• S. Kanageswari
• Dr. D. Gladis

Forecasting of air quality parameters is one topic of air quality research today due to the health effects caused by airborne pollutants in various areas. The quality of air is adversely affected due to various forms of pollution caused by transportation, electricity, fuel uses etc. The deposition of harmful gases is creating a serious threat for the quality of life in smart cities. With increasing air pollution, we need to implement efficient air quality monitoring models which collect information about the concentration of air pollutants and provide assessment of air pollution in each area. Hence, air quality evaluation and prediction has become an important research area. The quality of air is affected by multi-dimensional factors including location, time, and uncertain variables. There are different type of numerical as well as statistical tools for the prediction and analysis of air quality, but Artificial Neural Network is considered to be an excellent predictive and data analysis tool for Air quality forecasting. The generalization ability of the model is confirmed by root mean square error and correlation between observed and predicted concentrations. There are two modules used in forecasting the air pollution one with Multi-Layer Perceptron (MLP) neural network and the other with using statistical. With respect to the results obtained from the two models it is clear that MLP model gave the better results compared to the statistical indicators.

Artificial Neural Network, forecasting, root mean square error, correlation, statistical

"Forecasting the Air Pollution with using Artificial Neural Networks at Chennai", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.602-609, April-2019, Available :http://www.jetir.org/papers/JETIR1904891.pdf

"Forecasting the Air Pollution with using Artificial Neural Networks at Chennai", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp602-609, April-2019, Available at : http://www.jetir.org/papers/JETIR1904891.pdf