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This paper presents an evaluation of two bivariate statistical approaches, frequency ratio (FR) and weights-of-evidence (WoE) for landslide susceptibility mapping (LSM) at south-western part of Saudi Arabia, Jizan region. Landslide locations were identified and mapped from interpretation of high resolution satellite images (GeoEye 0.5 m and QuikeBird 0.6m), topographic maps (scale of 1:10,000), historical records, and extensive field surveys. A total of 106 landslide locations were mapped using Arc-GIS software and divided into two groups, 75% and 25% of landslide locations were used for training and validation of models, respectively. Eleven landslide conditioning factors such as elevation, slope, curvature, aspect, lithology, topographic wetness index (TWI), normalized difference vegetation index (NDVI), proximity to lineament, roads and rivers were considered in this evaluation. The effects of these factors on landslide occurrence were assessed using aforementioned bivariate statistical approaches. For validation, the models results were compared with landslide locations which were not used during the models building. Subsequently, the receiver operating characteristic (ROC) curves were established and area under the curves (AUC) was calculated for the landslide susceptibility maps using the success (training data) and prediction (validation data) rates. The results showed that the area under the curve for success rates are 0.861 (86.1%) and 0.839 (83.9%) and for prediction rates are 0.796 (79.6%) and 0.791 (79.1%), respectively for frequency ratio and weight-of-evidence models. The resulting landslide susceptibility maps showed five classes of susceptibility such as very high, high, moderate, low, and very low. The percentage of existing training and validating landslides data in high and very high zones of the susceptibility maps were calculated to be 90.02% and 76.03% for frequency ratio model and 88.33% and 79.3% for weight-of-evidence model, respectively. The results revealed that the frequency ratio and weights-of-evidence models produced reasonable accuracy. The resultant maps would be useful and can also help planners for general choosing favorable locations for development schemes, such as infrastructural, buildings, road constructions, and environmental protection. [ABSTRACT FROM AUTHOR]

The rheology of marine sediment-laden flows in a submarine environment is examined. The viscosity and yield stress of marine sediments are key parameters controlling landslide mobility: the former is related to the peak velocity and the latter is related to the final deposition of debris flow. One of the simplest rheological models is the ideal plastic Bingham model, which is often applied to diverse materials. For marine sediments, the flow behavior can be approximated by modified Bingham models, such as the bi-viscosity and Papanastasiou models. The flow of viscoplastic materials can then be described by unyielded (local shear stress τ is lower than a yield stress τ), yield surface ( τ = τ), and post-yield ( τ > τ) characteristics. It is concluded that modified Bingham models with a proper choice of flow parameters can be used to predict the unyielded, yielding and yielded zones for marine sediments. It is also demonstrated that in some cases the yield surface can be effectively obtained using the relationship between viscosity and shear rate. [ABSTRACT FROM AUTHOR]