★We usethe Compound Topographic Index (CTI) to represent moisture content of the area.
★At this time the measured displacement showed a sharp up slope movement followed by a steady but increasing down slope movement
★We also use the p-values (defined as the probability of finding a test statistic value as great as the observed test statistic value, assuming that the null hypothesis is true) in order to assess the significance of each regression coefficient….We reject the null hypothesis if the p-value is less than the significance value (α)we choose; here,we use α=0.001, corresponding to a 99% confidence level. Therefore if p<α,we reject the null hypothesis, and thereby assume that the regression coefficient is not equal to zero, and equals the computed value.
★In order to apply this approach to a global data set,we use multiple landslide inventories to calibrate the model. Using the model formula previously determined (using the Wenchuan earthquake data),we use the four datasets discussed in Section 1.3.1 in our global database to determine the coefficients for the global model.
★The resulting database is used to build a predicative model of the probability of landslide occurrence.
★Substantial effort has been invested to understand where seismically induced landslides may occur in the future, as they are a costly and frequently fatal threat in mountainous regions。
★Performance of the regression modelis assessed using statistical goodness-of-fit metrics and a qualitative review to determine which combination of the proxies provides both the optimum predication of landslide-affected areas and minimizes the false alarms in non-landslide zones.
★This paper reviews these factors, covering the characteristics, types and magnitudes, environmental impacts, and remediation of mine tailings dam failures.
★This conceptual model allowed the deformation of elements within the slope to be kept to a minimum.
★Those numerical studies mentioned above successfully validated the usage of supplemental means for the full scale tests and also contributed to develop and optimize new type of rockfall barrier system effectively.
★ The slope, however,was observed to remain largely saturated for most of the year with a phreatic surface near or at the surface.
★We begin modeling by assessing qualitative relationships within the data, moving forward by using logistic regression as a statistical method for establishing a functional form between the predictor variables and the outcomes (Figure 3).We iterate over combinations of predictor variables and outcomes within the model, focusing first on one training event (Wenchuan, China), with the ultimate goal of expanding the analysis to global landslide datasets.
★Median, minimum, and maximum slope values calculated from Shuttle Radar Topography Mission (SRTM) elevation data by Verdian et al. (2007) are used in tests of the model.
★If we define 20% probability of a landslide to be the threshold, any probability equal to or greater than 20% will then be defined as a landslide prediction.
★The RS unit is suitable for testing both fully-softened shear strength and residual shear strength parametersthat can be used for slope stability assessments of various scenarios.
★Approximately 5% of all earthquake-related fatalities are caused by seismically induced landslides, in some cases causing a majority of non-shaking deaths.
★Unsaturated residual shear strength can also be used as a macroscopic indicator of the nature of micro-structural changes experienced by the soils when subjected to drying.
★These data were originally calculated for the purpose of mechanical landslide modeling, and are used here as a statistical constraint on landslide susceptibility.