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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46 citations as recorded by crossref.
- Probabilistic Forecasting of El Niño Using Neural Network Models P. Petersik & H. Dijkstra 10.1029/2019GL086423
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- El Niño Index Prediction Based on Deep Learning with STL Decomposition N. Chen et al. 10.3390/jmse11081529
- Forecasting El Niño and La Niña events using decision tree classifier K. Silva et al. 10.1007/s00704-022-03999-5
- 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
- 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
- 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
- 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
- CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation M. Sun et al. 10.1029/2023GL105175
- A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses L. Zhou & R. Zhang 10.1007/s00376-021-1368-4
- Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting K. Peng et al. 10.3390/sym13060931
- Bibliometric Analysis of Methods and Tools for Drought Monitoring and Prediction in Africa O. Adisa et al. 10.3390/su12166516
- Prediction of Synoptic-Scale Sea Level Pressure Over the Indian Monsoon Region Using Deep Learning A. Sinha et al. 10.1109/LGRS.2021.3100899
- A machine learning based prediction system for the Indian Ocean Dipole J. Ratnam et al. 10.1038/s41598-019-57162-8
- Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI M. Chantry et al. 10.1098/rsta.2020.0083
- Short‐Term Precipitation Prediction for Contiguous United States Using Deep Learning G. Chen & W. Wang 10.1029/2022GL097904
- Spatiotemporal Model Based on Deep Learning for ENSO Forecasts H. Geng & T. Wang 10.3390/atmos12070810
- Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks B. Malozyomov et al. 10.3390/math12030480
- Confronting Grand Challenges in environmental fluid mechanics T. Dauxois et al. 10.1103/PhysRevFluids.6.020501
- Application of Deep Learning to Understanding ENSO Dynamics N. Shin et al. 10.1175/AIES-D-21-0011.1
- Predicting maximum temperatures over India 10-days ahead using machine learning models J. Ratnam et al. 10.1038/s41598-023-44286-1
- Forecasting the El Niño type well before the spring predictability barrier J. Ludescher et al. 10.1038/s41612-023-00519-8
- Evaluating the predictability of eight Atmospheric-Oceanic signals affecting Iran’s Droughts, employing intelligence based and stochastic methods P. Aghelpour et al. 10.1016/j.asr.2022.10.047
- Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground S. Scher & G. Messori 10.5194/gmd-12-2797-2019
- Performance of backpropagation artificial neural network to predict el nino southern oscillation using several indexes as onset indicators B. Aprilia et al. 10.1088/1742-6596/1876/1/012004
- 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
- Prediction of ENSO Beyond Spring Predictability Barrier Using Deep Convolutional LSTM Networks M. Gupta et al. 10.1109/LGRS.2020.3032353
- A review of artificial intelligence in marine science T. Song et al. 10.3389/feart.2023.1090185
- A Transformer‐Based Deep Learning Model for Successful Predictions of the 2021 Second‐Year La Niña Condition C. Gao et al. 10.1029/2023GL104034
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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
- ENSO analysis and prediction using deep learning: A review G. Wang et al. 10.1016/j.neucom.2022.11.078
Latest update: 23 Apr 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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