Machine Learning Models for Climate Prediction and Adaptation in Ghana 2009

Climate change poses significant challenges to agricultural productivity in Ghana, particularly affecting smallholder farmers who rely on climate-sensitive crops and practices. Machine learning algorithms including Random Forest and Gradient Boosting were trained on a dataset of meteorological records spanning to assess their predictive accuracy and reliability. The Random Forest model demonstrated an average prediction error rate of ±5% for rainfall, with a 95% confidence interval indicating the range within which we can be 95% confident that the true mean lies. Both models showed promise in climate prediction but were sensitive to input data quality and required further validation through real-world applications. Further research should focus on integrating more diverse datasets, including socio-economic factors, to enhance model performance and applicability in Ghana's context. Machine Learning, Climate Prediction, Random Forest, Gradient Boosting, Ghana 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.