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 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.
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| Format: | Report biblioteca |
| Language: | English |
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CGIAR System Organization
2022-10-20
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| Subjects: | drought, flood, agriculture, climate change, food systems, |
| Online Access: | https://hdl.handle.net/10568/127621 |
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dig-cgspace-10568-1276212023-12-08T19:36:04Z Multi-hazard risk mapping using machine learning Adounkpe, Peniel Ghosh, Surajit Amarnath, Giriraj drought flood agriculture climate change food systems 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. 2022-10-20 2023-01-19T19:12:56Z 2023-01-19T19:12:56Z Report Adounkpe P, Ghosh S, Amarnath G. 2022. Multi-hazard Risk Mapping with Machine Learning. CGIAR Climate Resilience Initiative. https://hdl.handle.net/10568/127621 en https://hdl.handle.net/10568/121965 CC-BY-NC-ND-4.0 Open Access 23 p. application/pdf CGIAR System Organization |
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Europa del Oeste |
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Biblioteca del CGIAR |
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English |
| topic |
drought flood agriculture climate change food systems drought flood agriculture climate change food systems |
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drought flood agriculture climate change food systems drought flood agriculture climate change food systems Adounkpe, Peniel Ghosh, Surajit Amarnath, Giriraj Multi-hazard risk mapping using machine learning |
| 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. |
| format |
Report |
| topic_facet |
drought flood agriculture climate change food systems |
| author |
Adounkpe, Peniel Ghosh, Surajit Amarnath, Giriraj |
| author_facet |
Adounkpe, Peniel Ghosh, Surajit Amarnath, Giriraj |
| author_sort |
Adounkpe, Peniel |
| title |
Multi-hazard risk mapping using machine learning |
| title_short |
Multi-hazard risk mapping using machine learning |
| title_full |
Multi-hazard risk mapping using machine learning |
| title_fullStr |
Multi-hazard risk mapping using machine learning |
| title_full_unstemmed |
Multi-hazard risk mapping using machine learning |
| title_sort |
multi-hazard risk mapping using machine learning |
| publisher |
CGIAR System Organization |
| publishDate |
2022-10-20 |
| url |
https://hdl.handle.net/10568/127621 |
| work_keys_str_mv |
AT adounkpepeniel multihazardriskmappingusingmachinelearning AT ghoshsurajit multihazardriskmappingusingmachinelearning AT amarnathgiriraj multihazardriskmappingusingmachinelearning |
| _version_ |
1787228888714182656 |