Earth System Dynamics
Earth System Dynamics
ESD
Articles | Volume 9, issue 3
Articles | Volume 9, issue 3
https://doi.org/10.5194/esd-9-969-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-9-969-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 3
Research article
 | Highlight paper
 | 
23 Jul 2018
Research article | Highlight paper |  | 23 Jul 2018

Using network theory and machine learning to predict El Niño

Peter D. Nooteboom, Qing Yi Feng, Cristóbal López, Emilio Hernández-García, and Henk A. Dijkstra

Viewed

Total article views: 8,848 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
6,324 2,316 208 8,848 251 217
  • HTML: 6,324
  • PDF: 2,316
  • XML: 208
  • Total: 8,848
  • BibTeX: 251
  • EndNote: 217
Views and downloads (calculated since 13 Mar 2018)
Cumulative views and downloads (calculated since 13 Mar 2018)

Viewed (geographical distribution)

Total article views: 8,848 (including HTML, PDF, and XML) Thereof 8,236 with geography defined and 612 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Nov 2025
Download
Short summary
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint