Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Machine Learning Models for Climate Prediction and Adaptation in Democratic Republic of Congo: An Empirical Approach

Domaine:

climate

Type de record:

paper
Créateur:
Nko
Éditeur:
Zenodo
Hôte:avatar

Climate change poses significant challenges to sustainable development in the Democratic Republic of Congo (DRC). Accurate climate predictions and adaptive planning are essential for mitigating these impacts. A hybrid ensemble of Random Forest and Gradient Boosting Machines (GBM) was employed. Data from meteorological stations across the country were used to train and validate models. The ensemble approach showed an improvement in prediction accuracy with a mean absolute error reduction of 15% compared to individual model predictions. Machine learning models, particularly the hybrid ensemble method, demonstrated significant potential for climate adaptation planning in DRC. Future research should explore additional data sources and model performance under varying conditions. Further studies should investigate regional variations and incorporate socio-economic factors into the predictive models to enhance their applicability. Machine Learning, Climate Prediction, Ensemble Methods, Democratic Republic of Congo 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

Geographic Terms Related to Africa: Democratic Republic of Congo Methodological and Theoretical Terms: Machine Learning Artificial Intelligence Predictive Analytics Data Mining Regression Analysis

Licenses

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

Similaires

Machine Learning Models in Climate Prediction and Adaptation Planning for Democratic Republic of CongoMachine Learning Models for Climate Prediction and Adaptation in Ethiopia: A Methodological ApproachMachine Learning Models for Climate Prediction and Adaptation in Madagascar: A Methodological ApproachMachine Learning Models for Climate Prediction and Adaptation in Botswana: A Methodological ApproachMachine Learning Models for Climate Prediction and Adaptation in Uganda: A Methodological ApproachMachine Learning Models for Climate Prediction and Adaptation Planning

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

The Democratic Republic of Congo (DRC) is vulnerable to climate variability, which impacts

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

Climate change poses significant challenges to Ethiopia's agricultural productivity and soc

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

Madagascar is a tropical island facing significant climate variability, which poses challen

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

Climate change poses significant challenges to Botswana's agricultural sector and water res

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

Uganda faces significant climate variability, impacting agriculture, water resources, and public hea

Machine Learning Models for Climate Prediction and Adaptation Planning

This article examines Machine Learning Models for Climate Prediction and Adaptation Plannin