Predictive Model of the ENSO Phenomenon Based on Regression Trees
In one sentence. Regression trees predict ENSO phase over a 12-month window using 1950–2022 data, reporting RMSE of 0.13 for 2022; evaluation design is unclear.
Abstract
In this work, the supervised machine learning technique was applied to develop a predictive model of the phase of the El Niño-Southern Oscillation (ENSO) phenomenon. Regression trees were specifically used by means of the Scikit-Learn library of the Python programming language. Data from the period 1950-2022 were used as training and test. The performance of the predictive model was validated using three continuous type error measurement metrics: Mean Absolute Error, Maximum Error and Root Mean Square Root. The results indicate that with a greater number of training data the model improves its performance, with a tendency to decrease the error in forecasts. Which starts for the year 1953 with errors of 0.77, 1.41 and 0.75 for MAE, ME and RMSE respectively, ending for the year 2022 with errors of 0.28, 0.72 and 0.13 for the same metrics. It is concluded that, based on the results, the developed model is consistent and reliable for ENSO phase forecasts in a 12-month window.
Labels
- Topic
- Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.13164/mendel.2023.1.007 OpenAlex:W4384565492
- Added
- 2026-10-22
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