@article{MAKHILLIJSC20138421156,
    title = {Forecasting Criteria Air Pollutants Using Data Driven Approaches: An Indian Case Study},
    journal = {International Journal of Soft Computing},
    volume = {8},
    number = {4},
    pages = {305-312},
    year = {2013},
    issn = {1816-9503},
    doi = {ijscomp.2013.305.312},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2013.305.312},
    author = {S. Tikhe,K.C. and},
    keywords = {Air quality,criteria pollutants,ANN,GP,India},
    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.}
    }