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

Uganda faces significant climate variability, impacting agriculture, water resources, and public health. Employed ensemble machine learning techniques including Random Forest and Gradient Boosting Machines (GBM). Modelled climate data showed an average prediction accuracy of 85% with GBM, indicating a reliable method for future projections. The models provide valuable insights into climate change impacts on agriculture in Uganda. Implement these models to enhance agricultural resilience and water management strategies. Machine Learning, Climate Prediction, Adaptation Planning, Uganda 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.