Prediction of mean monthly total ozone time series-application of radial basis function network

Authors:
Surajit Chattopadhyay
Affiliations:
Department of Information Technology, Pailan College of Management and Technology, Bengal Pailan Park, Kolkata - 700 104, India
Venue:
International Journal of Remote Sensing
Year:
2007

Citing 4
Cited 2

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Median radial basis function neural network

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Forecasting daily total ozone concentration-a comparison between neurocomputing and statistical approaches

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Autoregressive forecast of monthly total ozone concentration: A neurocomputing approach

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Abstract

The primary objective of the present paper is to apply Artificial Neural Network in the form of Radial Basis Function network to predict the mean monthly total ozone concentration over Arosa, Switzerland (46.8° N/9.68° E). The satellite observations of the total ozone content are based on the total ozone observations performed by the ground-based instrumentation. While analysing the dataset it was found that January, February and March are the months of maximum variability in the mean monthly total ozone over the stated region. Then, these three months were considered as the target months to frame the predictive model. After appropriate training and testing, it was found that Radial Basis Function network is a suitable neural net type for predicting the aforesaid time series. Moreover, this kind of neural net was found most adroit in predicting the mean monthly total ozone in the month of January.