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Multi-hazard risk mapping using machine learning

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Title Multi-hazard risk mapping using machine learning
 
Creator Adounkpe, Peniel
Ghosh, Surajit
Amarnath, Giriraj
 
Subject drought
flood
agriculture
climate change
food systems
 
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.
 
Date 2022-10-20
2023-01-19T19:12:56Z
2023-01-19T19:12:56Z
 
Type Report
 
Identifier Adounkpe P, Ghosh S, Amarnath G. 2022. Multi-hazard Risk Mapping with Machine Learning. CGIAR Climate Resilience Initiative.
https://hdl.handle.net/10568/127621
 
Language en
 
Relation https://hdl.handle.net/10568/121965
 
Rights CC-BY-NC-ND-4.0
Open Access
 
Format 23 p.
application/pdf
 
Publisher CGIAR