Keywords
franciscan complex, gis, hillslope stability, landslides, landslide susceptibility, logistic regression, neural network, modeling, redwood national and state parks, california, sinmap
Start Date
1-7-2006 12:00 AM
Abstract
Shallow landslides or slope failures have been studied from several points of view. In particular, numerous methods have been developed to assess slope stability. However, little work has been done on the systematic comparison of different techniques, and the incorporation of vertical contrasts of geotechnical properties in multiple soil layers. In this research, stability 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 a tool to assess landslide susceptibility. The system is fully operational for models handling an enhanced cartographic-hydrologic model (SINMAP) and logistic regression. The enhanced implementation of SINMAP was tested and found to have improved factor of safety estimates base on comparison with landslide inventory maps. The enhanced SINMAP and logistic regression subsystems have functions that allow the user to include vertical variation in geotechnical properties through summation of forces in specific layers, acting on failure planes on a local or regional scale. The working group of LOGISNET foresees the development of an integrated tool system to handle and support the prognostic studies of slope instability, and to communicate the results to the public through maps.
LOGISNET: A TOOL FOR MULTIMETHOD, MULTILAYER SLOPE STABILITY ANALYSIS
Shallow landslides or slope failures have been studied from several points of view. In particular, numerous methods have been developed to assess slope stability. However, little work has been done on the systematic comparison of different techniques, and the incorporation of vertical contrasts of geotechnical properties in multiple soil layers. In this research, stability 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 a tool to assess landslide susceptibility. The system is fully operational for models handling an enhanced cartographic-hydrologic model (SINMAP) and logistic regression. The enhanced implementation of SINMAP was tested and found to have improved factor of safety estimates base on comparison with landslide inventory maps. The enhanced SINMAP and logistic regression subsystems have functions that allow the user to include vertical variation in geotechnical properties through summation of forces in specific layers, acting on failure planes on a local or regional scale. The working group of LOGISNET foresees the development of an integrated tool system to handle and support the prognostic studies of slope instability, and to communicate the results to the public through maps.
