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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 3
Earth Syst. Dynam., 9, 969–983, 2018
https://doi.org/10.5194/esd-9-969-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Earth Syst. Dynam., 9, 969–983, 2018
https://doi.org/10.5194/esd-9-969-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 23 Jul 2018

Research article | 23 Jul 2018

Using network theory and machine learning to predict El Niño

Peter D. Nooteboom et al.

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Cited articles

Akaike, H.: A New Look at the Statistical Model Identification, IEEE T. Automat. Contr., AC-19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974.
Aladag, C. H., Egrioglu, E., and Kadilar, C.: Forecasting nonlinear time series with a hybrid methodology, Appl. Math. Lett., 22, 1467–1470, https://doi.org/10.1016/j.aml.2009.02.006, 2009.
Al-Smadi, A. and Al-Zaben, A.: ARMA Model Order Determination Using Edge Detection: A New Perspective, Circuits, Systems Signal Processing, 24, 723–732, 2005.
Berezin, Y., Gozolchiani, A., Guez, O., and Havlin, S.: Stability of Climate Networks with Time, Sci. Rep.-UK, 2, 1–8, https://doi.org/10.1038/srep00666, 2012.
Bergmeir, C. and Benítez, J. M.: On the use of cross-validation for time series predictor evaluation, Inf. Sci. (Ny)., 191, 192–213, https://doi.org/10.1016/j.ins.2011.12.028, 2012.
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The prediction of the El Niño phenomenon, an increased sea surface temperature in the eastern Pacific, fascinates people for a long time. El Niño is associated with natural disasters, such as droughts and floods. Current methods can make a reliable prediction of this phenomenon up to 6 months ahead. However, this article presents a method which combines network theory and machine learning which predicts El Niño up to 1 year ahead.
The prediction of the El Niño phenomenon, an increased sea surface temperature in the eastern...
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