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Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland

Surajit Chattopadhyay·Goutami Bandyopadhyay
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Abstract

Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.

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Surajit Chattopadhyay, Goutami Bandyopadhyay. 2006-08-17. Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland. https://doi.org/10.1080/01431160701250440

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