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Landslide failure time prediction is considered a challenging issue in landslide research. A significant goal of landslide research is to provide scientific and accurate prediction methods. In this paper, the short-term forecasting of landslides (STFL) model is proposed by analyzing landslide deformation characteristics in a known evolution process. The landslides on the southern slope of the West open pit mine (SSWOPM) in Liaoning province, northeast China, and Huangci in Gansu province, northwest China, were selected as the study cases. After pre-processing, which include eliminating abnormal data (t test), adding missing data (cubic spline interpolation), and smoothing noisy data (Savitzky–Golay filters), the displacement data are assembled to serve as the model's input parameters. The parameters of STFL can be obtained through the Levenberg–Marquardt (LM) algorithm. Using the forecasting criterion, the time of failure of landslides can be predicted and determined. Forecasting results provide evidence that the STFL model can achieve an accurate prediction of landslide displacement (correlation coefficient R2 > 0.99). The forecasting results of the SSWOPM and Huangci landslides indicate that the forecasting failure time of the STFL model is March 10, 2014 and January 31, 1995, respectively, which are near the actual failure time compared with those obtained using the Verhulst model. This finding indicates that the new method has excellent reliability and accuracy in landslide prediction and a robust description of the landslide movement. [ABSTRACT FROM AUTHOR]

In a strong earthquake, not only a large number of coseismic landslides are triggered, but relaxation and cracks in the rocks and soils are induced, which make these rocks and soils vulnerable to instability during subsequent rainfall; thus, strong earthquakes always have long-term effects on landslides. The geo-hazards along the Zhaotong-Qiaojia road in the 2014 Ludian earthquake (Ms 6.5) of Yunnan Province, China, were investigated, and the Ganjiazhai giant landslide was chosen as a case study. First, using the limit equilibrium analysis and Newmark method, the critical seismic intensity of the landslide before the earthquake was evaluated. Secondly, the dynamic failure process of the landslide under the measured ground motion was simulated with the discontinuous deformation analysis method. Lastly, based on stress–seepage coupling analysis and precipitation data from Ludian meteorological station, the stability of the landslide during subsequent rainfall after the earthquake was predicted. The results show that the critical seismic intensity was within degrees VIII–IX, which is consistent with the results of the earthquake damage investigation. The dynamic failure process can be divided into four stages, and four scarps formed; the potential sliding zones during the subsequent rainfall were at the first scarp and the fourth scarp, and their critical rainfall amounts were 35–40 mm and 55–60 mm, respectively. In this paper, failure process simulation and stability prediction of the landslide before, during, and after the strong earthquake are presented, which provide analysis methods for the dynamic stability of landslides in the meizoseismal area. [ABSTRACT FROM AUTHOR]

The analysis of landslides using monitoring data is a commonly used method for landslide prediction and early warning. However, the loss of data due to breakdown of the monitoring equipment or interference of external factors is unavoidable in the process of monitoring landslide data. An interpolation algorithm can supplement and correct the data to solve the problem of data loss. This multi-position and long-term monitoring data is non-linear, multidimensional and time-varying, which makes it difficult for the commonly used spatiotemporal kriging interpolation methods to construct an appropriate model straightaway. This paper presents a non-uniform spatiotemporal kriging interpolation method. It breaks through the restriction of Euclidean distance in the spatial dimension while breaking away from linear relationship in the temporal dimension. The spatiotemporal deformation field model is constructed using spatiotemporal optimal weights combination and subsequently optimized by particle swarm optimization algorithm. The ordinary kriging interpolation is extended to the non-uniform spatiotemporal kriging interpolation under the spatiotemporal constraints condition. This method is successfully applied to the interpolation of the monitoring data of landslide displacement. It provides better data for studies of landslide disasters and is of great practical significance for prevention and prediction of landslide disasters. [ABSTRACT FROM AUTHOR]

Landslide risk analysis is a common geotechnical evaluation and it aims to protect life and infrastructure. In the case of sensitive clay zones, landslides can affect large areas and are difficult to predict. Here we propose a methodology to determine the landslide hazard across a large territory, and we apply our approach to the Saint-Jean-Vianney area, Quebec, Canada. The initial step consists of creating a 3D model of the surficial deposits of the target area. After creating a chart of the material electrical resistivity adapted for eastern Canada, we applied electric induction to interpret the regional soil. We transposed parameter values obtained from the laboratory to a larger scale, that is to a regional slope using the results of a back analysis undertaken earlier, on a smaller slide within the same area. The regional 3D model of deposits is then used to develop a zonation map of slopes that are at risk and their respective constraint areas with the study region. This approach allowed us to target specific areas where a more precise stability analysis would be required. Our methodology offers an effective tool for stability analysis in territories characterized by the presence of sensitive clays. [ABSTRACT FROM AUTHOR]

Landslide vulnerability prediction maps are among the most important tools for managing natural hazards associated with slope stability in river basins that affect ecosystems, properties, infrastructure and society. Landslide events are among the most hazardous patterns of slope instability in the coastal mountains of Syria. Thus, the main goals of this research are to evaluate the performance of three different statistical outputs: Frequency Ratio (FR), Statistical Index (SI) and Index of Entropy (IoE) and therefore map landslide susceptibility in the coastal region of Syria. To this end, we identified a total of 446 locations of landslide events, based on the preliminary inventory map derived from fieldwork and high-resolution imagery surveys. In this regard, 13 geo-environmental factors that have a high influence on landslides were selected for landslide susceptibility mapping. The results indicated that the FR method outperformed the SI and IoE models with a high AUC of 0.824 and better adaptability, followed by the SI with 0.791. According to the SCAI values, although the FR model achieved the best reliability, the other two models also showed good capability in determining landslide susceptibility. The result of FR-based modelling showed that 18.51 and 19.98% of the study area fall under the high and very high landslide susceptible categories, respectively. In the map generated by the SI method, about 36% of the study area is classified as having high or very high landslide sensitivity. In the IoE method, whereas 14.18 and 25.62% of the study area were classified as "very high susceptible" and "high susceptible," respectively. The relative importance analysis demonstrated that the slope aspects, lithology and proximity to roads effectively motivated the acceleration of slope material instability and were the most influential in both the FR and SI models. On the other hand, the IoE model indicated that the proximity to faults and roads, along with the lithology factor, were important influences in the formation of landslide events. As a result, the statistical bivariate models-based landslide mapping provided a reliable and systematic approach to guide the long-term strategic planning procedures in the study area. [ABSTRACT FROM AUTHOR]

Landslides are the most destructive geological hazard in the hilly regions. For systematic landslide mitigation and management, landslide evaluation and hazard zonation is required. Over the past few decades several techniques have been developed that can be used for landslide evaluation and zonation. These techniques can broadly be classified into qualitative and quantitative approaches. Qualitative approaches include geomorphological analysis and heuristic techniques whereas quantitative approaches include statistical, artificial intelligence and deterministic techniques. In quantitative techniques prediction for landslide susceptibility is based on the actual realistic data and interpretations. Further, the quantitative techniques also overcome the subjectivity of qualitative approaches. Each of these techniques may consider different causative factors and utilizes various means for factor evaluation and analysis. When compared, each of these techniques has its own advantage and disadvantage over other techniques. The selection of appropriate technique for landslide hazard evaluation and zonation is very crucial. The factors that need to be considered to adopt an appropriate approach are; investigation purpose, the extent of the area to be covered, the type of mapping units, the scale of map to be produced, type of data to be used, type of landslides, availability of resources, capability and skill set of an evaluator and the accessibility to the study area. The main aim of this article is to present a comprehensive review on various techniques and approaches available for landslide susceptibility and hazard zonation mapping. Further, attempt is also made to assess the effectiveness of these techniques in landslide hazard zonation studies. [ABSTRACT FROM AUTHOR]

On 16 April 2016, a Mw 7.0 earthquake occurred in Kumamoto city, Japan. The main shock induced two large landslides, namely the Aso Bridge landslide and the Aso Volcanological Laboratory landslide. Their topographical and geological conditions and motion features were investigated by using an unmanned aerial vehicle (UAV) and portable dynamic cone penetration tests (PPTs). The Aso Bridge landslide lies between elevations of 385 m and 725 m, with a total estimated volume of about 1,980,000 m3. The main body is composed of cohesive soil with lapilli and block. The Aso Volcanological Laboratory landslide lies on a slope between 483 m to 582 m, and the total volume is about 81,000 m3, with an average thickness of 4.5 m. The main body is composed of Kusasenrigahama volcanic pumice tephra beds. The material compositions and deposits of both landslides have low cohesion and easily induced shear failure for the two landslides. The sliding distance of the Aso Bridge landslide was long, the sliding direction almost unchanged from the scarp to the toe, and the sliding speed was rapid. The sliding distance of the Aso Volcanological Laboratory landslide, however, was short, the sliding direction changed from the N-direction at the scarp to the NW-direction at the toe, and the sliding speed was slow. [ABSTRACT FROM AUTHOR]

Landslides are a common type of mass movement that occur in large numbers and have significant effects on topography. In Turkey, landslides rank second after earthquakes among natural disasters that cause widespread loss of life and property. This study investigates the monitoring of the Taşkent Landslide (Konya, Turkey), which has considerably affected the local topography. The landslide in Taşkent had an adverse effect on transportation to nearby towns and villages. Initial studies were conducted using the global navigation satellite system (GNSS) measurement scale; these studies are currently on-going. Since 2011, studies using terrestrial laser scanning (TLS), which is one of the latest ground-positioning technologies, have also been launched. This article presents the results of the TLS and GNSS approaches used to monitor, characterise and model an active landslide. It includes the comparison and analysis of a point cloud, three-dimensional model, profile and change in volume mass acquired in different time periods in one landslide area. The point cloud data, obtained from two different time periods using the TLS technique, and supported by the results of GNSS analysis, were analysed via different methods. The TLS revealed that up to 4 m of material was displaced at the site of the landslide, and it revealed detailed information about how the landslide occurred. The volume of the mass that moved as a result of the landslide was determined, and movements that occurred in the geological structure were monitored. These studies showed that the TLS point cloud method can be effectively used in the monitoring of landslides that occur in areas that are large, difficult to reach, rugged or mountainous. [ABSTRACT FROM AUTHOR]