Articles | Volume 9, issue 3
Earth Syst. Dynam., 9, 969–983, 2018
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.
Earth Syst. Dynam., 9, 969–983, 2018
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 23 Jul 2018
Research article | 23 Jul 2018
Using network theory and machine learning to predict El Niño
Peter D. Nooteboom et al.
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Cited
17 citations as recorded by crossref.
- Probabilistic Forecasting of El Niño Using Neural Network Models P. Petersik & H. Dijkstra 10.1029/2019GL086423
- El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition Y. Guo et al. 10.3390/sym12060893
- 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
- The Application of Machine Learning Techniques to Improve El Niño Prediction Skill H. Dijkstra et al. 10.3389/fphy.2019.00153
- 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
- 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
- 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
- Spatiotemporal Model Based on Deep Learning for ENSO Forecasts H. Geng & T. Wang 10.3390/atmos12070810
- Challenges and design choices for global weather and climate models based on machine learning P. Dueben & P. Bauer 10.5194/gmd-11-3999-2018
- 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
- Confronting Grand Challenges in environmental fluid mechanics T. Dauxois et al. 10.1103/PhysRevFluids.6.020501
- Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier J. Meng et al. 10.1073/pnas.1917007117
- El Niño forecasting based on the global atmospheric oscillation I. Serykh & D. Sonechkin 10.1002/joc.6488
- A machine learning based prediction system for the Indian Ocean Dipole J. Ratnam et al. 10.1038/s41598-019-57162-8
- Temporal Convolutional Networks for the Advance Prediction of ENSO J. Yan et al. 10.1038/s41598-020-65070-5
- Machine Learning Models for the Seasonal Forecast of Winter Surface Air Temperature in North America Q. Qian et al. 10.1029/2020EA001140
15 citations as recorded by crossref.
- Probabilistic Forecasting of El Niño Using Neural Network Models P. Petersik & H. Dijkstra 10.1029/2019GL086423
- El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition Y. Guo et al. 10.3390/sym12060893
- 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
- The Application of Machine Learning Techniques to Improve El Niño Prediction Skill H. Dijkstra et al. 10.3389/fphy.2019.00153
- 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
- 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
- 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
- Spatiotemporal Model Based on Deep Learning for ENSO Forecasts H. Geng & T. Wang 10.3390/atmos12070810
- Challenges and design choices for global weather and climate models based on machine learning P. Dueben & P. Bauer 10.5194/gmd-11-3999-2018
- 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
- Confronting Grand Challenges in environmental fluid mechanics T. Dauxois et al. 10.1103/PhysRevFluids.6.020501
- Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier J. Meng et al. 10.1073/pnas.1917007117
- El Niño forecasting based on the global atmospheric oscillation I. Serykh & D. Sonechkin 10.1002/joc.6488
- A machine learning based prediction system for the Indian Ocean Dipole J. Ratnam et al. 10.1038/s41598-019-57162-8
Latest update: 24 Jul 2021
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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