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.

Данное издание не является оригинальным. Книга печатается по технологии принт-он-деманд после получения заказа.