| Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01103 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/e3sconf/202339101103 | |
| Published online | 05 June 2023 | |
Air Quality Index Prediction
Department of Information Technology, GRIET, India
* Corresponding Author: pavithra.griet@gmail.com
Falling back past few years rapid progress in Air pollution has become a life-threatening concern in many nations throughout the world due to human activity, industrialisation, and urbanisation.. As a result of these activities, sulphur oxides, carbon dioxide (CO2), nitrogen oxides, carbon monoxide (CO), chlorofluorocarbons (CFC), lead, mercury, and other pollutants be emitted into atmosphere. Simultaneously, estimating quality of air is a tough undertaking because of evolution, variability, also unreasonable unpredictability over pollution and particle region and time. In this project we compare the two Algorithms of machine learning in predicting Index of Air Quality and its predominant. Support vector machine (SVM) exists as prominent machine learning method beneficial to forecasting pollutant plus particle levels and predicting the air quality index (AQI), and Random Forest Regression is another. We'll be working with data from India's Open Government Data Platform. This website displays Air Quality Index readings from around India, including Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), and Particulate Matter (PM) are examples of contaminants (PM10 and PM2.5), Carbon Monoxide (CO), and others. The output of the project is the predict of Air Quality index using two different algorithms and the comparison of models using various error metrics.
Key words: Machine Learning / Air Quality Index / SVM / Random Forest Regression / Error Metrics
© The Authors, published by EDP Sciences, 2023
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