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Modelling air pollution time-series by using wavelet functions and genetic algorithms

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Abstract

This paper proposes a novel approach based on the use of wavelet functions to model air pollution time series. One peculiarity of the approach is that of combining the use of wavelets and genetic algorithms to search for the best wavelet parameters. A case study, referring to the modelling of daily averages of SO2 time series recorded in the industrial area of Syracuse (Italy) is reported in order to compare the performance of a wavelet-based prediction model and a Multi-layer perceptron neural model. The results obtained show that there are no significant differences between the neural and the wavelet approach in terms of model performance and computational effort. There is however an appreciable advantage in using the proposed wavelet-based technique in terms of model readability.

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Correspondence to G. Nunnari.

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The paper has been financially supported by the EU in the framework of the APPETISE project (Contract N. IST-99–11764). The author is also grateful to the Municipal and Provincial authorities in Syracuse (Italy) for providing the pollution and meteorological data considered in the paper. Finally the author is grateful to Dr Libero Bertucco who helped to code part of the software package considered in this work.

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Nunnari, G. Modelling air pollution time-series by using wavelet functions and genetic algorithms. Soft Computing 8, 173–178 (2004). https://doi.org/10.1007/s00500-002-0260-0

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  • DOI: https://doi.org/10.1007/s00500-002-0260-0

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