Logisnet: A tool for multimethod, multiple soil layers slope stability analysis
Introduction
Triggered by extrinsic factors (such as earthquakes or intense rainfall) and intrinsic factors (such as geology, slope, vegetation, and geotechnical parameters), landslides can cause significant damage and potentially generate destructive debris flows (Ohlmacher and Davis, 2003; Dai and Lee, 2002; Atkinson and Massari, 1998). In spite of the efforts made by local authorities and scientists to monitor and forecast landslides, it has been difficult to accurately evaluate landslide potential or susceptibility due to large spatial and temporal variability. Several inventory, heuristic, statistical, and deterministic approaches to assess landslide susceptibility have been proposed (Castellanos Abella and Van Westen, 2008; Demoulin and Chung, 2007; Metternicht et al., 2005; Lee et al., 2004; Zhou et al., 2003; Bozzano et al., 2002; Qin et al., 2002a, Qin et al., 2002b; Guzzetti et al., 1999; Parise, 2001; Maceo-Giovanni et al., 2000; Lang et al., 1999; González-Díez et al., 1999; Di Gregorio et al., 1999; Miles and Ho, 1999; Pasuto and Soldati, 1999; Dehn and Buma, 1999; Atkinson and Massari, 1998; Jäger and Wieczorek, 1994) and their automatization and application are embedded in landslide GIS programs (Claessens et al., 2007; D’Ambrosio et al., 2007; Bursik et al., 2007; Xie et al., 2006; Lee et al., 2004; Zhou et al., 2003; Hammond et al., 1992; Pack et al., 1998; Montgomery and Dietrich, 1994); however, few studies take full account of topographic control through shallow subsurface water flow in landslide generation (Pack et al., 2001; Montgomery and Dietrich, 1994). Based on a hydrologic model developed by O’Loughlin (1986), Montgomery and Dietrich (1994) developed a model that combines topographic and hydrographic variables to predict potential landslide zones with sparse information. Using a similar approach, Pack et al. (1998) developed a cartographic/hydrologic model (Stability Index Mapping: SINMAP) in which poorly constrained parameters are incorporated through the use of uniform probability distributions. However, little work has been done to clarify the importance of geotechnical properties of material in different soil layers that produce landslides (Chigira et al., 2003; Chigira, 2001; Simon et al., 2000; Voight and Elsworth, 1997). Evaluation of landslides with different soil layers was done by Simon et al. (2000). Simon et al. (2000) developed a 2-D streambank failure model that allows for a slip plane crossing multiple soil layers with differing friction angle and cohesion. The model worked well in post-diction of failure events along Goodwin Creek, MS, USA.
Also, in spite of notable contributions to evaluate landslide models (Haneberg, 2005; Chinnayakanahalli et al., 2003; Borga et al., 2002; Morrissey et al., 2001; Guzzetti et al., 1999), the systematic comparison of models is lacking to outline advantages and limitations of the methods due to the difficulties of assessing a model in natural conditions at different cartographic scales, DEM resolutions, and sampling strategies.
As an attempt to develop a 3-D model allowing for soil layering, SINMAP, Multiple Logistic Regression (MLR), and Neural Network (NN) approaches are being modified to accept geotechnical parameters for multiple soil layers in order to assess landslide potential. A proposed computer system uses Geographic Information System (GIS) resources. The system, called LOGISNET is developed by using Arc Macro Language (AML) under ArcInfo GIS software. LOGISNET is used to host, compare, and visualize results after using the three approaches. This paper describes development, implementation, and testing of the first two approaches (SINMAP and MLR) as methods for delineating landslide potential. Pilot tests are performed using LOGISNET as a GIS application in geohazard for an area on Highway 101 in the northern Coast Ranges of California.
Section snippets
Background and physical basis
Two approaches for landslide susceptibility are used: (1) SINMAP, which expresses the stability of the slope in terms of a factor of safety, a ratio between the forces that prevent the slope from failing and those that make the slope fail and (2) MLR, which has the advantage over other multivariate statistical techniques for this application in that the dependent variable can have only two values—an event occurring or not occurring (Ohlmacher and Davis, 2003). The SINMAP and MLR approaches are
Pilot tests
The study area is near the Highway 101 corridor in Del Norte County, California (Fig. 5). In the area, landslides along the coast between Wilson Creek and Crescent City create a potentially hazardous situation for people and property. More than 200 landslides have been mapped in this area by the California Geological Survey and the California Department of Transportation. The study area is prone to landslides due to the combination of several factors, such as high precipitation (2583.94 mm/y),
Results
A comparison of outputs by using SINMAP with default system parameters, SINMAP with field data parameters, LOGISNET multiple soil layers, and MLR is done. Fig. 6 shows the results at 30 m pixel resolution. At this resolution, SINMAP, LOGISNET multiple soil layers, and MLR approaches appear to reflect topographic conditions more than landslide geomorphology. It seems the models just detect steep terrain rather than the landslide process.
The three SINMAP outputs (Fig. 6b–d) show high instability
Conclusions
We introduce the implementation of LOGISNET as a user interface and collection of functions in AML designed to facilitate the analysis and validation of cartographic–hydrologic (SINMAP) and MLR landslide models with multiple soil layers data. The set of functions provides a toolbox that allows evaluation and calculation for prediction of landslides. The tools and methods to assess the model quality at different cartographic scales and DEM resolutions are still under development and depend on
Acknowledgments
The authors wish to express their deepest appreciation to authorities from the Redwood National and State Parks, California for their approval and help in conducting the field sampling for this research. The inventory and elevation data used in this study was drawn from Dave Best (GIS Coordinator, Redwood National Park) and from C. J. Wills (Department Of Conservation California Geological Survey). This work benefited greatly from the comments and help of Vicky Ozaki, Brian R. Merrill, Andrew
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