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

Salcedo-Sanz, S. and Deo, R. C. and Carro-Calvo, L. and Saavedra-Moreno, B. (2016) Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theoretical and Applied Climatology, 125 (1). pp. 13-25. ISSN 0177-798X

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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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: This paper represents collaboration between USQ and Universidad de Alcal´a, Madrid (Spain). Permanent restricted access to Published version in accordance with the copyirhgt policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 12 Jun 2015 06:01
Last Modified: 20 Oct 2016 03:48
Uncontrolled Keywords: machine learning; temperature modelling in Australia and New Zealand; support vector regression; artificial intelligence
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010406 Stochastic Analysis and Modelling
04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
04 Earth Sciences > 0401 Atmospheric Sciences > 040102 Atmospheric Dynamics
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
01 Mathematical Sciences > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960202 Atmospheric Processes and Dynamics
D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
E Expanding Knowledge > 97 Expanding Knowledge > 970104 Expanding Knowledge in the Earth Sciences
D Environment > 96 Environment > 9603 Climate and Climate Change > 960311 Social Impacts of Climate Change and Variability
E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
D Environment > 96 Environment > 9602 Atmosphere and Weather > 960203 Weather
Identification Number or DOI: 10.1007/s00704-015-1480-4
URI: http://eprints.usq.edu.au/id/eprint/27227

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