• Shikha Saxena
• A.K Dwivedi
• Anil K Mathur

Urbanization of the cities and exponentially increasing population led global warming climate change and health problems related to environment. In this regard many studies have been conducted that are concerned about the prediction of pollutants using supervised learning methods to model the pollutant concentrations as well as to control the activities as per alerts signalized by future predictions. As per the Data shown by pollution control board the concentrations of ground level ozone is under control. To encapsulate the trend of ground level ozone promotes the study and future predictions In Kota city. The trend of pollutants is rising every day. For precise trend determination the continuous historical data are of utmost importance. In this paper time series forecasting approach is used for predicting future data by analyzing historical data and as a trend analyzer. Nonlinear Autoregressive (NAR) of time series tool of neural network have been used in this paper to predict series from past series values. Time series forecasting Predictor model is developed for the prediction of prime pollutant from i.e. Ground level ozone

Time series forecasting, O3, Health effects.

"Time series forecasting for ground level ozone in Kota city", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.62-65, May 2019, Available :http://www.jetir.org/papers/JETIRCY06009.pdf

"Time series forecasting for ground level ozone in Kota city", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp62-65, May 2019, Available at : http://www.jetir.org/papers/JETIRCY06009.pdf