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

Solstice: An Electronic Journal of Geography and Mathematics, Volume XVIII, Number 1

This document was delivered over the internet. The .zip file contains all static images, animated images, and text files.The purpose of Solstice is to promote interaction between geography and mathematics. Articles in which elements of one discipline are used to shed light on the other are particularly sought. Also welcome, are original contributions that are purely geographical or purely mathematical. These may be prefaced (by editor or author) with commentary suggesting directions that might lead toward the desired interaction. Contributed articles will be refereed by geographers and/or mathematicians. Invited articles will be screened by suitable members of the editorial board. IMaGe is open to having authors suggest, and furnish material for, new regular features.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58244/2/sum107.ziphttp://deepblue.lib.umich.edu/bitstream/2027.42/58244/3/sum07.ziphttps://deepblue.lib.umich.edu/bitstream/2027.42/58244/4/SolsticeVolXVIIINo1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/58244/6/SolsticeVolXVIIINo1.pdfDescription of SolsticeVolXVIIINo1.pdf : Cover of JournalDescription of SolsticeVolXVIIINo1.pdf : Cover fil