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.
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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52 citations as recorded by crossref.
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- A novel deep neural network model approach to predict Indian Ocean dipole and Equatorial Indian Ocean oscillation indices P. Sarkar et al. 10.1016/j.dynatmoce.2021.101266
- El Niño Index Prediction Based on Deep Learning with STL Decomposition N. Chen et al. 10.3390/jmse11081529
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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
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- A Place‐Based Approach to Drought Forecasting in South‐Central Oklahoma R. McPherson et al. 10.1029/2022EA002315
- Winter Surface Air Temperature Prediction over Japan Using Artificial Neural Networks J. Ratnam et al. 10.1175/WAF-D-20-0218.1
- Survey on the Application of Artificial Intelligence in ENSO Forecasting W. Fang et al. 10.3390/math10203793
- The efficacy of tropical and extratropical predictors for long‐lead El Niño‐Southern Oscillation prediction: A study using a machine learning algorithm W. Song et al. 10.1002/joc.8241
- Reservoir Computing with Delayed Input for Fast and Easy Optimisation L. Jaurigue et al. 10.3390/e23121560
- A New Hybrid Machine Learning Model for Short-Term Climate Prediction by Performing Classification Prediction and Regression Prediction Simultaneously D. Li et al. 10.1007/s13351-022-1214-3
- Predicting daily maximum temperature over Andhra Pradesh using machine learning techniques S. Velivelli et al. 10.1007/s00704-024-05146-8
- Challenges and design choices for global weather and climate models based on machine learning P. Dueben & P. Bauer 10.5194/gmd-11-3999-2018
- Network percolation provides early warnings of abrupt changes in coupled oscillatory systems: An explanatory analysis N. Ehstand et al. 10.1103/PhysRevE.108.054207
- A Neural Network‐Based Scale‐Adaptive Cloud‐Fraction Scheme for GCMs G. Chen et al. 10.1029/2022MS003415
- Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier J. Meng et al. 10.1073/pnas.1917007117
- Network-based forecasting of climate phenomena J. Ludescher et al. 10.1073/pnas.1922872118
- El Niño forecasting based on the global atmospheric oscillation I. Serykh & D. Sonechkin 10.1002/joc.6488
- Spatio-temporal data generation based on separated attention for ENSO prediction L. Lin et al. 10.1007/s10489-024-05547-2
- Committor Functions for Climate Phenomena at the Predictability Margin: The Example of El Niño–Southern Oscillation in the Jin and Timmermann Model D. Lucente et al. 10.1175/JAS-D-22-0038.1
- Feature Selection and Spatial-Temporal Forecast of Oceanic Niño Index Using Deep Learning J. Jonnalagadda & M. Hashemi 10.1142/S0218194022500048
- Unpacking the black box of deep learning for identifying El Niño-Southern oscillation Y. Sun et al. 10.1088/1572-9494/ace17d
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- CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation M. Sun et al. 10.1029/2023GL105175
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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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Latest update: 23 Nov 2024
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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