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.

Leveraging Routine Data and Local Weather Variables for Malaria Case Prediction in Tanzania: A Deep Learning Approach

Type de record:

paper
Créateur:
JanLemWil
Éditeur:
IEEE
Hôte:

Visit

doi.org

Licenses

https://doi.org/10.15223/policy-029https://doi.org/10.15223/policy-037

Similaires

Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictionsLightweight Deep Learning for Weather Prediction and Forecasting in AfricaLeveraging AI Models for Regional Weather Prediction: A Data Pipeline for AfricaMachine Learning Models for Prediction of Meteorological Variables for Weather ForecastingDeep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities dataForecasting solar power output in Ibadan: A machine learning approach leveraging weather data and system specifications

Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions

ABSTRACT Streamflow information is crucial for effectively managin

Lightweight Deep Learning for Weather Prediction and Forecasting in Africa

International audience

Weather forecasting in Africa is hampered by sparse me

Leveraging AI Models for Regional Weather Prediction: A Data Pipeline for Africa

Machine Learning Models for Prediction of Meteorological Variables for Weather Forecasting

International audience This study trained six machine learning models to predict mete

Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data

Forecasting solar power output in Ibadan: A machine learning approach leveraging weather data and system specifications

This study predicts hourly solar irradiance components, Global Horizontal Irradiance (GHI), Direct N