Analysis of Air Quality in Machine Learning

  • Deepali Jawale
  • Rashmi Deshpande
  • Vishal Patil
Keywords: Air Quality, Deep Learning, Machine Learning


Mainly the urbanization and industrial technologies around the world were responsible for environmental contamination. Pollution has been marked in conjunction with the most critical issues of metropolitan areas of the world, especially in Delhi, the Indian capital, wherever its leaders and people have long been grappling with pollution, such as health problems with their citizens. Asian nation's air quality by using machine learning to predict a given area's air quality index. Indian air quality index could be a normal measure used to show the amount of waste (so2, no2, rspm, spm, etc.) over a quantity. We examine model for predicting an air quality index that has supported past year historical knowledge and forecasts a certain multivariable regression in the next year as a positive gradient. The most widely used air quality models during this paper include dispersion models, chemistry and regression models. Some neural network models have also been shown to be effective in predicting air quality as non-linear regression models. Completely different models and applications will be introduced during this section.

How to Cite
Jawale, D., Deshpande, R., & Patil, V. (2019). Analysis of Air Quality in Machine Learning. National Journal of Computer and Applied Science, 2(3), 22-25. Retrieved from