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This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.
Seasonal variations, such as those due to El Niño/Southern Oscillation (ENSO) are difficult to predict using traditional methods. ML and deep learning can be useful for multi-year ENSO forecasting.
Conferences, Journals, and Professional Organizations
Libraries and Tools
Relevant Groups and Organizations
- Ham, Yoo-Geun; Kim, Jeong-Hwan; Luo, Jing-Jia (2019). "Deep learning for multi-year ENSO forecasts". Nature. 573 (7775): 568–572. doi:10.1038/s41586-019-1559-7. ISSN 1476-4687.
- Toms, Benjamin A.; Barnes, Elizabeth A.; Ebert‐Uphoff, Imme (2020). "Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability". Journal of Advances in Modeling Earth Systems. 12 (9): e2019MS002002. doi:10.1029/2019MS002002. ISSN 1942-2466.
- Mahesh,, A., et al., (2019). "Forecasting El Niño with Convolutional andRecurrent Neural Networks" (PDF).CS1 maint: extra punctuation (link)
- Cachay,, S. R. et al., (2020). "Graph Neural Networks for Improved El NiñoForecasting" (PDF).CS1 maint: extra punctuation (link)
- Guo, Yanan; Cao, Xiaoqun; Liu, Bainian; Peng, Kecheng (2020). "El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition". Symmetry. 12 (6): 893. doi:10.3390/sym12060893.