Thesis (M.A.)--Miami University, Dept. of Geography, 2007. / Title from first page of PDF document. Includes bibliographical references (p. 67-72).

The rigid sliding-block analysis introduced by Newmark in 1965 has become a popular method for assessing the stability of slopes during earthquakes. Estimates of sliding displacement calculated using this methodology serve as an index of seismic performance and are used for mapping seismic landslide hazard potential. The original approach of rigorously integrating ground acceleration time-histories to compute estimates of sliding displacement has been replaced by the use of simple, empirical models that predict displacement as a function of a slope's yield acceleration and one or more measures of ground shaking. To be useful the results of these models must be compared with observations of landslides from previous earthquakes. Seven different empirical models were evaluated by comparing predicted displacements with an inventory of observed landslides from the 1994 Northridge, California earthquake. Using a comprehensive set of ground motion data and shear strength properties from the Northridge earthquake, sliding displacements were calculated within a geographic information system (GIS) and the accuracy of each model was computed. The influence of factors such as landslide size, geologic unit, slope angle, and material strength on the prediction of landslides was also evaluated. The results were used to show that the accuracy of the predictive models depends less on the model used and more on the uncertainty in the model parameters, specifically the assigned shear strength values. Because current approaches do not take into account the spatial variability of strength within individual geologic units, the accuracy of the predictive models is controlled by the distribution of slope angles within observed and predicted landslide cells. Assigning overly conservative (low) shear strength values results in a higher percentage of landslides accurately identified, but also results in a large over-estimation of the seismic landslide hazard. / text

Thesis (Ph. D.)--University of Alberta, 2009. / Title from pdf file main screen (viewed on June 29, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Geotechnical Engineering, Civil and Environmental Engineering Department, University of Alberta." Includes bibliographical references.

Thesis (M.S. in civil engineering)--Texas A&M University, May 2007. / Includes bibliographical references (leaves 46-47). Also available online.

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (leaves 265-276).

Landslides are one of the most destructive disasters that cause damage to both property and life every year. Various methodologies have been reported for landslide susceptibility mapping. Statistical methods are widely used to fit the mathematical relationship between observed landslides and the factors considered to influence the slope failure, and have shown remarkable accuracy. Among these models, frequency ratio and logistic regression models are the most popular for its simplicity and high accuracy. However, virtually all previous studies randomly extracted and reserved a portion of historical landslide records to perform the model evaluation. The purpose of this study are: 1) To produce a landslide susceptibility map for Lantau Island by GIS and remote sensing methods as well as statistical modeling techniques 2) To add extra value to the literature of evaluating their “prediction rate” (rather than “success rate”) for landslide susceptibility mapping in a temporal context. The mountainous terrain, heavy and prolonged rainfall, as well as dense development near steep hillsides make Hong Kong as one of the most vulnerable metropolitans to the risk of landslides. As there is an increasingly high demand for land resource to support the growth of economic and population, regional specific landslide susceptibility assessment in Hong Kong is necessary for hazard management and effective land use planning. Firstly, the spatial relationship among landslide occurrence and nine causative factors (elevation, slope aspect, slope gradient, plan curvature, profile curvature, NDVI, distance to river, SPI and lithology) were explored. The distribution of landslides on Lantau Island is largely governed by a combination of geo-environmental conditions, such as elevation of 200m-300m, slope gradient of 25°-35°, slope aspect of west or northwest, high degree of positive or negative plan curvature and profile curvature, sparse vegetation in terms of NDVI in 0.3-0.5 (shrub/grassland), proximity (0.6-1.2km) to fault line, presence of volcanic bedrocks (especially rhyolite lava and tuff) and high stream power index. Second, landslide susceptibility maps were generated by frequency ratio and logistic regression model, respectively. Validations of the mapping results were performed by calculating relative operating characteristics (ROC). The models, trained by 1,864 (70%) landslides records in the Enhanced Natural Terrain Landslide Inventory (ENTLI) from 2000 to 2008, were validated by subsequent 799 (30%) landslide occurred from 2008 to 2009. The validation result shows that logistic regression model (88.70%) possesses a better prediction power than frequency ratio model (78.00%) for the study area. The findings suggested that logistic regression analysis is more reliable for landslide susceptibility mapping. The resultant maps are expected to provide a scientific assessment of the risk areas with respect to landslides on Lantau Island, and to serve as a basis for decisions or justification of the Lantau development planning. Keywords: landslide susceptibility; frequency ratio; logistic regression; temporal verification; GIS; Hong Kong

LiDAR (Light Detection and Ranging) elevation data were collected in the Panther Creek Watershed, Yamhill County, Oregon in September and December, 2007, March, 2009 and March, 2010. LiDAR derived images from the March, 2009 dataset were used to map pre-historic, historic, and active landslides. Each mapped landslide was characterized as to type of movement, head scarp height, slope, failure depth, relative age, and direction. A total of 153 landslides were mapped and 81% were field checked in the study area. The majority of the landslide deposits (127 landslides) appear to have had movement in the past 150 years. Failures occur on slopes with a mean estimated pre-failure slope of 27° ± 8°. Depth to failure surfaces for shallow-seated landslides ranged from 0.75 m to 4.3 m, with an average of 2.9 m ± 0.8 m, and depth to failure surfaces for deep-seated landslides ranged from 5 m to 75m, with an average of 18 m ± 14 m. Earth flows are the most common slope process with 110 failures, comprising nearly three quarters (71%) of all mapped deposits. Elevation changes from two of the successive LiDAR data sets (December, 2007 and March, 2009) were examined to locate active landslides that occurred between the collections of the LiDAR imagery. The LiDAR-derived DEMs were subtracted from each other resulting in a differential dataset to examine changes in ground elevation. Areas with significant elevation changes were identified as potentially active landslides. Twenty-six landslides are considered active based upon differential LiDAR and field observations. Different models are used to estimate landslide susceptibility based upon landslide failure depth. Shallow-seated landslides are defined in this study as having a failure depth equal to less than 4.6 m (15 ft). Results of the shallow-seated susceptibility map show that the high susceptibility zone covers 35% and the moderate susceptibility zone covers 49% of the study area. Due to the high number of deep-seated landslides (58 landslides), a deep-seated susceptibility map was also created. Results of the deep-seated susceptibility map show that the high susceptibility zone covers 38% of the study area and the moderate susceptibility zone covers 43%. The results of this study include a detailed landslide inventory including pre-historic, historic, and active landslides and a set of susceptibility maps identifying areas of potential future landslides.

Thesis (MSc)--Stellenbosch University, 2013. / Landslide susceptibility maps are important for development planning and disaster management. The current synthesis of landslide susceptibility maps largely applies GIS and remote sensing techniques. One of the most critical stages on landslide susceptibility mapping is the selection of landslide causative factors and weighting of the selected causative factors, in accordance to their influence to slope instability. GIS is ideal when deriving static factors i.e. slope and aspect and most importantly in the synthesis of landslide susceptibility maps. The integration of landslide causative thematic maps requires the selection of the weighting method; in order to weight the causative thematic maps in accordance to their influence to slope instability. Landslide susceptibility mapping is based on the assumption that future landslides will occur under similar circumstances as historic landslides. The weight of evidence method is ideal for landslide susceptibility mapping, as it calculates the weights of the causative thematic maps using known landslides points. This method was applied in an area within the Western Cape province of South Africa, the area is known to be highly susceptible to landslide occurrences. A prediction rate of 80.37% was achieved. The map combination approach was also applied and achieved a prediction rate of 50.98%. Satellite remote sensing techniques can be used to derive the thematic information needed to synthesize landslide susceptibility maps and to monitor the variable parameters influencing landslide susceptibility. Satellite remote sensing techniques can contribute to landslide investigation at three distinct phases namely: (1) detection and classification of landslides (2) monitoring landslide movement and identification of conditions leading up to an event (3) analysis and prediction of slope failures. Various sources of remote sensing data can contribute to these phases. Although the detection and classification of landslides through the remote sensing techniques is important to define landslide controlling parameters, the ideal is to use remote sensing data for monitoring of areas susceptible to landslide occurrence in an effort to provide an early warning. In this regard, optical remote sensing data was used successfully to monitor the variable conditions (vegetation health and productivity) that make an area susceptible to landslide occurrence.

Earthquake triggered landslides cause loss of life, destroy structures, roads, powerlines, and pipelines and therefore they have a direct impact on the social and economic life of the hazard region. The damage and fatalities directly related to strong ground shaking and fault rupture are sometimes exceeded by the damage and fatalities caused by earthquake triggered landslides. Even though future earthquakes can hardly be predicted, the identification of areas that are highly susceptible to landslide hazards is possible. For geographical information systems (GIS) based deterministic slope stability and earthquake-induced landslide analysis, the grid-cell approach has been commonly used in conjunction with the relatively simple infinite slope model. The infinite slope model together with Newmark's displacement analysis has been widely used to create seismic landslide susceptibility maps. The infinite slope model gives reliable results in the case of surficial landslides with depth-length ratios smaller than 0.1. On the other hand, the infinite slope model cannot satisfactorily analyze deep-seated coherent landslides. In reality, coherent landslides are common and these types of landslides are a major cause of property damage and fatalities. In the case of coherent landslides, two- or three-dimensional models are required to accurately analyze both static and dynamic performance of slopes. These models are rarely used in GIS-based landslide hazard zonation because they are numerically expensive compared to one dimensional infinite slope models. Building metamodels based on data obtained from computer experiments and using computationally inexpensive predictions based on these metamodels has been widely used in several engineering applications. With these soft computing methods, design variables are carefully chosen using a design of experiments (DOE) methodology to cover a predetermined range of values and computer experiments are performed at these chosen points. The design variables and the responses from the computer simulations are then combined to construct functional relationships (metamodels) between the inputs and the outputs. In this study, Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are used to predict the static and seismic responses of slopes. In order to integrate the soft computing methods with GIS for coherent landslide hazard analysis, an automatic slope profile delineation method from Digital Elevation Models is developed. The integrated framework is evaluated using a case study of the 1989 Loma Prieta, CA earthquake (Mw = 6.9). A seismic landslide hazard analysis is also performed for the same region for a future scenario earthquake (Mw = 7.03) on the San Andreas Fault.

Landslide hazard assessment of densely forested, remote, and difficult to access areas can be rapidly accomplished with airborne light detection and ranging (lidar) data. An evaluation of geomorphic change by lidar-derived digital elevation models (DEMs) coupled with geotechnical soils analysis, aerial photographs, ground measurements, precipitation data, and numerical modeling can provide valuable insight to the reactivation process of unstable landslides. A landslide was selected based on previous work by Mickleson (2011) and Burns et al. (2010) that identified the Madrone Landslide with significant volumetric changes. This study expands on previous work though an evaluation of the timing and causation of slope failure of the Madrone Landslide. The purpose of this study was to evaluate landslide morphology, precipitation data, historical aerial photographs, ground crack measurements, geotechnical properties of soil, numerical modeling, and elevation data (with multi-temporal lidar data), to determine the conditions associated with failure of the Madrone Landslide. To evaluate the processes involved and timing of slope failure events, a deep seated potentially unstable landslide, situated near the contact of Eocene sedimentary and volcanic rocks, was selected for a detailed analysis. The Madrone Landslide (45.298383/-123.338796) is located in Yamhill County, about 12 kilometers west of Carlton, Oregon. Site elevation ranges from 206 meters (m) North American Vertical Datum (NAVD-88) near the head scarp to 152 m at the toe. The landslide is composed of two parts, an upper more recent rotational slump landslide and a lower much older earth flow landslide. The upper slide has an area of 2,700 m2 with a head scarp of 5-7 m and a volume of 15,700 m3. The lower earth flow has an area of 2300 m2, a head scarp of 15 m, and a volume of 287,500 m3. Analysis of aerial photographs indicates the lower slide probably originated between 1956 and 1963. The landslide is located at a geologic unit contact of Eocene deep marine sedimentary rock and intrusive volcanic rock. The landslide was instrumented with 20 crack monitors established across ground cracks and measured periodically. Field measurements did not detect ground crack displacement over a 15 month period. Soil samples indicate the soil is an MH soil with a unit weight of 12 kN/m3 and residual friction angle of 28φ'r which were both used as input for slope stability modeling. Differential DEMs from lidar data were calculated to generate a DEM of Difference (DoD) raster to identify and quantify elevation changes. Historical aerial photograph review, differential lidar analysis, and precipitation data suggest the upper portion of the landslide failed as a result of the December 2007 storm.