Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy
Computers & Geosciences - Special issue on computers, geoscience and geocomputation
Discovering Statistics Using SPSS for Windows: Advanced Techniques for Beginners
Discovering Statistics Using SPSS for Windows: Advanced Techniques for Beginners
A macroscopic collisional model for debris-flows simulation
Environmental Modelling & Software
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Shallow landslides and slope failures have been studied from several points of view (inventory, heuristic, statistic, and deterministic). 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 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 Multiple Logistic Regression, Geographic Information System, and Neural Network. The main purpose of LOGISNET 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 multiple logistic regression. The enhanced implementation of SINMAP was tested at regional scale in the Highway 101 corridor in Del Norte County, California, and its susceptibility map was found to have improved factor of safety estimates based on comparison with landslide inventory maps. The enhanced SINMAP and multiple logistic regression subsystems have functions that allow the user to include vertical variation in geotechnical properties through summation of forces in specific soil layers acting on failure planes for a local or regional-scale mapping. The working group of LOGISNET foresees the development of an integrated tool system to handle and support the prognostic studies of slope instability, and communicate the results to the public through maps.