Air Quality Index Prediction

• Published in: E3S web of conferences 2023-01, Vol.391, p.1103
• Main Authors: Avvari, Pavithra, Nacham, Preethi, Sasanapuri, Snehitha, Mankena, Sirija Reddy, Kudipudi, Phanisree, Madapati, Aishwarya
• Format: Article
• Language: English
• Subjects: air quality index; error metrics; machine learning; random forest regression; svm
• ISSN: 2267-1242; 2267-1242
• DOI: 10.1051/e3sconf/202339101103

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.