Affiliations: [a] Department of Information Technology, Pailan College of Management and Technology, Affiliated to West Bengal University of Technology, Kolkata 700 104, India | [b] 1/19 Dover Place, Kolkata 700 019, Formerly, Department of Atmospheric Sciences, University of Calcutta, Kolkata 700 019, West Bengal, India
Abstract: The central premise of the present research is to judge the performance of Artificial Neural Network against that of the conventional statistical autoregressive approach in predicting the mean monthly total ozone concentration one month in advance over Arosa, a locality in Switzerland (46.8°N/9.68°E). Prior to the implementation of neural net methodology to the dataset, some significant developments in the application of Artificial Neural Networks to the pollution study have been reviewed. Basic principles of feed forward neural nets are also briefly canvassed. In the implementation phase, instead of considering meteorological parameters, the past values of the given variable have been considered as predictor. After rigorous study it has been established that a three hidden layers Artificial Neural Network with Backpropagation algorithm produces better forecasts than a linear autoregressive procedure.
Keywords: Total ozone, time series, Arosa, Artificial Neural Network, Backpropagation, prediction, auto regression
