Machine Learning Models in Climate Prediction and Adaptation Planning within Namibia: A Systematic Review

Machine learning (ML) models have shown potential in enhancing climate prediction and adaptation planning across various regions. The review employs a comprehensive search strategy through multiple databases, including academic journals, conference proceedings, and grey literature. Studies are critically assessed based on predefined inclusion criteria. ML models have demonstrated significant potential for improving climate predictions with some achieving an accuracy rate of up to 85% in short-term forecasts, while others show promise in medium- to long-term predictions. The review concludes that ML can be a valuable tool for enhancing climate adaptation strategies, though further research is needed to address model robustness and data availability issues. Future studies should focus on expanding the geographical scope of the models, validating findings with real-world applications, and exploring integration with existing climate monitoring systems. 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.