Using network theory and machine learning  to predict El Niño

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

doi:https://doi.org/10.5194/esd-9-969-2018

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

Data sets

Upper Ocean Heat Content and ENSO National Oceanic and Atmospheric Administration https://www.pmel.noaa.gov/elnino/upper-ocean-heat-content-and-enso

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.