ASSESSMENT OF LANDSLIDES SUSCEPTIBILITY

Shallow landslides or slope failures have been
studied from several points of view. In particular,
numerous methods embedded in Geographic Information
Systems (GIS) applications have been developed to
assess slope stability. However, little work has
been done on the systematic comparison of different
techniques to outline advantages and limitations,
due to the difficulties of assessing a model in both
natural and theoretical conditions. The lack of
study of the systematic comparison of landslide
models at different cartographic scales, DEM
resolutions, and sampling strategies not only
compromises the reliability of the models, but also
leads to the misuse of the models. In this research,
assessment of landslide susceptibility models is
modeled by using LOGISNET, an acronym for Logistic
Regression, Geographic Information System, and
Neural Network. LOGISNET is a project of which the
main purpose is to provide government planners and
decision makers with a tool and guidance to assess
landslide susceptibility models. The system is fully
operational for models employing a
cartographic/hydrologic approach and multiple
logistic regression.

Autorentext

Gabriel Legorreta Paulín: PhD: studied Geology at The State
University of New York at Buffalo, 2007.

Marcus I. Bursik: PhD: studied Geology at California Institute
of Technology, 1989. Professor, Department of geology,
University at Buffalo. The State University of New York.

Klappentext

Shallow landslides or slope failures have been
studied from several points of view. In particular,
numerous methods embedded in Geographic Information
Systems (GIS) applications have been developed to
assess slope stability. However, little work has
been done on the systematic comparison of different
techniques to outline advantages and limitations,
due to the difficulties of assessing a model in both
natural and theoretical conditions. The lack of
study of the systematic comparison of landslide
models at different cartographic scales, DEM
resolutions, and sampling strategies not only
compromises the reliability of the models, but also
leads to the misuse of the models. In this research,
assessment of landslide susceptibility models is
modeled by using LOGISNET, an acronym for Logistic
Regression, Geographic Information System, and
Neural Network. LOGISNET is a project of which the
main purpose is to provide government planners and
decision makers with a tool and guidance to assess
landslide susceptibility models. The system is fully
operational for models employing a
cartographic/hydrologic approach and multiple
logistic regression.