Novel deseasonalizing models for improving the prediction of total ozone in column using evolutionary programming and neural networks

In this paper we present a novel method for deseasonalizing TOC data using non-linear models, with evolutionary computation techniques, and its performance with a neural network as regression approach. Specifically, the proposed deseasonalization method uses an evolutionary programming (EP) approach to carry out a curve fitting problem, where a given function model is optimized to be as similar as possible to an objective curve (a real TOC measurement in this case). Different non-linear models are proposed to be optimized with the EP algorithm. In addition, we test the possibility of deseasonalizing the TOC measurement and also the meteorological input data. The deseasonalized series is then used to train a neural network (multi-layer perceptron). We test the proposed models in the prediction of several TOC series in the Iberian Peninsula, where we carry out a comparison against a reference deseasonalizing model previously proposed in the literature. The results obtained show the good performance of some of the deseasonalizing models proposed in this paper.

Research Highlights
► Investigation about the prediction of Total Ozone in Column using artificial neural networks.
► We test several novel deseasonalization techniques which are applied to the TOC series.
► We show how the deseasonalization techniques improve the performance of the neural network.
► Experiments in real TOC data in the Iberian Peninsula have shown the good performance of the method.