Machine Learning Models for Climate Prediction and Adaptation in Moroccan Settings

Morocco is a country facing significant climate variability, which poses challenges to its agricultural productivity and socio-economic stability. Machine learning techniques were employed using historical climate data from Morocco. The Random Forest algorithm was selected due to its robust performance in handling complex datasets. The model showed a prediction accuracy of approximately 78% with an uncertainty interval indicating that the actual performance could range between 65-90%. This suggests a promising approach for climate forecasting and adaptation planning. Machine learning models have demonstrated potential in enhancing climate predictions, which can inform more effective adaptation strategies in Morocco. The findings suggest integrating these machine learning models into existing climate change adaptation frameworks to improve decision-making processes. 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.