What question did this study set out to answer?

The research aims to enhance climate prediction capabilities in South Africa using machine learning models.

March 12, 2026Open Access

Machine Learning Models for Climate Prediction and Adaptation in South Africa

Puntos clave

The research aims to enhance climate prediction capabilities in South Africa using machine learning models.
Developed a hybrid ensemble model combining Random Forest and Support Vector Machines (SVM).
Trained the model on historical climate data from the South African Weather Service.
Evaluated model performance through out-of-sample error metrics.
Achieved an accuracy rate of 78% in predicting temperature anomalies.
Demonstrated robust reliability based on confidence interval assessments.
Suggested integration of models into government planning frameworks for maximum impact.

Resumen

Climate change poses significant challenges to South Africa's agricultural productivity and infrastructure resilience. A hybrid ensemble of Random Forest and Support Vector Machines (SVM) was trained on historical climate data from the South African Weather Service. The ensemble model achieved an accuracy rate of 78% in predicting temperature anomalies across different regions, with a confidence interval indicating robust reliability. Machine learning models can significantly enhance climate prediction capabilities for South Africa's adaptation strategies. Adopted models should be integrated into government planning frameworks and shared among stakeholders to maximise impact. Climate Prediction, Machine Learning, Ensemble Models, South Africa Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.

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