Book Description

In this research study, a new probabilistic methodology for landslide hazard assessment in regional scale using Copula modeling technique is presented. In spite of the existing approaches, this methodology takes the possibility of dependence between landslide hazard components into account; and aims at creating a regional slope failure hazard map more precisely. Copula modeling technique as a widely accepted statistical approach is integrated with the hazard assessment concept to establish the dependence model between "landslide magnitude", "landslide frequency" and "landslide location" elements. This model makes us able to evaluate the conditional probability of occurrence of a landslide with a magnitude larger than an arbitrarily amount within a specific time period and at a given location. Part of the Seattle, WA area was selected to evaluate the competence of the presented method. Based on the results, the mean success rate of the presented model in predicting landslide occurrence is 90% on average; while the success rate is only 63% when these hazard elements were treated as mutually independent. Also, Seismic-induced landslides are one of threatening effects of earthquakes around the world that damage structures, utilities, and cause human loss. Therefore, predicting the areas where significant earthquake triggered hazard exists is a fundamental question that needs to be addressed by seismic hazard assessment techniques. The current methods used to assess seismic landslide hazard mostly ignore the uncertainty in the prediction of sliding displacement, or lack the use of comprehensive field observations of landslide and earthquake records. Therefore, a new probabilistic method is proposed in which the Newmark displacement index, the earthquake intensity, and the associated spatial factors are integrated into a multivariate Copula-based probabilistic function. This model is capable of predicting the sliding displacement index (Dn) that exceeds a threshold value for a specific hazard level in a regional scale. A quadrangle in Northridge area in Northern California having a large landslide database was selected as the study area. The final map indicates the sliding displacements in mapping units for the hazard level of 10% probability of exceedance in 50 years. Furthermore, to reduce human losses and damages to properties due to debris flows runout in many mountainous areas, a reliable prediction method is necessary. Since the existing runout estimation approaches require initial parameters such as volume, depth of moving mass and velocity that are involved with uncertainty and are often difficult to estimate, development of a probabilistic methodology for preliminary runout estimate is precious. Thus, we developed an empirical-statistical model that provides the runout distance prediction based on the average slope angle of the flow path. This model was developed within the corridor of the coastal bluffs along Puget Sound in Washington State. The robustness of this model was tested by applying it to 76 debris-flow events not used in its development. The obtained prediction rates of 92.2% for pre-occurred and 11.7% for non-occurred debris flow locations showed that the model results are consistent with the real debris-flow inventory database.