal ofEngineering and Technology (IRJET) .e-ISSN: 2395-0056 p-ISSN: 2395-0072 Volume: 05 Issue: 03 Mar-2018

Aditya C R, Chandana R Deshmukh, Nayana D K, Praveen Gandhi Vidyavastu .” Detection and Prediction of Air Pollution using Machine Learning Models”. International Journal o f Engineering Trends and Technology (IJETT) - volume 59 Issue 4 - May 2018

Gaganjot Kaur Kang, Jerry ZeyuGao, Sen Chiao, Shengqiang Lu, and Gang Xie.” Air Quality Prediction: Big Data and Machine Learning Approaches”. International Journal o f Environmental Science and Development, Vol. 9, No. 1, January 2018

PING-WEI SOH, JIA-WEI CHANG, AND JEN-WEI HUANG,” Adaptive Deep Learning-Based Air Quality Prediction Model Using the Most Relevant Spatial-Temporal Relations,” IEEE ACCESSJuly 30, 2018.Digital Object Identifier10.1109/ACCESS.2018.2849820.

GaganjotKaur Kang, Jerry Zeyu Gao, Sen Chiao, Shengqiang Lu, and Gang Xie,”Air Quality Prediction: Big Data and Machine Learning Approaches,” International Journal of Environmental Science and Development, Vol. 9, No. 1, January2018.

Haripriya Ayyalasomayajula, Edgar Gabriel, Peggy Lindner and Daniel Price, “Air Quality Simulations using Big Data Programming Models,” IEEE Second International Conference on Big Data Computing Serviceand Applications,2016.