21 research outputs found
Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland
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.Comment: 22 pages, 14 figure
Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India
In the present research, possibility of predicting average summer-monsoon
rainfall over India has been analyzed through Artificial Neural Network models.
In formulating the Artificial Neural Network based predictive model, three
layered networks have been constructed with sigmoid non-linearity. The models
under study are different in the number of hidden neurons. After a thorough
training and test procedure, neural net with three nodes in the hidden layer is
found to be the best predictive model.Comment: 19 pages, 1 table, 3 figure