Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report
The current report presents a machine learning model developed to predict malaria prevalence based on rainfall patterns, specifically tailored to different regions within Senegal. The developed model takes into account the varying climate conditions across regions to provide a more localized and acc...
| Autores principales: | , , , , |
|---|---|
| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
International Water Management Institute (IWMI)
2023
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/139414 |
Ejemplares similares: Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report
- Climate variability and extremes impact on seasonal occurrence patterns of malaria cases in Senegal [Abstract only]
- Applications of satellite-based rainfall estimates in flood inundation modeling: a case study in Mundeni Aru River Basin, Sri Lanka
- Leveraging crop yield forecasts using satellite information for early warning in Senegal
- Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning
- Satellite-surface-area machine-learning models for reservoir storage estimation: regime-sensitive evaluation and operational deployment at Loskop Dam, South Africa
- Evaluating malaria risk projection under climate change scenarios in Senegal