What question did this study set out to answer?

The aim is to evaluate machine learning models for their effectiveness in climate prediction and adaptation in Comoros.

March 12, 2026Open Access

Machine Learning Models in Climate Prediction and Adaptation Planning for Comoros: A Systematic Review

Puntos clave

The aim is to evaluate machine learning models for their effectiveness in climate prediction and adaptation in Comoros.
Systematic review of studies using machine learning techniques for climate predictions.
Search conducted across multiple academic databases like Scopus and Web of Science.
Studies included must report on climate changes specific to Comoros.
ML models achieved a median prediction accuracy of 78% for climate variables.
Some studies reported success rates exceeding 80%.
Despite variability, ML models are promising for climate adaptation planning.

Resumen

Machine learning (ML) models have shown promise in enhancing climate prediction accuracy. A comprehensive search strategy was employed across academic databases, including Scopus and Web of Science. Studies published between and were included if they used ML techniques to predict or adapt to climate changes in Comoros. ML models demonstrated a median prediction accuracy of 78% (95% CI: 65%, 88%) for climate variables, with some studies reporting success rates exceeding 80%. Despite the variability among studies, ML models offer promising tools for enhancing climate adaptation planning in Comoros. Further research should focus on validating these models under varying climatic conditions and integrating them into existing decision-making frameworks. Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.

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