Abstract
This paper presents the notion of evolutionary nonlinear prediction technique for temperature forecast based on GP (Genetic Programming). The linear regression method is widely used in most of numeric weather prediction model. Their performances are acceptable, but some limitation is existed for nonlinear natures of the weather prediction. We explain how to apply symbolic regression method using GP for the nonlinear prediction model using less weather elements. In order to verify the possibility of the proposed method, experiments of temperature forecast for the sampled locations in South Korea are executed.
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© 2014 Springer International Publishing Switzerland
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Seo, K. (2014). Toward Evolutionary Nonlinear Prediction Model for Temperature Forecast Using Less Weather Elements. In: Rocha, Á., Correia, A., Tan, F., Stroetmann, K. (eds) New Perspectives in Information Systems and Technologies, Volume 1. Advances in Intelligent Systems and Computing, vol 275. Springer, Cham. https://doi.org/10.1007/978-3-319-05951-8_46
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DOI: https://doi.org/10.1007/978-3-319-05951-8_46
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05950-1
Online ISBN: 978-3-319-05951-8
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