Authors : S. Tikhe Shruti, K.C. Khare and S.N. Londhe
Abstract: Forecasting air pollutant trends especially in metropolitan cities of India has become a vital issue as air pollution has immediate and severe impacts on human health. Criteria pollutants like Oxides of Sulphur (SOx), Oxides of Nitrogen (NOx) and Respirable Suspended Particulate Matter (RSPM) have either reached or exceeded the acceptable limits specified by Central Pollution Control Board of India for Pune city which is at the second position as far as pollution levels of India are concerned. In the present research, two soft computing approaches namely Artificial Neural Networks (ANN) and Genetic Programming (GP) are used to predict the air quality parameters (SOx, NOx, RSPM) a few time steps in advance for Pune city. Six models have been developed based on daily average data values of pollutant concentrations spanning >7 years. ANN, one of the proven tools in estimation and prediction of air quality has been used and the results of the models are compared with GP which is rarely used tool in the field of air quality modelling and forecasting. The performance of the models has been compared using r, RMSE and d. Considering the complexity of the air pollution phenomenon, it was found that GP Models are robust and could work well as compared to ANN.
S. Tikhe Shruti, K.C. Khare and S.N. Londhe, 2013. Forecasting Criteria Air Pollutants Using Data Driven Approaches: An Indian Case Study. International Journal of Soft Computing, 8: 305-312.