Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland

Abstract

The 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 Network 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 a learning rate of 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 to be skillful. But the Two‐hidden‐layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.