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Machine Learning Models for Climate Prediction and Adaptation in Zimbabwean Environments

Domaine:

climate

Type de record:

paper
Créateur:
OneMas
Éditeur:
Zenodo
Hôte:avatar

Zimbabwe is vulnerable to climate variability and change, necessitating robust prediction models for effective adaptation planning. A hybrid ensemble model combining Random Forest (RF) and XGBoost was employed to predict monthly rainfall. Model performance was evaluated using mean absolute error (MAE). The hybrid RF-XGBoost model achieved a MAE of 10.5 mm, indicating moderate accuracy in predicting rainfall. Machine learning models can be effectively utilised for climate prediction in Zimbabwean settings, supporting informed adaptation planning. Further research should explore the integration of socio-economic data to enhance model predictive capabilities and validate findings across different regions. machine learning, climate prediction, XGBoost, Random Forest, rainfall forecasting 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

Sub-SaharanAfricaGeospatialEnsembleMachineLearning

Licenses

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

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