NDLI: Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

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Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

Content Provider	Springer Nature Link
Author	Chattopadhyay, Surajit Chattopadhyay, Goutami
Copyright Year	2011
Abstract	In the work discussed in this paper we considered total ozone time series over Kolkata (22°34′10.92″N, 88°22′10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.
Starting Page	1891
Ending Page	1908
Page Count	18
File Format	PDF
ISSN	00334553
Journal	Pure and applied geophysics
Volume Number	169
Issue Number	10
e-ISSN	14209136
Language	English
Publisher	SP Birkhäuser Verlag Basel
Publisher Date	2011-11-27
Publisher Place	Basel
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural network total ozone meteorological data prediction estimation principal component analysis Geophysics/Geodesy
Content Type	Text
Resource Type	Article
Subject	Geochemistry and Petrology Geophysics

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