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Quantifying the uncertainties in the prediction of landslide displacement is important for making reliable predictions and for managing landslide risk. This study develops a novel approach for the interval prediction (i.e. uncertainty) of landslide with step-like displacement pattern in the Three Gorges Reservoir (TGR) area using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTEENN) based Random Forest (RF) and bootstrap-Multilayer Perceptron (MLPs). DBSCAN was employed to carry out clustering analysis for different deformation states of the landslide with step-like displacement pattern. The SMOTEENN based RF classifier was trained to deal with imbalanced classification problems. A dynamic switching prediction scheme to construct high-quality Prediction Intervals (PIs) using bootstrap-MLPs was established. The concepts of Pareto front and Knee point were adopted to select the PIs that could provide the best compromise between reliability and accuracy. The proposed DBSCAN-RF-bootstrap-MLP method is illustrated and verified with one typical landslide with step-like displacement pattern, the Bazimen landslide from the TGR area in China. The method showed to perform well and provides the uncertainties associated with landslide displacement prediction for decision making. [ABSTRACT FROM AUTHOR]

Human beings are, in general, risk-averse and willing to go to great lengths to reduce failure consequences. However, if the underlying issues are not understood, effective action cannot properly be taken. A landslide hazard assessment framework capable of estimating regional probabilities of slope failure can be used to aid a vast number of communities currently living in landslide 'danger zones'. Such a framework would provide a tool with which community resources can be optimized and ensure that appropriate preparedness and mitigation strategies are in place. Maximum slope angles, as estimated using digital elevation models (DEMs), are one of the most important indicators for landslide hazard assessment. This paper uses local averaging theory to determine how the resolution of DEMs affects regional landslide probability estimates. Emphasis is on a regional landslide hazard assessment, measured by the probability that one or more slopes of at least a critical minimum scale will fail within the region. Key words: regional landslide hazard assessment, digital elevation models (DEM), slope failure probability, slope stability, random finite element method (RFEM). Les humains tentent generalement d'eviter les risques et sont prets a prendre les actions necessaires pour reduire les consequences des ruptures. Cependant, si les causes ne sont pas comprises, des actions efficaces ne peuvent pas etre appliquees. Un cadre d'evaluation du risque de glissement de terrain capable d'estimer les probabilites regionales d'une rupture de pente peut etre utile a un grand nombre de communautes qui vivent presentement dans des << zones de danger >> de glissement de terrain. Un tel cadre permettra d'offrir un outil avec lequel les ressources des communautes pourront etre optimisees et qui s'assurera d'une prevention adequate et que les strategies d'attenuation sont en place. Les angles maximaux des pentes, tels qu'estimes a l'aide de modeles digitaux d'elevation (MDE), sont parmi les indicateurs les plus importants pour l'evaluation des risques de glissement de terrain. Cet article se base sur la theorie des moyennes locales pour determiner comment la resolution des MDE affecte les estimations des probabilites de glissement de terrain a l'echelle regionale. L'emphase est placee sur une evaluation du risque regional de glissement de terrain, mesure par la probabilite qu'une ou plusieurs pentes d'au moins une echelle critique minimum cedent dans la region. [Traduit par la Redaction] Mots-cles : evaluation regionale du risque de glissement de terrain, modele digital d'elevation (MDE), probabilite de rupture de pente, stabilite de pente, methode par elements finis aleatoire (RFEM)., Introduction Landslides are a common and devastating type of natural disaster, often causing loss of life and irreparable damage. Given the unstable nature of the natural environment, particularly after humans [...]

In some cut-slope projects landslide is a common problem during construction due to unfavorable geomorphological and geomechanical conditions. It is necessary to do a quantitative assessment of the risk posed by landslide before determining the budget or tender price. This paper outlines a general procedure for doing this, followed by an example to demonstrate the approach in comparison to a known failure. Finite-element analyses identify the most dangerous landslide scenario among all construction steps. The slope failure probability is then estimated using reliability theory based on the most dangerous construction step. After identifying the potential failure surface and estimating the volume of the sliding mass, the runnout behavior of sliding mass is simulated to delimit the extent of likely impacted area. Then, the exposed elements at risk and their vulnerabilities are identified and analyzed. The landslide risk is assessed quantitatively for three types of consequences: casualties, economic loss, and time overrun. Compared with actual consequences, the estimation results were in acceptable agreement with the case study. The paper demonstrates that it is feasible to analyze the risk associated with landslides during construction of cut-slopes. DOI: 10.1061/(ASCE)GT.1943-5606.0000381 CE Database subject headings: Quantitative analysis: Assessment; Risk management; Landslides; Construction management. Author keywords: Quantitative analysis; Assessment; Risk management; Landslides; Construction.

The discusser quotes the mean value of HT ht when assessing method performance. • If a normalized stress term is defined as HT ht , then the ratio of the post-equalization term and the installation term is HT ht . 3 and 5 to HT ht values up to 142. [Extracted from the article]