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Multi-hazard risk mapping using machine learning
| Content Provider | Consultative Group on International Agricultural Research (CGIAR) |
|---|---|
| Author | Adounkpe, Peniel Ghosh, Surajit Amarnath, Giriraj |
| Spatial Coverage | Ghana [GH] |
| Description | 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 Support Vector Machine (SVM) with inputs of location and features of natural hazards. Topographic, precipitation, temperature, hydrology, land cover and soil cover raster images were also used in these models. The value of the Area Under the Curve (AUC) of Receiver Operating Characteristic Curve (ROC) was above 0.80 for all models except the LR model for drought classification. The best performing model was RF, with an AUC of 0.84 and 0.82 for flood and drought classification. |
| Sponsorship | CGIAR Trust Fund |
| Related Links | https://cgspace.cgiar.org/items/3d2dafe8-f11f-48d3-9547-7c045efb4efe |
| File Format | |
| Language | English |
| Publisher | CGIAR System Organization |
| Publisher Place | Colombo, Sri Lanka |
| Access Restriction | Open |
| Rights License | CC-BY-NC-ND-4.0 |
| Subject Keyword | Drought Flood Agriculture Climate Change Food Systems Climate Change Adaptation Livelihoods Modeling Policy Resilience Water |
| Content Type | Text |
| Resource Type | Report |
| Subject | Agronomy and Crop Science Food Science Plant Science |
