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Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

Theoretical and Applied Climatology volume 125, pages 13–25 (2016)Cite this article

Abstract

Long-term air temperature prediction is of major importance in a large number of applications, including climate-related studies, energy, agricultural, or medical. This paper examines the performance of two Machine Learning algorithms (Support Vector Regression (SVR) and Multi-layer Perceptron (MLP)) in a problem of monthly mean air temperature prediction, from the previous measured values in observational stations of Australia and New Zealand, and climate indices of importance in the region. The performance of the two considered algorithms is discussed in the paper and compared to alternative approaches. The results indicate that the SVR algorithm is able to obtain the best prediction performance among all the algorithms compared in the paper. Moreover, the results obtained have shown that the mean absolute error made by the two algorithms considered is significantly larger for the last 20 years than in the previous decades, in what can be interpreted as a change in the relationship among the prediction variables involved in the training of the algorithms.

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Acknowledgments

The high quality mean temperature datasets and climate mode indices were obtained from the Australian Bureau of Meteorology.

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Authors and Affiliations

  1. Department of Signal Processing and Communications, Universidad de Alcalá, Madrid, Spain

    S. Salcedo-Sanz, L. Carro-Calvo & B. Saavedra-Moreno

  2. School of Agricultural Computational and Environmental Sciences, University of Southern Queensland, Springfield, Australia

    R. C. Deo

Authors
  1. S. Salcedo-Sanz
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  2. R. C. Deo
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  3. L. Carro-Calvo
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  4. B. Saavedra-Moreno
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Correspondence to S. Salcedo-Sanz.

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Salcedo-Sanz, S., Deo, R.C., Carro-Calvo, L. et al. Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theor Appl Climatol 125, 13–25 (2016). https://doi.org/10.1007/s00704-015-1480-4

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  • DOI: https://doi.org/10.1007/s00704-015-1480-4

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