Machine Learning and Sensitivity Analysis Approach to Quantify Uncertainty in Landslide Susceptibility Mapping (English)

Mitigating the impacts of landslides requires quantifying the susceptibility of different infrastructures to this hazard through landslide susceptibility mapping. The mapping requires overlaying the spatial effects of multiple factors that contribute to the occurrence of landslide events (rainfall, land cover, distance to roads, lithology, and slope) and this process requires assigning weights to the different factors contributing to landslides. This...

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Author

Basheer,Mohammed Adam Abbaker;

Oommen,Thomas;

Takamatsu,Masatsugu;

Suzuki,Sachi;
Document Date
2022/12/14
Document Type
Policy Research Working Paper
Report Number
WPS10264
Volume No
1
Country
South Asia,
Region
South Asia,
Disclosure Date
2022/12/14
Disclosure Status
Disclosed
Doc Name
Machine Learning and Sensitivity Analysis Approach to Quantify Uncertainty in Landslide Susceptibility Mapping

Language

English

Rel. Proj ID

8S-Building Resilience to Landslide and Geo-hazard risk in the South Asia Region -- P160578

Major Sector

FY17 - Public Administration

Theme

FY17 - Rural Infrastructure and service delivery,FY17 - Climate change,FY17 - Rural Development,FY17 - Flood and Drought Risk Management,FY17 - Adaptation,FY17 - Disaster Risk Management

Sector

FY17 - Other Public Administration

TF No/Name

TF0A2897-Building Resilience to Landslide and Geohazard risk in the South Asia R

Topics

Disaster Risk Assessment,Disaster Risk Management,Geographic Information Systems,Landslide Risk Reduction,Road Asset Management,

Unit Owning

SAR- Climate Change and DRM (SSACD)

Version Type

Final

Series Name

Policy Research working paper ; no. WPS 10264;

Digital Object Identifier

10.1596/1813-9450-10264

Product Line

Advisory Services & Analytics

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Citation

Basheer,Mohammed Adam Abbaker; Oommen,Thomas; Takamatsu,Masatsugu; Suzuki,Sachi.

Machine Learning and Sensitivity Analysis Approach to Quantify Uncertainty in Landslide Susceptibility Mapping (English). Policy Research working paper ; no. WPS 10264 Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/099356212142224352

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Machine Learning and Sensitivity Analysis Approach to Quantify Uncertainty in Landslide Susceptibility Mapping (English)

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