Machine Learning Models for Climate Prediction and Adaptation in Madagascar: A Methodological Approach

Madagascar is a tropical island facing significant climate variability, which poses challenges for sustainable development. We employed Random Forest regression models with cross-validation techniques to predict precipitation patterns over the next decade. Uncertainty was assessed using bootstrapping methods. Random Forest models predicted precipitation changes with an average absolute error of 15% and a confidence interval for predictions at ±20%, indicating moderate predictive accuracy. The machine learning models provide a robust framework for climate prediction in Madagascar, complementing existing data-driven approaches. Further research should focus on integrating socioeconomic factors into the model to enhance its applicability and effectiveness. Machine Learning, Climate Prediction, Adaptation Planning, Random Forest, Cross-Validation Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.