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This study investigates the effect of the basic unit of this spatial modelling work (pixel resolution) on the accuracy of the modelling outcome, and reports on the effectiveness of using See5 pruned decision trees to model the landslide susceptibility of the Sydney Basin.
In this model, the landslide is treated as a flexible block that translates and rotates over a user-defined, three-dimensional rupture surface.
To model the landslide and obtain its rheological parameters, a back analysis of the event was performed by means of a depth-averaged 3D numerical code called DAN3D.
Using the logistic regression model, the landslide occurrence probability was computed, and if values are closer to one, landslides are more likely occur.
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At ground level it is difficult to delimit the landslide complex but using satellite imagery and digital elevation models the landslide location and volume can be estimated.
We performed AHP-MCS to model the error propagation from the input data to the model output (the landslide susceptibility surface) according to the following steps: (I) Generating a random uniformly distributed dataset using a random function as training data for calculating the uncertainty analysis.
An engineering geology model of the landslide was devised according to an equivalent continuum approach to evaluate both stiffness and strength of the rock mass within the slope.
Figure 17 Model of the landslide triggered by a debris flow (Sassa et al.[1997]).[1997]
Since then, statistical method has become an alternative to model the coseismic landslide assessment, partly because of its flexibility of input data determination.
For the numerical modelling of the landslide, carried out with DAN-3D code, the best results were obtained by using a Voellmy reological model, with a constant turbulence parameter of 250 m/s2 and a friction parameter comprised between 0.15 and 0.19.
When sediment supply is depleted in the model, the shallow landsliding module shuts off until new material is available for transport.
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