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 =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Abena Asareña (Wed,) studied this question.