Prediction of the ENSO and EQUINOO indices during June–September using a deep learning method
In one sentence. Autoencoder features with regression trees predict the EQUINOO index 7 months ahead (r=0.88) and ENSO 1 month ahead (r=0.87).
Abstract
Abstract The Equatorial Indian Ocean Oscillation (EQUINOO) and El Niño Southern Oscillation (ENSO) are important climatic oscillations over the Indian and Pacific oceans influencing the inter‐annual variation of the Indian monsoon. The study of these indices, including their relationship and influence over various climatic phenomena, is the main focus in the literature. However, an attempt is made here to predict the indices for different temporal periods. Though ENSO prediction is established by many statistical and numerical models, the prediction of the EQUINOO index is not much studied. A deep‐learning method using an autoencoder is proposed for the prediction of the EQUINOO and ENSO. An autoencoder assists in feature learning. The learned features are ranked using linear and nonlinear correlation studies. This assists in identifying a set of potential predictors, used for indices prediction, with an ensemble of regression trees and decision forest models. Predictors identified by nonlinear correlation are observed to predict with better accuracy as compared with linear correlation. The predicted indices show high correlation against the observed values. The EQUINOO prediction is provided with a high lead of 7 months with a 0.88 correlation co‐efficient (p < 0.001) and the ENSO with a lead of 1 month with a 0.87 correlation co‐efficient (p < 0.001) between the observed and predicted indices. Moreover, the proposed method proves efficient in predicting the positive or negative index values with an appropriate sign. The ENSO prediction by the proposed approach is observed to be comparable with the existing models.
Labels
- Topic
- Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Other, Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1002/met.1826 OpenAlex:W2967174540
- Added
- 2026-10-22
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