International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
Vol. 4, No. 1, 2007, pp. 141-149
Bioline Code: st07018
Full paper language: English
Document type: Research Article
Document available free of charge
International Journal of Environment Science and Technology, Vol. 4, No. 1, 2007, pp. 141-149
© Copyright 2007 Center for Environment and Energy Research and Studies (CEERS)
Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone|
Bandyopadhyay, G. & Chattopadhyay, S.
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.
Arosa, total ozone, single-hidden-layer, artificial neural network, multiple linear regression, forecast
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