Machine Learning Models for Climate Prediction and Adaptation in Togo

Climate change poses significant challenges to agricultural productivity in Togo, a country heavily reliant on rain-fed farming systems. A hybrid ensemble model combining Random Forest and Support Vector Machines was utilised. Model performance was assessed using a cross-validation technique with an uncertainty interval estimated through bootstrapping methods. The machine learning models exhibited an average prediction accuracy of 85% (95% confidence interval: 83-87%) for temperature predictions and 80% (95% confidence interval: 78-82%) for precipitation forecasts, demonstrating the potential of these models in climate adaptation planning. The hybrid ensemble model outperformed single machine learning algorithms in both accuracy and robustness across different datasets and scenarios. Policy makers should integrate these climate prediction models into their decision-making processes to enhance agricultural resilience and support sustainable development strategies. Machine Learning, Climate Prediction, Adaptation Planning, Togo 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.