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www.nonlin-processes-geophys.net/11/683/2004/
doi:10.5194/npg-11-683-2004
© Author(s) 2004. This work is licensed
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Total ozone time series analysis: a neural network model approach
Electronics Design Group – GDE–, Faculty of Sciences, University of Zaragoza, E-50009 Zaragoza, Spain
Abstract. This work is focused on the application of neural network based models to the analysis of total ozone (TO) time series. Processes that affect total ozone are extremely non linear, especially at the considered European mid-latitudes. Artificial neural networks (ANNs) are intrinsically non-linear systems, hence they are expected to cope with TO series better than classical statistics do. Moreover, neural networks do not assume the stationarity of the data series so they are also able to follow time-changing situations among the implicated variables. These two features turn NNs into a promising tool to catch the interactions between atmospheric variables, and therefore to extract as much information as possible from the available data in order to make, for example, time series reconstructions or future predictions. Models based on NNs have also proved to be very suitable for the treatment of missing values within the data series. In this paper we present several models based on neural networks to fill the missing periods of data within a total ozone time series, and models able to reconstruct the data series. The results released by the ANNs have been compared with those obtained by using classical statistics methods, and better accuracy has been achieved with the non linear ANNs techniques. Different network structures and training strategies have been tested depending on the specific task to be accomplished.
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Citation: Monge Sanz, B. M. and Medrano Marqués, N. J.: Total ozone time series analysis: a neural network model approach, Nonlin. Processes Geophys., 11, 683-689, doi:10.5194/npg-11-683-2004, 2004. Bibtex EndNote Reference Manager XML
