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Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone

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Abstract

Present paper endeavors to develop predictive artificial neural network model for forecasting the mean monthly total ozone concentration over Arosa, Switzerland. Single hidden layer neural network models with variable number of nodes have been developed and their performances have been evaluated using the method of least squares and error estimation. Their performances have been compared with multiple linear regression model. Ultimately, single-hidden-layer model with 8 hidden nodes have been identified as the best predictive model.

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Authors and Affiliations

  1. Department of Information Technology of Pailan College of Management and Technology under West Bengal, University of Technology, India

    G. Bandyopadhyay B.Sc. in chemistry, M.Sc. & S. Chattopadhyay B.Sc.

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  1. G. Bandyopadhyay B.Sc. in chemistry, M.Sc.
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  2. S. Chattopadhyay B.Sc.
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Correspondence to S. Chattopadhyay B.Sc..

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Bandyopadhyay, G., Chattopadhyay, S. Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. Int. J. Environ. Sci. Technol. 4, 141–149 (2007). https://doi.org/10.1007/BF03325972

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  • DOI: https://doi.org/10.1007/BF03325972

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