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approach of rigorously integrating ground acceleration time-histories to compute estimates of sliding displacement has been replaced by the use of simple, empirical models that predict displacement as a function of a slope's yield acceleration and one or more measures of ground shaking.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

The Saint Lawrence Lowlands in eastern Canada contain extensive deposits of marine soils deposited in post-glacial seas during and following the retreat of the most recent continental glacier. These marine soils include silt and clay deposits known collectively as Champlain clay. When the pore fluid in these marine deposits has changed over time to a lower salinity, the clay can become very sensitive, or demonstrate substantial strength loss after reaching the peak strength with sufficient strain under undrained load conditions. Sensitive clay soils are subject to a peculiar type of very large landslide that typically involves great extents of nearly horizontal ground, usually occurring suddenly and without warning. These landslides tend to be described as “retrogressive” in the literature and practice, implying that they develop as a series of successive small failures that advance rearward until a final stable position is reached. The work of this thesis is organized into four different themes, with an overall objective of understanding the hazard and risk associated with large landslides in sensitive clay to linear infrastructure such as railways. The first theme, documented in Chapter 2, develops a number of spatial relationships between specific physiographic and geologic features and landslide occurrence or absence, as determined through air photo analysis and a review of the literature. The second theme, documented in Chapter 3, presents the construction of a digital database of large landslides in sensitive clay in eastern Canada, for the purposes of studying landslide susceptibility, hazard and risk. The third theme, documented in Chapters 4 and 5, presents and defends a novel mechanical model for development of these large landslides. This model suggests the landslides develop progressively, rather than retrogressively, and the science of fracture mechanics is employed to substantiate the model. The fourth theme, documented in Chapters 6 and 7, synthesizes the findings of the earlier themes and presents a methodology for estimating landslide susceptibility in Champlain clay. That approach is then extended to develop an understanding of the hazard. The concluding chapter extends that work to present an initial appreciation of landslide risk to railways. / Thesis (Ph.D, Geological Sciences & Geological Engineering) -- Queen's University, 2009-04-23 13:22:19.53

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).

This thesis examines the indirect impact of natural hazards on infrastructure networks. It addresses several key themes and issues for hazard assessment, network modelling and risk assessment using the case study of landslides impacting the national road network in Scotland, United Kingdom. The research follows four distinct stages. First, a landslide susceptibility model is developed using a database of landslide occurrences, spatial data sets and logistic regression. The model outputs indicate the terrain characteristics that are associated with increased landslide potential, including critical slope angles and south westerly aspects associated with increased rates of solar irradiance and precipitation. The results identify the hillslopes and road segments that are most prone to disruption by landslides and these indicate that 40 % (1,700 / 4,300 km) of Scotland s motorways and arterial roads (i.e. strategic road network) are susceptible to landslides and this is above previous assessments. Second, a novel user-equilibrium traffic model is developed using UK Census origin-destination tables. The traffic model calculates the additional travel time and cost (i.e. indirect impacts) caused by network disruptions due to landslide events. The model is applied to calculate the impact of historic scenarios and for sets of plausible landslide events generated using the landslide susceptibility model. Impact assessments for historic scenarios are 29 to 83 % greater than previous, including £1.2 million of indirect impacts over 15 days of disruption at the A83 Rest and Be Thankful landslide October 2007. The model results indicate that the average impact of landslides is £64 k per day of disruption, and up to £130 k per day on the most critical road segments in Scotland. In addition to identifying critical road segments with both high impact and high susceptibility to landslides, the study indicates that the impact of landslides is concentrated away from urban centres to the central and north-west regions of Scotland that are heavily reliant on road and haulage-based industries such as seasonal tourism, agriculture and craft distilling. The third research element is the development of landslide initiation thresholds using weather radar data. The thresholds classify the rainfall conditions that are most commonly associated with landslide occurrence in Scotland, improving knowledge of the physical initiation processes and their likelihood. The thresholds are developed using a novel optimal-point threshold selection technique, high resolution radar and new rain variables that provide spatio-temporally normalised thresholds. The thresholds highlight the role of the 12-day antecedent hydrological condition of soils as a precursory factor in controlling the rain conditions that trigger landslides. The new results also support the observation that landslides occur more frequently in the UK during the early autumn and winter seasons when sequences or clustering of multiple cyclonic-storm systems is common in periods lasting 5 to 15 days. Fourth, the three previous elements are combined to evaluate the landslide hazard of the strategic road segments and a prototype risk assessment model is produced - a catastrophe model. The catastrophe model calculates the annual average loss and aggregated exceedance probability of losses due to the indirect impact of landslides in Scotland. Beyond application to cost-benefit analyses for landslide mitigation efforts, the catastrophe model framework is applicable to the study of other natural hazards (e.g. flooding), combinations of hazards, and other infrastructure networks.

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.