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

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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INFORMATION

Auteur

Basheer,Mohammed Adam Abbaker;

Oommen,Thomas;

Takamatsu,Masatsugu;

Suzuki,Sachi;
Date du document
2022/12/14
Type de document
Document de travail de recherche sur les politiques
Numéro du rapport
WPS10264
Volume
1
Pays
Asie du Sud,
Région
Asie du Sud,
Date de publication
2022/12/14
Disclosure Status
Disclosed
Titre du rapport
Machine Learning and Sensitivity Analysis Approach to Quantify Uncertainty in Landslide Susceptibility Mapping

Langue

anglais

Numéro d'identification

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

Secteur principal

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

Secteur

FY17 - Other Public Administration

Numéro/nom du fonds fiduciaire

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

Thèmes

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

Unité propriétaire

SAR- Climate Change and DRM (SSACD)

Version Type

Final

Series Name

Policy Research working paper ; no. WPS 10264;