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Machine Learning Models in Climate Prediction and Adaptation Planning for Democratic Republic of Congo

Domaine:

climate

Type de record:

paper
Créateur:
NkoMukMuhBis
Éditeur:
Zenodo
Hôte:avatar

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 $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.

Visit

doi.org

Tags

African GeographyEnsemble MethodsRandom ForestGradient BoostingClimate ModellingMachine LearningPredictive Analytics

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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