African Pharmaceutical Regulatory Affairs | 14 February 2009
Machine Learning Models in Climate Prediction and Adaptation Planning for Democratic Republic of Congo
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Abstract
The Democratic Republic of Congo (DRC) is vulnerable to climate variability, which impacts agriculture, water resources, and health systems. A hybrid ensemble model combining Random Forest and Gradient Boosting Machines (GBM) was employed to predict temperature anomalies with an uncertainty of ±2°C over a spatial scale of 10 km². The models demonstrated a predictive accuracy of 85% in simulating historical climate data, with a confidence interval indicating the reliability of model predictions. This study provides robust machine learning models for climate prediction in DRC, contributing to more effective adaptation planning and policy-making. Adaptation strategies should be developed based on these climate predictions to mitigate risks associated with climate change. Model estimation used $\hat{\theta}=argmin<em>{\theta}\sum</em>i\ell(y<em>i,f</em>\theta(x<em>i))+\lambda\lVert\theta\rVert</em>2^2$, with performance evaluated using out-of-sample error.