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- KurzbeschreibungShallow landslides or slope failures have been <br>studied from several points of view. In particular, <br>numerous methods embedded in Geographic Information <br>Systems (GIS) applications have been developed to <br>assess slope stability. However, little work has <br>been done on the systematic comparison of different <br>techniques to outline advantages and limitations, <br>due to the difficulties of assessing a model in both <br>natural and theoretical conditions. The lack of <br>study of the systematic comparison of landslide <br>models at different cartographic scales, DEM <br>resolutions, and sampling strategies not only <br>compromises the reliability of the models, but also <br>leads to the misuse of the models. In this research, <br>assessment of landslide susceptibility models is <br>modeled by using LOGISNET, an acronym for Logistic <br>Regression, Geographic Information System, and <br>Neural Network. LOGISNET is a project of which the <br>main purpose is to provide government planners and <br>decision makers with a tool and guidance to assess <br>landslide susceptibility models. The system is fully <br>operational for models employing a <br>cartographic/hydrologic approach and multiple <br>logistic regression.
- AutorGabriel Legorreta Paulin
- Seiten360 Seiten
- Gewicht488 g
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