Deep learning for multi-year ENSO forecasts
Author
Abstract
Suggested Citation
DOI: 10.1038/s41586-019-1559-7
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Fenghua Ling & Jing-Jia Luo & Yue Li & Tao Tang & Lei Bai & Wanli Ouyang & Toshio Yamagata, 2022. "Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
- Wenxiang, Ding & Caiyun, Zhang & Shaoping, Shang & Xueding, Li, 2022. "Optimization of deep learning model for coastal chlorophyll a dynamic forecast," Ecological Modelling, Elsevier, vol. 467(C).
- Yan, Qing-dong & Chen, Xiu-qi & Jian, Hong-chao & Wei, Wei & Wang, Wei-da & Wang, Heng, 2022. "Design of a deep inference framework for required power forecasting and predictive control on a hybrid electric mining truck," Energy, Elsevier, vol. 238(PC).
- Arturs Kempelis & Inese Polaka & Andrejs Romanovs & Antons Patlins, 2024. "Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems," Future Internet, MDPI, vol. 16(2), pages 1-14, January.
- Yuquan Qu & Diego G. Miralles & Sander Veraverbeke & Harry Vereecken & Carsten Montzka, 2023. "Wildfire precursors show complementary predictability in different timescales," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
- Wei Fang & Yu Sha & Victor S. Sheng, 2022. "Survey on the Application of Artificial Intelligence in ENSO Forecasting," Mathematics, MDPI, vol. 10(20), pages 1-22, October.
- Yumin Liu & Kate Duffy & Jennifer G. Dy & Auroop R. Ganguly, 2023. "Explainable deep learning for insights in El Niño and river flows," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
- Coulibaly, Saliya & Bessin, Florent & Clerc, Marcel G. & Mussot, Arnaud, 2022. "Precursors-driven machine learning prediction of chaotic extreme pulses in Kerr resonators," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
- Li, Zhuo-Lin & Yu, Jie & Zhang, Xiao-Lin & Xu, Ling-Yu & Jin, Bao-Gang, 2022. "A Multi-Hierarchical attention-based prediction method on Time Series with spatio-temporal context among variables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
- Xin Wei & Lulu Zhang & Junyao Luo & Dongsheng Liu, 2021. "A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(1), pages 471-497, October.
- Weston Anderson & Shraddhanand Shukla & Jim Verdin & Andrew Hoell & Christina Justice & Brian Barker & Kimberly Slinski & Nathan Lenssen & Jiale Lou & Benjamin I. Cook & Amy McNally, 2024. "Preseason maize and wheat yield forecasts for early warning of crop failure," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
- Cheng, Wei & Wang, Yan & Peng, Zheng & Ren, Xiaodong & Shuai, Yubei & Zang, Shengyin & Liu, Hao & Cheng, Hao & Wu, Jiagui, 2021. "High-efficiency chaotic time series prediction based on time convolution neural network," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:573:y:2019:i:7775:d:10.1038_s41586-019-1559-7. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.