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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2015-273
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-2015-273
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: model description paper 11 Feb 2016

Submitted as: model description paper | 11 Feb 2016

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This preprint was under review for the journal GMD but the revision was not accepted.

ClimateLearn: A machine-learning approach for climate prediction using network measures

Qing Yi Feng1, Ruggero Vasile2,3, Marc Segond4, Avi Gozolchiani5, Yang Wang5, Markus Abel3, Shilomo Havlin5, Armin Bunde6, and Henk A. Dijkstra1 Qing Yi Feng et al.
  • 1Institute for Marine and Atmospheric research Utrecht, Utrecht University, The Netherlands
  • 2UP Transfer, Potsdam, Germany
  • 3Ambrosys, Potsdam, Germany
  • 4European Centre for Soft Computing, Mieres, Spain
  • 5Bar-Ilan University, Isreal
  • 6University of Giessen, Germany

Abstract. We present the toolbox ClimateLearn to tackle problems in climate prediction using machine learning techniques and climate network analysis. The package allows basic operations of data mining, i.e. reading, merging, and cleaning data, and running machine learning algorithms such as multilayer artificial neural networks and symbolic regression with genetic programming. Because spatial temporal information on climate variability can be efficiently represented by complex network measures, such data are considered here as input to the machine-learning algorithms. As an example, the toolbox is applied to the prediction of the occurrence and the development of El Niño in the equatorial Pacific, first concentrating on the occurrence of El Niño events one year ahead and second on the evolution of sea surface temperature anomalies with a lead time of three months.

Qing Yi Feng et al.

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Qing Yi Feng et al.

Qing Yi Feng et al.

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Short summary
We present the toolbox ClimateLearn to tackle problems in climate prediction using machine learning techniques and climate network analysis. Because spatial temporal information on climate variability can be efficiently represented by complex network measures, such data are considered here as input to the machine-learning algorithms. As an example, the toolbox is applied to the prediction of the occurrence and the development of El Niño in the equatorial Pacific.
We present the toolbox ClimateLearn to tackle problems in climate prediction using machine...
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