• Amrutha V K
• Harshita M Raj
• Nisha M
• Sindhu C
• Sumathi M.

The interpolation, prediction and feature analysis of fine air-quality are the three main important topics in the urban air computing, the solutions to these topics can provide useful information to support air pollution control. The economic and social impact of poor air quality in towns and cities is increasingly being recognized with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With increasing air pollution we need to implement efficient quality monitoring models which collect information about the concentration of air pollutants and provide assessment of air pollution in each area. The system for monitoring and forecasting urban air pollution is presented in this paper. The models are responsible for receiving and storing the data, preprocessing and converting the data into useful information forecasting the pollutants levels. Two machine learning models are used to find accurate forecasting models. The Machine Learning models used are Artificial Neural Network (ANN) and Random Forest Algorithm.

Air quality, forecasting, random forest.

"Air Pollution Prediction on Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.107-117, May-2019, Available :http://www.jetir.org/papers/JETIRCD06018.pdf

"Air Pollution Prediction on Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp107-117, May-2019, Available at : http://www.jetir.org/papers/JETIRCD06018.pdf