Multi-hazard risk mapping using machine learning
This study maps out Ghana’s multi-hazard risk of flood and drought by using machine learning (ML) models for susceptibility analysis, socioeconomic survey for vulnerability analysis and population density for exposure analysis. The ML models used were Logistic Regression (LR), Random Forest (RF) and...
| Autores principales: | , , |
|---|---|
| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
CGIAR System Organization
2022
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/127621 |
Ejemplares similares: Multi-hazard risk mapping using machine learning
- Development of drought indicators Using Machine Learning Algorithm: A case study of Zambia
- Multi-hazard Early Warning for All Action Plan for Africa (2023-2027)
- QGIS plugin for fine-scale hazard, exposure, vulnerability, and risk mapping
- Urban flash flood hazard mapping using machine learning, Bahir Dar, Ethiopia
- Polycentric governance model for transformative adaptation in Morocco: Institutional mapping
- Polycentric governance model for transformative adaptation in Morocco: Transformative adaptation options