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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
23 Jul 2018
Research article | Highlight paper |
| 23 Jul 2018
| 23 Jul 2018
Using network theory and machine learning to predict El Niño
Peter D. Nooteboom
CORRESPONDING AUTHOR
Institute for Marine and Atmospheric Research Utrecht (IMAU), Department of Physics, Utrecht University, Utrecht, the Netherlands
Centre for Complex Systems Studies, Utrecht University, Utrecht, the Netherlands
Qing Yi Feng
Institute for Marine and Atmospheric Research Utrecht (IMAU), Department of Physics, Utrecht University, Utrecht, the Netherlands
Centre for Complex Systems Studies, Utrecht University, Utrecht, the Netherlands
Cristóbal López
Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, CSIC-UIB), University of the Balearic Islands, Balearic Islands, Spain
Emilio Hernández-García
Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, CSIC-UIB), University of the Balearic Islands, Balearic Islands, Spain
Henk A. Dijkstra
Institute for Marine and Atmospheric Research Utrecht (IMAU), Department of Physics, Utrecht University, Utrecht, the Netherlands
Centre for Complex Systems Studies, Utrecht University, Utrecht, the Netherlands
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- The Application of Machine Learning Techniques to Improve El Niño Prediction Skill H. Dijkstra et al. 10.3389/fphy.2019.00153
- Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels B. Hamzi et al. 10.1098/rspa.2021.0326
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- A Neural Network‐Based Scale‐Adaptive Cloud‐Fraction Scheme for GCMs G. Chen et al. 10.1029/2022MS003415
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- El Niño forecasting based on the global atmospheric oscillation I. Serykh & D. Sonechkin 10.1002/joc.6488
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- Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing R. Silini et al. 10.5194/esd-13-1157-2022
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- A review of artificial intelligence in marine science T. Song et al. 10.3389/feart.2023.1090185
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- Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data A. Chattopadhyay et al. 10.1038/s41598-020-57897-9
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- Machine Learning Models for the Seasonal Forecast of Winter Surface Air Temperature in North America Q. Qian et al. 10.1029/2020EA001140
- El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition Y. Guo et al. 10.3390/sym12060893
- Harnessing evolutionary algorithms for enhanced characterization of ENSO events U. Abdulkarimova et al. 10.1007/s10710-024-09497-z
Latest update: 06 Sep 2025
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.
The prediction of the El Niño phenomenon, an increased sea surface temperature in the eastern...
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