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Small-scale landslides that occur at unexpected locations and seasons can cause severe losses of life and property. Unusually, the Shidang landslide occurred prior to the rainy season in Jinkouhe County, Sichuan Province, China, resulting in 19 deaths and missing persons. In this paper, landslide characteristics and causes are revealed through a field investigation, laboratory experiments, and numerical calculations. The results show that the Shidang landslide was a small-scale landslide with a total volume of approximately 19500 m3, originating from hollow colluvial deposits. The geomorphological hollow above the landslide provided topographic conditions for the collection of subsurface runoff. Groundwater flow was transported downward along dolomite and accumulated in the area above shale, with a low conductivity. Groundwater table rise combined with the decrease in the soil strength led to landslide occurrence. Improper siting of residential areas and insufficient vigilance against post-rainfall landslides caused this tragedy. Considering the post-disaster lessons, it is strongly recommended that in the future, more attention should be given to the investigation of landslides triggered by groundwater, especially in determining landslide hydrological system boundaries. In mountainous areas where the developable space is limited, areas with hollow landforms are potentially at risk of geohazards. This paper could provide a reference for the prevention and management of similar landslides and new insights for updating landslide investigation systems and residential housing plans. [ABSTRACT FROM AUTHOR]

The reactivation of ancient landslides, a persistent issue that hinders urban development and threatens human safety, is the central focus of this paper. Specifically, this paper offers a detailed description and analysis of an ancient landslide that reactivated on February 26, 2021, in Guoye town, Zhouqu County, Gansu Province, China, and has remained slow-moving since then. With a volume of approximately 21.2 million m3 of rock and soil masses involved, this landslide poses a significant risk to the safety of 1119 individuals from 282 households in Moli village and Guoye village. A combination of field investigations, remote sensing interpretation, in situ monitoring, and interferometric synthetic aperture radar (InSAR) was used to investigate the landslide characteristics and reactivation mechanisms. The results revealed that the presence of sliding-prone rock formations and tectonic movement were the primary contributors to the formation of the Moli landslide. Furthermore, the accumulation of bedrock blocks in the quarry and collapsed rock masses caused by fault activity at the rear parts, along with the additional load from buildings on the middle and rear sections, increased the sliding force. Simultaneously, the erosion of the Dashuiba River resulted in a reduction in the anti-slip force, thereby directly facilitating landslide reactivation. The notable increase in annual rainfall in recent years has played a crucial role in ancient landslide reactivation by elevating the groundwater level and softening the shear strength of the sliding zone soil. The findings of this study contribute to an enhanced understanding of the reactivation mechanisms in giant, ancient landslides situated within areas characterized by highly complex geological conditions. [ABSTRACT FROM AUTHOR]

Typhoons are recurring meteorological phenomena in the southeastern coastal area of China, frequently triggering debris flows and other forms of slope failures that result in significant economic damage and loss of life in densely populated and economically active regions. Accurate prediction of typhoon-triggered debris flows and identification of high-risk zones are imperative for effective risk management. Surprisingly, little attention has been devoted to the construction of physical vulnerability curves in typhoon-affected areas, as a basis for risk assessment. To address this deficiency, this paper presents a quantitative method for developing physical vulnerability curves for buildings by modeling debris flow intensity and building damage characteristics. In this study, we selected the Wangzhuangwu watershed, in Zhejiang Province of China, which was impacted by a debris flow induced by Typhoon Lekima on August 10, 2019. We conducted detailed field surveys after interpreting remote sensing imagery to analyze the geological features and the mechanism of the debris flow and constructed a comprehensive database of building damage characteristics. To model the 2019 debris flow initiation, entrainment, and deposition processes, we applied the Soil Conservation Service-Curve Number (SCS-CN) approach and a two-dimensional debris flow model (FLO-2D). The reconstructed debris flow depth and extent were validated using observed debris flow data. We generated physical vulnerability curves for different types of building structures, taking into account both the degree of building damage and the modeled debris flow intensity, including flow depth and impact pressure. Based on calibrated rheological parameters, we modeled the potential intensity of future debris flows while considering various recurrence frequencies of triggering rainfall events. Subsequently, we calculated the vulnerability index and economic risk associated with buildings for different frequencies of debris flow events, employing diverse vulnerability functions that factored in uncertainty in both intensity indicators and building structures. We observed that the vulnerability function utilizing impact pressure as the intensity indicator tends to be more conservative than the one employing flow depth as a parameter. This comprehensive approach efficiently generated physical vulnerability curves and a debris flow risk map, providing valuable insights for effective disaster prevention in areas prone to debris flows. [ABSTRACT FROM AUTHOR]

This paper aims to provide a framework for simulating the formation process of melt-driven debris flow for use in the risk assessment of hazard. According to field research, the formation process of melt-driven debris flows consists of three stages including snow melting, soil infiltration, and runoff/debris flow propagation. From this point of view, an integrated model of melt-driven debris flows is developed, coupling three corresponding physical models through some common variables while considering the effects of temperature variations and entrainment. For the evaluation of the erosion rate of loose materials while considering the presence of fine particles, a relationship between critical shear stress and surface fine-particle proportion is introduced. This integrated model is employed to retrospectively analyze the formation process of the 2010 melt-driven debris flows in the Tianmo catchment, China. The numerical results show that the melt-driven debris flow formation process is effectively described by reproducing the behaviors at each stage based on the proposed model. Furthermore, the hourly temperature variation and sediment characteristics (e.g., coarse particle size and initial soil moisture content) are discussed in detail as factors affecting runoff and entrainment. [ABSTRACT FROM AUTHOR]

Machine learning models have been widely used in landslide susceptibility prediction. However, landslide multidimensional feature extraction, model generalization ability, and prediction quantification of deep learning are still challenging. This paper proposes a new approach, the landslide density-based bidirectional long short-term memory (LD-BiLSTM) model with multichannel input and an optimized sampling strategy to predict and map landslide susceptibility in active seismic mountainous areas of Sichuan Province, China. First, to ensure the generalization ability of the LD-BiLSTM model, other regions in Sichuan were selected as the model training area independent of the prediction area (Luding County). Multichannel landslide datasets were constructed to extract high-dimensional geospatial features of landslides. Subsequently, the landslide density of each grid cell was utilized as the label for the corresponding input sample. The LD-BiLSTM model was improved by using transfer learning and sampling optimization strategies, which makes our method attenuate the impact of historical landslide inventory deviation on the spatial susceptibility mode compared with the existing DL model, which usually uses landslide objects (LO) as input sample labels. Model performance evaluation results show that the LD-BiLSTM model (precision = 0.903, recall = 0.899, F1-score = 0.901, Area under receiver operating characteristic curve (AUC) = 0.940) outperformed the LO-BiLSTM model (precision = 0.812, recall = 0.815. F1-score = 0.813, AUC = 0.910) in the case areas. Meanwhile, the performance of the LD-BiLSTM model (AUC = 0.9407) significantly outperformed both the information value (IV) (AUC = 0.7207) model and the random forest (RF) (AUC = 0.8116) models in the landslide prediction area (Luding), which confirms that the proposed LD-based method is superior to traditional LO-based methods. Significantly, our approach can effectively extract the spatial distribution of landslides and predict potential landslides in complex high-mountain environments. [ABSTRACT FROM AUTHOR]

On June 17, 2020, Aniangzhai landslide, an ancient landslide located in Danba County, southwest China, was reactivated by Meilonggou debris flow. The front edge of the slope collapsed, mobilizing a soil mass of about 2.35 × 106 m3. Evaluating the stability of the whole slope is of great importance to avoid further landslides and mitigate the damage for Aniangzhai villagers living on this slope. This paper focuses on the inaccessible upper slope of Aniangzhai landslide (no attention paid before) that exhibits a relative elevation difference of more than 1000 m. Multi-source remote sensing, including unmanned aerial vehicle (UAV) photogrammetry, light detection and ranging (LiDAR), and satellite-based interferometric synthetic aperture radar (InSAR) techniques, was used in this research to identify and evaluate this high mountain upper slope potential hazard in Aniangzhai landslide. Considering the huge height difference and the steep slope of Aniangzhai landslide, an iterative route planning method was proposed and adopted to obtain a 3D model with 0.02 m resolution and a DEM with 0.25 m resolution by using UAV and LiDAR close-in flight method, respectively. Meter-level huge cracks were clearly identified by the high-resolution UAV 3D model and LiDAR data, which confirm that the location of these cracks is related to the morphological structure of this ancient landslide. Time series InSAR analysis reveals the activity of this high-altitude area, with a maximum LOS displacement rate of 15 cm/a. The combination of the above remote sensing technologies confirms and reveals the high potential risk and the reactivated condition of the upper slope of Aniangzhai landslide. Through this finding, we show that the evolution of Aniangzhai landslide happened through four stages with a cascading effect. This paper proves the usefulness of an integrated method to successfully identify and evaluate the high-altitude upper slope potential hazard and compares the technical features of them, providing a reference for future works that aimed at mitigating the potential damage of the upper slope. [ABSTRACT FROM AUTHOR]

Slow-moving landslide-prone areas (SLAs) are unstable objects on the terrestrial surface that can collapse rapidly when provoked by earthquakes, leading to infrastructure damage. It is critical to identify SLAs prior to earthquake events and assess their hazard-causing probabilities when triggered. An assessment approach of earthquake-triggered geohazards is proposed in this paper by combining interferometric synthetic aperture radar (InSAR) derived SLAs and geological and geomorphological factors. Taking the Ms6.8 Luding earthquake, which occurred in the Sichuan Province of southwestern China on September 5, 2022, as an example, 1320 scenes of Sentinel-1 SAR data in western Sichuan were processed using the small baseline subset (SBAS) InSAR technique before the earthquake. After the earthquake, hazard probability assessment was performed in real-time by filtering the SLAs using a spatial analysis technique with geological and geomorphological factors, e.g., real-time peak ground acceleration (PGA), slope, distance to fault (DTF), and distance to the river (DTR) data. The results show that 11 SLAs were classified into high-risk zones. As verified by the Luding co-seismic landslide dataset from visual interpretation of optical remote sensing images, 142 coseismic landslides were triggered by the earthquake in these high-risk regions. In these areas, an ancient landslide, with high pre-earthquake displacement rates (−50 mm/year) on the scarp was reactivated under the Luding earthquake forces. This method can provide a scientific tool for disaster mitigation and rapid response emergency management. [ABSTRACT FROM AUTHOR]

Landslides frequently disrupt roadway networks in mountainous regions worldwide. Because of the relatively long roadway extension and low roadway density in mountainous regions, the occurrence of a landslide hazard along a local road segment will cause traffic paralysis on the individual roadway and will further impact regional roadway network accessibility. This paper establishes an integrative risk assessment framework based on risk theory and complex network theory to combine the results of landslide susceptibility mapping along roadways and impact assessment on the roadway network. Through an analysis of the relationship between various geo-environmental conditioning factors and historical landslides along roadways, the support vector machine (SVM) model is used to assess landslide susceptibility across the regional roadway network. Both topological connectivity of the entire roadway network and transport accessibility between local residents are considered in the impact assessment on roadway networks. To illustrate the effectiveness of the proposed risk assessment framework, a case study of the roadway network in Fengjie County, China, which is prone to landslide occurrence, is conducted. The resulting landslide risk heatmap of Fengjie County's roadway network is generated using ArcGIS software. The most critical road segments are identified as being highly susceptible to landslides, and if they are disrupted, the entire roadway network will suffer significant performance loss. The results can support adaptive strategies for landslide mitigation, preparedness, and emergency response services, as well as improve roadway plans to reduce exposure and associated consequences by adding new links to the existing roadway network. [ABSTRACT FROM AUTHOR]

Landslide failure near a reservoir generates impulse waves, greatly increasing casualties and property losses. This paper presents a field investigation and potential impulse wave simulation analysis of the Wulipo (WLP) landslide, which deformed after the initial impoundment of the Baihetan Hydropower Station, China. According to geological surveys, the WLP landslide is a weak base-type, weathered, fragmented rockslide with soft rock in the lower portion and hard rock in the upper portion. The deformation and failure mechanism under the action of reservoir water consist of compression, fracturing, and shearing. The periodic fluctuation of reservoir water may cause overall high-speed slip failure of the WLP landslide. According to the inferred failure mode of the WLP landslide, the impulse waves caused by the overall failure of the landslide at the highest (825 m) and lowest (765 m) water levels are predicted. Due to the significant height difference between the center of gravity of the landslide and the reservoir water level, the wave height and fluctuation degree of the overall failure of the WLP landslide for 765 m are significantly greater than those for 825 m. However, the wave flood area for 825 m is larger than that for 765 m. Although the impulse waves triggered by the overall landslide at either water level will not destroy the nearby Menggu community, at 765 m, impulse waves will overflow the bridge deck of Ganhu Road. Therefore, engineering measures such as reducing the landslide volume can lower the secondary disaster risk posed by WLP landslides. This study provides insights into landslide disaster risk in the Baihetan Reservoir region and similar areas worldwide. [ABSTRACT FROM AUTHOR]

Landslides are one of the main geological disasters in the riparian zone of the Three Gorges Reservoir Area (TGRA) in China, causing massive losses in terms of industrial and agricultural production and people's lives and property. Due to the difficulty of data collection and lack of facade information, the remote sensing methods currently used for landslide mapping and detection are insufficient. The current landslide information acquisition method is mainly based on visual interpretation and automatic computer classification from aerial orthophotographs, which is subjective and time-consuming and yields low accuracy. New shipborne photogrammetry has unique advantages in small and steep slope landslide detection. In recent years, the deep learning technology represented by convolutional neural networks (CNNs) exhibits more powerful feature learning ability in image recognition than traditional machine learning technology. Meanwhile, transfer learning can make the network suitable for small datasets through fine-tuning, which could transfer the parameters of the pretrained model to the new model in a certain way, thereby increasing the learning efficiency of the model. In this paper, the training of multiple deep learning models is realized through transfer learning, and automatic identification of landslides based on shipborne images is achieved through decision-level fusion. The experimental results show that through the integration of decision-level fusion and transfer learning, it performs well in the classification of landslides based on shipborne images. [ABSTRACT FROM AUTHOR]

Landslides which occur at unexpected locations and times, and which have unrecognizable risk areas, often cause mass casualties. Despite there being few preceding landslides in the same season, the catastrophic Qijiaba landslide occurred in the new government-selected settlement of Wufu village, Sichuan Province, China, on September 5, 2021, killing three people and destroying 13 houses. In this paper, the landslide features and causes are discussed based on field investigations, remote sensing interpretations, and laboratory-based soil experiments. The results showed that it was a typical soil landslide within a geomorphic hollow of the slope with a volume of 20,230 m3, recurrence interval of at least 69 years, and runout distance of 137.5 m. This type of landslide is difficult to predict. The soil strength in the sliding surface decreased suddenly as the water content increased, and the soil's rich clay mineral and high clay particle contents determined its high mobility (Heim's ratio of 0.44). The occurrence time of the Qijiaba landslide lagged 1.3 days behind the peak rainfall, and the 2-day cumulative rainfall was a crucial factor in the landslide occurrence. Finally, the large water confluence area behind the main scarp was determined to be 3.9 times the area of the landslide itself, thereby providing abundant water conditions to induce failure. The factors underlying the casualties included inappropriate site selection, insufficient awareness of potential landslides, and failure to evacuate in time. The results of this study provide a basis for future prevention, mitigation, and early warning of similar landslides. [ABSTRACT FROM AUTHOR]

Due to their practicability, simple and data-driven empirical models have been extensively developed and applied in practical engineering to predict the run-out distance of landslides. However, the definition of the most appropriate empirical model for specific landslide data is rarely discussed. Moreover, the empirical model is subjected to the high variability of landslide data, which should be quantified into the model to provide more reliable predictions. As such, we propose in this paper, a simple, practical, and probabilistic run-out distance prediction method based on an empirical model and Bayesian method. This method was implemented with a regional landslide inventory compiled from 34 loess landslides in Heifangtai Terrace, Gansu Province, China. In this method, we performed a Bayesian model selection to determine the most appropriate empirical model for the compiled database among the possible candidate models adapted from previous literature. Considering the high variability of data, unknown parameters of the empirical model are regarded as random variables, and their posterior distributions are obtained by Bayesian updating with the compiled database. Then, we developed the probabilistic run-out prediction model to evaluate the run-out distance exceedance probability of landslides based on the most appropriate model and its associated posterior random variable information. We utilized data from two recent landslides that occurred in Heifangtai Terrace to validate the performance of the proposed model. In addition, we produced a run-out distance exceedance probability curve using the proposed method for a potential landslide in Heifangtai Terrace, in which the sliding volume interval is estimated using the sloping local base level (SLBL) method. In general, this study presents a practical method for landslide run-out distance analyses within a probabilistic framework, aiming to provide support for risk-based decisions. [ABSTRACT FROM AUTHOR]

The Jinsha River flows through one of the most geologically complex regions in western China with extremely high altitudes and capricious climates. Frequent landslides (e.g., the Baige landslides on 11 October and 3 November 2018) occurred along its stretch which posed severe damage to bridges, dams, and roads, and put the safety of local residents at risk. Systematic and comprehensive landslide detection has been rarely carried out in the Jinsha River corridor because it is a wide and hard-to-reach region. With 30 years of development, InSAR has become an effective way to map ground motion, which in turn makes it feasible to detect active landslides over wide regions. However, several challenges remain in applying InSAR in the Jinsha River corridor, such as low coherence due to dense vegetation and/or large gradient surface movements, and atmospheric effects due to the spatio-temporal variations of the atmosphere (especially the part due to tropospheric water vapor). In this paper, we propose a framework to overcome the limitations of conventional InSAR through the integration of GACOS-assisted InSAR, advanced SBAS InSAR, and SAR pixel offset tracking techniques. This integrated framework enables us to detect active landslides under complex topographic and climate conditions. Our results show that the detected active landslides are largely controlled by active faults, most of them are found at elevations of 2500–4000 m, their slopes fall between 25 and 45 ∘ facing southwest and northwest, and 83.3% of the active landslides have a NDVI value of less than 0.3. Furthermore, precipitation is one critical triggering factor of active landslides, evidenced by the high temporal correlation between average precipitation and surface displacement time series. [ABSTRACT FROM AUTHOR]

As the global climate warms, the frequency of soil slope failures induced by snowmelt is gradually increasing, especially loess landslide disasters in seasonally frozen areas. Landslide disasters in seasonally frozen areas pose a serious threat to human lives and engineering constructions and are gradually drawing greater public attention around the world. However, the mechanism that regulates deformation and failure processes, resulting in snowmelt-driven landslides, remains elusive. The Yili River Valley in Xinjiang, China, is an ideal location for exploring the mechanism of snowmelt-driven landslides. Based on detailed field surveys, remote sensing image identification, meteorological data analysis and loess characteristic tests, this paper takes a representative high-level loess landslide in north-west China as a case in order to explore its evolution history, movement process and resurrection mechanism. It was found that this loess slope had suffered two large-scale sliding failures and different degrees of inherited slope deformation had been found between the two slope failures. The 32 surface cracks identified have a total length of 3,505.10 m, and the total area of the landslide was found to be 135,462m2. The average thickness of the sliding body was about 30 m, and the volume was approximately 504,000 m3. The actual movement time lasted for 32 s with an average moving speed of about 15 m/s. Human grazing activities dominate the formation and development of slope surface cracks in the early stage. The strong water sensitivity of distinctive loess controls soil strength deterioration in the slip zone. Rapid snow removal and infiltration, driven by an abnormal temperature rise in Spring, is the most important triggering factor for slope deformation, evolution and failure. As regional grazing activities increase and global warming intensifies, the potential for resurrection landslides will increase. The results provide essential information for a comprehensive understanding of early warning systems and risk assessment for snowmelt-triggered landslides in cold areas. [ABSTRACT FROM AUTHOR]

This paper presents a recent rainfall-induced reactivated compound soil slide in Yanyuan County, China, on July 19, 2018. In this paper, according to the elaborate investigation of the soil slide area at field and prefailure slope stability analysis, the corresponding failure mechanisms are comprehensively studied. Unmanned aerial vehicle (UAV) based on remote sensing, three-dimensional modelling, in situ and laboratory experimental testing, borehole exploration, and numerical simulation were carried out in this work. According to the results, two different sliding processes were identified involving the main sliding and lateral collapse. The main sliding occurred as multiple stages along with two different types of slip zones, which involve a preexisting basal zone of the ancient landslide and an interlaminar shear zone within the ancient landslide deposits. The predominance of reactivated surface geomorphology provides a clue to elucidate the dominant compound movement of the event. Unconsolidated sliding mass and the basal sliding surface are ideal for the development of the reactivation motion. Saltatory microrelief of the original slope determined the retrogressive failure type of the soil slide. The well-developed groundwater in the landslide area significantly increased the soil mass saturation, with an average saturation value of 98%, formed dominant seepage in the sliding mass, and promoted landslide reactivation. Furthermore, rainwater infiltration due to 14 days' antecedent cumulative rainfall of 350.6 mm directly contributed to the occurrence of the landslide by increasing the pore water pressure in the slope. The maximum shear plastic strain and displacement obtained by numerical simulation show that the lateral slope was unstable with the removal of the main sliding. Because the soil slide is located in the west area of the Emeishan Large Igneous Province (ELIP), it is a partial resurrection of the ancient basalt landslide. Therefore, this study has a certain significance for revealing the reactivated mechanism of basalt ancient landslide. [ABSTRACT FROM AUTHOR]

On 24 June 2017, a rockslide-debris avalanche occurred in Xinmo Village, Maoxian County, Sichuan Province, China. The displaced materials traveled approximately 2.6 km and caused 83 casualties. Combined with the field investigation, seismic signal, and numerical simulation, the dynamic process and deposit characteristics of the Xinmo landslide were analyzed. Based on the Arias intensity and Hibert time–frequency analysis of the seismic signal, the Xinmo landslide is determined to have a total duration of 120 s and with three-stage dynamic processes, namely, the initial accelerating stage (50 s), erosion impact stage (50 s), and accumulation stage (20 s). Then, taking the field investigation results, the total duration, and kinetic parameters inverted from the seismic signal as criteria, the DAN3D (Dynamic Analysis of Landslides) was employed to reconstruct the dynamic process of the Xinmo landslide. The results show that the erosion and deposit depth distributions obtained from the simulation are in agreement with the pre- and post-landslide DEM (digital elevation model) data and field results. Further, the three-stage dynamic processes (40 s, 60 s, 20 s) obtained from the numerical simulation are also close to the seismic analysis results. In this study, the results of the field investigation and seismic analysis provide a more detailed set of inversion criteria for the numerical simulation work, which contributes to reconstructing the dynamic processes of the Xinmo landslide more accurately. Based on the approach of multi-source data collaboration in this paper, those landslides that lack direct observation data can be better understood and the numerical parameters used for landslide dynamics inversion are better calibrated, which can be applied to predict the runout behavior and disaster scope of potential landslides in the immediate area. [ABSTRACT FROM AUTHOR]

Based on field geological survey and unmanned aerial vehicle (UAV) photography, this paper studied the inherent causes, intrinsic mechanisms, and kinematic characteristics of a catastrophic landslide of dissolved and fractured rock slope in a mountainous area of Southwestern China. The discrete element method (DEM) model of representative volume element of fractured rock mass considering karst existence was developed with its micromechanical parameters calibrated from laboratory element tests. The coupled finite difference and discrete element methods (FDM-DEM) were then employed to simulate deformation and failure evolution and collapse development of the rock slope with both internal and externally triggering factors properly addressed. The kinematic characteristics of mobile collapse debris flow were analyzed, and the numerical simulation results were validated by laboratory physical model test. The results show that the internal causes were mainly manifested in slope structure, lithology combination, karst, and fracture development, among which the unfavorable interaction disintegrated rock masses. The primary external cause was the staged underground coal-mining operations underneath the collapsed body, which led to large cracks appearing at the back edges of the slope. The maximum velocity of mobile collapse debris was about 65 m/s with the maximum travel distance of more than 600 m. Numerical simulation results matched well with both field forensic investigation and laboratory physical model test results. The findings would help further understand the deformation and failure process of fractured rock slope subject to underground mining and provide technical reference for accurate assessment and proper mitigation of similar landslide disasters. [ABSTRACT FROM AUTHOR]

This paper presents a study on an ancient river-damming landslide in the SE Tibet Plateau, China, with a focus on time-dependent gravitational creep leading to slope failure associated with progressive fragmentation during motion. Field investigation shows that the landslide, with an estimated volume of 4.9 × 107 m3, is a translational toe buckling slide. Outcrops of landslide deposits, buckling, toe shear, residual landslide dam, and lacustrine sediments are distributed at the slope base. The landslide deposits formed a landslide dam over 60 m high and at one time blocked the Jinsha River. Optically stimulated luminescence dating for the lacustrine sediments indicates that the landslide occurred at least 2,600 years ago. To investigate the progressive evolution and failure behavior of the landslide, numerical simulations using the distinct element method are conducted. The results show that the evolution of the landslide could be divided into three stages: a time-dependent gravitational creep process, rapid failure, and granular flow deposition. It probably began as a long-term gravitationally induced buckling of amphibolite rock slabs along a weak interlayer composed of mica schist which was followed by progressive fragmentation during flow-like motion, evolving into a flow-like movement, which deposited sediments in the river valley. According to numerical modeling results, the rapid failure stage lasted 35 s from the onset of sudden failure to final deposition, with an estimated maximum movement rate of 26.8 m/s. The simulated topography is close to the post-landslide topography. Based on field investigation and numerical simulation, it can be found that the mica schist interlayer and bedding planes are responsible for the slope instability, while strong toe erosion caused by the Jinsha River caused the layered rock mass to buckle intensively. Rainfall or an earthquake cannot be ruled out as a potential trigger of the landslide, considering the climate condition and the seismic activity on centennial to millennial timescales in the study area. [ABSTRACT FROM AUTHOR]

Due to the huge losses caused by landslides, landslide deformation monitoring has become essential. The change of the tilting angle of the slope surface can reflect the instability process of the landslide. However, there are few studies on the relationship between the instability process of landslides and the tilting angle of the slope surface on the time scale. Therefore, there are even fewer cases in using the inclination angle of the landslide as an early warning method in practice. On August 24, 2018, a wireless tilt sensor network was deployed in the Heifangtai area of Gansu Province in China, to monitor the inclination state of loess landslides to study the relationship between the tilting angle of the slope surface and the landslide instability process. At 4 o'clock on October 5, 2019, the wireless tilt sensor network mentioned above detected a new loess fall landslide. Through the analysis of the monitoring data from the tilting sensor nodes named T3 and T4, we found that the tilting angle of the landslide surface in the pre-sliding stage has significant change compared with the stable stage, which directly verifies that the inclination data can be used for landslide warning. At the same time, we inputted the monitored inclination data into the inclination warning model proposed by Xie et al. (Landslides 17:301–312, 2020) for verification and found that the trend of inclination changes matches the model well. However, the forecast time error is large. Therefore, this paper optimizes the inclination early warning model proposed by Xie. Using the optimized early warning model, the predicted landslide occurrence time based on the data of the T3 node was 0.517 days, which was about 8 h later than the actual landslide that occurred on October 4, 2019, which is an improvement of forecasting. This case serves as an on-site direct evidence to further verify the feasibility of using the landslide body inclination state as a landslide monitoring and early warning parameter which can be promoted and used in practice. [ABSTRACT FROM AUTHOR]

A rainfall-induced catastrophic landslide occurred at UTC + 8 03:50 local time on August 21, 2020, in Zhonghaicun village (N 29°20′30″, E 102°41′40″), Hanyuan County, Sichuan Province, China, resulting in damage to 6 buildings and 9 casualties (7 fatalities and 2 missing). In this paper, multitemporal remote sensing images, comprehensive field investigations, drilling holes, deformation monitoring, and numerical simulations are used to identify the main characteristics and the formation mechanism of the event. The results showed that long-term intensive rainfall in 2020 played an essential role in the failure of the Zhonghaicun landslide and that strong earthquakes in Wenchuan County (Ms 8.0, on 12 May 2008) and Lushan County (Ms 7.0, on 20 April 2013) also exerted a negative influence on the evolution process of this catastrophic landslide. Several traction zones appeared after the occurrence of the landslide. Fortunately, no evident deformation was observed and these slopes were in a stable state. In addition, a large-scale potential sliding slope, four small-scale landslides, and a debris flow were found in the study area. [ABSTRACT FROM AUTHOR]

Qinghai-Tibet Plateau is one of the most seismically active aeras of China and rock mass structures or geological structure are also complex here. Earthquake-induced geological disasters are occurred frequently in this region. Among them, plane failure is often generated on bedding rock slope especially in destructive area of the Wenchuan earthquake which caused numerous casualties and serious economic loss. To reveal failure mechanism of bedding rock slope, this paper studied the seismic response and investigated progressive failure characteristics of the bedding rock slope through a large-scale shaking table test. A bedding rock slope of 45° contained an unfilled joint set with spacing of 0.1 m that dipped at an angle of 34° out of the surface, was conducted in a rigid model box, with a length of 3.47 m, width of 0.68 m, and height of 1.2 m, respectively. A series of tests results show that acceleration amplification factor in horizontal direction (AAF-X) increases with the increase of the slope elevation and acceleration amplification factor in vertical direction (AAF-Z) amplifies at lower part of the slope. When the shaking intensities are over 0.2 g, the slope crest and its vicinity start to show nonlinear dynamic response. Existence of bedding planes let the isoline morphology of AAF-X redistribute and dominate the seismic amplification at the crest. The progressive failure progress of the model under earthquake can be divided into four stages. This novel experiment offers some important insights to mechanism of bedding plane landslides triggered by earthquakes, evaluation stability of slopes under earthquake, and disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The Jianshanying landslide in Guizhou province, China, is a typical mining-induced landslide with its complex formation in karst mountainous area. However, the spatiotemporal deformation characteristics and failure mechanism are still unknown. In this paper, the surface deformation time series from 2017 to 2020 is firstly estimated by combining multi-temporal synthetic aperture radar (SAR) interferometry and improved offset-tracking methods to overcome the difficulties caused by the large gradient surface deformation and surface material changes. Then, the spatiotemporal characteristics of Jianshanying landslide is analyzed in terms of line-of-sight (LOS) deformation with stacking InSAR method and two-dimensional (2D) deformation with improved SAR offset-tracking method. And the different deformation features between sliding body and surrounding areas are uncovered. Accordingly, we point out the "three-section" surface deformation model. We further analyze the deformation time series by considering the joint effects of rainfall and underground mining. Finally, we conceptualize the landslide failure mode for the Jianshanying landslide, which can be extended to the similar landslide in karst mountainous areas of southwestern China. [ABSTRACT FROM AUTHOR]

At about 04:00 on 10 August 2019, the heavy rainfall carried by Typhoon Lekima induced a catastrophic rockslide in Shanzao Village, Yongjia County, China. The heavy rainfall triggered the tuff rock mass of about 12×104 m3 in volume sliding along the interbedded mudstone. Subsequently, the Shaozao river at the slope toe was blocked by the sliding mass, which formed a landslide dam afterwards. The dam broke later, ruining many houses and causing 32 casualties. We investigated the geological setting, characteristics, and failure mechanisms of the Shanzao rockslide through field investigation. In addition, the dynamic course of the landslide was analyzed using discrete element method. This paper reveals the dynamic course and risk assessment of the rockslides in the mountainous areas of the Southeast China. The findings can provide guidelines for processing the landslides with similar geological conditions. [ABSTRACT FROM AUTHOR]

Owing to the heavy rainfall, a landslide occurred at the Anqian Iron mine, at 18:00(UTC + 8) on November 24, 2019, in China. The landslide was about 3.0 × 104 m3 and caused damage to the road of transporting waste materials. Failure characteristics and the mechanism of this landslide were analyzed in this study. The landslide area was divided into three parts: the rear tension cracking area, the middle sliding deformation area, and the front colluvium area. A contact-free measuring technique using the new ShapeMetrix3D system was applied and 204 joints were analyzed based on equal-angle stereographic projection. Thus, a conceptual model of the mechanism of the landslide was constructed and the formation process of the landslide was divided into three stages: the first shearing and dislocation stage; the second sliding, front bulging, and rear tractive cracking stage; and the third local rock mass collapse and colluvium depositing stage. Numerical modeling was performed to discover the landslide mechanism by progressively reducing the shear strength of rock mass. The results showed that the original slope was stable, whereas heavy rainfall triggered the landslide, and the predicted failure surface matched closely the field investigations. The factor of safety obtained by real three-dimensional analyses was slightly higher than that obtained by plane problem analyses, and the difference was attributed to the three-dimensional effect of the landslide. This paper also presents the results obtained from the parametric analysis in order to understand the impact of shear strength parameters on the overall stability of the slope. [ABSTRACT FROM AUTHOR]

Critical acceleration is an inherent property of a slope and determines the slope stability under seismic action. The critical acceleration model is a core element of regional seismic landslide hazard assessment. Therefore, the purpose of this paper is to reveal the influence of different critical acceleration models on assessments of potential earthquake–induced landslide hazards. Traditionally, the Newmark critical acceleration model has commonly been used to evaluate the potential earthquake–induced landslide hazard. This method needs to assume the failure depth of the slope, which leads to an underestimation of the predicted displacement of the seismic landslide. Recently, the prediction equations of critical acceleration based on a parametric study of the limit equilibrium method overcomes the limitation of Newmark critical acceleration model and has been applied to assessments of co-seismic landslide hazards. In this study, we use Newmark critical acceleration model and prediction equations of critical acceleration to obtain the distribution maps of potential earthquake–induced landslide hazard in Shimian County, with peak ground acceleration of 10% and 2% exceeding the probability in 50 years. In addition, the nonlinear effect of site and topographic effects on peak ground acceleration were considered. The results show that Newmark critical acceleration model underestimates the area and value of the predicted displacement, while prediction equations of critical acceleration produces seismic landslides in a wider range of mountainous areas. This indicates that the critical acceleration model has a significant influence on assessments of potential earthquake–induced landslide hazards. In addition, the study not only provides valuable reference for assessment of potential earthquake–induced landslide hazard, emergency response of seismic landslides, and land planning in the study area, it also provides a useful demonstration for the selection of a critical acceleration model in seismic landslide hazard assessments for future researches. [ABSTRACT FROM AUTHOR]

Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments. [ABSTRACT FROM AUTHOR]

The great M ~ 8 1920 Haiyuan earthquake (HYEQ) was one of the largest and most deadly earthquakes in China in the last century, with ~ 234,000 deaths. The earthquake occurred within the Loess Plateau of northern China, where Quaternary loess deposits form a distinctive blanket across the landscape. Large regions of this loess cover experienced co-seismic landslides. Based on an analysis of the original disaster reports, field surveys, and satellite image interpretation, we have compiled the shaking effects of the earthquake, including the distribution of landslides, fatalities, and structural damage. Landslides triggered by the HYEQ (n > 7,000) are concentrated south of the Haiyuan fault, in a region that has both thick loess cover and long-term relief generated by the drainage network. This distribution is spatially separated from landslides triggered by other earthquakes. We find that in contrast to previous studies, the most important factor in the severe death toll of the HYEQ was the collapse of housing by ground shaking, including collapse of loess house-caves. Landslides were a secondary factor; although up to 32,000 deaths occurred in areas with intense landsliding. Based on the revised distribution pattern of landslides and damage (e.g., house collapses), we suggest that the isoseismal intensity IX line extends south of previous locations. We have also identified 126 dammed lakes created by co-seismic landslides, which form major modifications of this semi-arid landscape. The research methods in this paper, combining historical review, satellite image interpretation, and field validation of landslides, can be used as a reference for studies of other areas affected by historical earthquakes and co-seismic landslides, elsewhere in the Loess Plateau and beyond. [ABSTRACT FROM AUTHOR]

This research aims to explore detailed spatial distribution characteristics, identify key factors weight, and establish an accurate susceptibility regionalization model of coseismic landslides by the Mw 6.6 Iburi, Japan, earthquake sequence of 6 September 2018. Based on the remote sensing interpretation database, 5,977 individual coseismic landslides were delineated, which occupies an area of about 15.26 km2. The relationship between eight key factors and spatial distribution of coseismic landslides were precisely analyzed by the landslide area density (LAD) and landslide point density (LPD) curves. In order to obtain the weight of eight key factors more accurately, this paper compares the similarities and differences of the two curves in the earthquakes with similar magnitude, which was included the Lushan and Jiuzhaigou earthquake in China. According to the weight of eight key factors, the improved weight of evidence method was used to produce the susceptibility regionalization map of coseismic landslides in the Iburi earthquake. The results show that the improved weight of evidence method has better precision and can provide a scientific coseismic landslides susceptibility regionalization map for disaster prevention and mitigation in the Iburi earthquake. [ABSTRACT FROM AUTHOR]

A high-position destructive rockfall, significantly influenced by post-seismic effect of Wenchuan earthquake in 2008 and associated aftershocks, was reported in Miansi Town of Wenchuan County, Sichuan Province, China. About 3,000 m3 of rock at high position detached from a dipping rocky slope, which manifested as a translational motion. Detailed field investigation, geological mapping, kinematic analysis, and numerical modelling are presented to comprehensively analyze the complex failure mode of the post-seismic rockfall. The results demonstrated that the sliding surfaces are mainly controlled by the internal shear discontinuity sets. The backscarps are progressively evolved from the tensile cracks at rear slopes caused by the Wenchuan earthquake. Because of the combination effect of heavily shocks of previous earthquake and local geomorphology of the rockfall with beneficial for facilitating the magnification effect on peak ground acceleration (PGA), the rock mass of slopes is dramatically damaged and fractured. The original intact slopes finally developed to the shattered slopes as an optimum birthplace for the post-seismic rockfalls. It should be noticed that intensive precipitation plays a triggering factor that generates the hydrostatic water pressure in the rear cracks and the uplift pressure at the bottom sliding surface within a short time. The evolution processes are divided into three stages: slope relaxation stage, rock shattering and disaggregation stage, and high-speed falling stage. Velocities, energy, and bounce heights of falling rock blocks are computed by the RocPro3D software for understanding the corresponding kinematics. The paper can provide an insight into post-seismic rockfalls in the tectonic areas associated with the combination of seismic activities and intense rainfall. [ABSTRACT FROM AUTHOR]

The accuracy of landslide displacement prediction can effectively prevent casualties and economic losses. To achieve accurate prediction of the Majiagou landslide displacement in the Three Gorges Reservoir (TGR), China, a hybrid machine learning prediction model considering the deformation hysteresis effect is proposed. The real-time deep displacement measurements were captured by using in-place inclinometers with Fiber Bragg grating (FBG) sensors. The time series method was adopted to divide the total displacement into a trend term and periodic term. Trend displacement was determined by the geological condition and predicted by the fitting method. Periodic displacement was controlled by external factors such as rainfall and fluctuation of reservoir water level. Before making the prediction, the grey correlation analysis was adopted to confirm that the fluctuation of the reservoir water level was the main influence factor. In view of the deficiency that current prediction methods could not quantitatively determine the lag time of landslide deformation and thus select the influencing factors empirically, the dynamic analysis of the correlation between periodic influence factors and periodic displacement was carried out in this paper, and the deformation lag time was identified to be 18 days by using set pair analysis (SPA) method. Finally, the optimal influence factors were selected and the prediction model of Majiagou landslide based on support vector machine optimized by particle swarm optimization (SPA-PSO-SVM) was established. Results showed that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the proposed SPA-PSO-SVM prediction model are 0.28 and 12.8, respectively. Compared with the PSO-SVM model, the prediction accuracy of the proposed model had been improved significantly. The reliability and effectiveness of the SPA-PSO-SVM prediction model is verified and it has apparent advantages while predicting landslide displacement with deformation hysteresis effect involved. [ABSTRACT FROM AUTHOR]

This paper provides newly found and profound insight into the initiation mechanism of the first Baige landslide occurred on the upper reaches of the Jinsha River in October 10, 2018. The detailed geological characteristics are interpreted by comprehensive field surveys in the past year, which indicate that the Baige landslide developed in the Jinsha River tectonic suture zone, and the active tectogenesis significantly contributes to broken stratigraphic structures and complex spatial distribution of lithologies. The initiation is considered to be long-term creep under the exogenic and endogenic integration, while the active tectogenesis and the influence of serpentinite and foliation, respectively, are the primary exogenic and endogenic factors. In addition, this creep process can be analyzed within three stages: evolution and formation of failure-prone geological structures, progressive deformation and fracturing, and shear failure of the locking section. The distribution and easily degraded behavior of the serpentinite are the fundamental, enabling the formation of failure-prone structures and also responsible for the subsequent deformation evolution. The foliation controls the toppling deformation-failure mode and direction. Furthermore, this catastrophic landslide further reminds us to pay attention to the landslides along the upper reaches of the Jinsha River, and our preliminary results indicate that the distribution characteristics of landslides significantly depend on the Jinsha River tectonic suture zone and the serpentinite mélange belt and thus are always associated with tectonically induced damage. [ABSTRACT FROM AUTHOR]

Rainfall infiltration in unsaturated slopes alters the effective stress through pore water pressure changes, thus causing ground deformation. Although important to assess the timescale over which the margin of safety of a slope decreases, such coupled processes are rarely accounted in the context of spatially distributed hazard assessment procedures. In this paper, a physically based, spatially distributed model accounting for full hydro-mechanical coupling is discussed. The model relies on a vectorized finite element (FE) solver to calculate the stability of deformable unsaturated infinite slopes subjected to transient flow. First, the FE solver is used to study the response of individual slopes to a prolonged rainfall for three scenarios (i.e., rigid, swelling, and collapsible soil). Then, the model is used in the context of spatially distributed computations to assess spatiotemporal variations of factor of safety over a large area. For this purpose, a series of shallow landslides occurred in a mountainous landscape covered by collapsible loess deposits in northwestern China was used as test site. The analyses show that hydro-mechanical couplings affect the performance of the model in terms of computed failure time and areal extent of the unstable zones. Specifically, volume collapse due to suction decrease is found to reduce the time of failure compared with uncoupled computations obtained for a rigid soil scenario. The most substantial advantages of using coupled analyses have been reported with reference to gentle slopes, for which the higher rate of suction reduction driven by volume change was crucial to capture landslide source areas that would otherwise be overlooked by uncoupled analyses. The proposed methodology offers a complete tool for landslide hazard assessment, in that it incorporates sources of coupling between hydrology and mechanics that are crucial to replicate the physics of landslide initiation. [ABSTRACT FROM AUTHOR]

River blocking caused by landslide dams is a common geological disaster in mountainous regions worldwide, threatening the safety of human lives and infrastructure. However, studies on the formation process of landslide dams and the criteria of river blockage are still at an early stage. This paper built a laboratory-scale experimental apparatus for simulating the formation and evolution process of landslide dams, and then carried out a total of 29 experiments. It analyzed the common evolution stages of river blocking, including landslides, dam formation, and dam breaching in detail. Then, we investigated the influences of the landslide volume, the landslide discharge, and the water flow rate on a criterion and the time length of river blockage. The experimental results suggest that the formation condition of a landslide dam is related to the ratio of the landslide discharge to the water flow rate. Based on this understanding, we proposed a dimensionless River Blockage Criterion (RBC) to judge the formation of landslide dams. The criterion indicates that when RBC > 1.5, a landslide dam may form; otherwise, a landslide dam may not form. Additionally, the time length of river blockage was found to be related to the landslide volume and the water flow rate. Subsequently, we proposed a model for predicting the time length of river blockage and verified it by the 29 experimental results. The Baige River-blocking event, which occurred on the Jinsha River, China, in 2018, was used as a case study to further verify the RBC and the proposed model. The results show that the proposed RBC agreed well with reality, and the percentage difference between the calculated and the actual time length of river blockage was 11%. The findings of this study could contribute to the early warning and emergency response of river-blocking disasters. [ABSTRACT FROM AUTHOR]

Currently, the frequency of landslides is increasing. Scientific monitoring methods are playing an essential role in effectively reducing landslide disasters. This paper proposes a method for identifying the cracks at the trailing edge of a landslide (TEL) based on image processing technology and adopts the custom interval median comparison algorithm (IMCA) to calculate the crack motion parameters. First, we perform a series of processes on the TEL images, including image preprocessing, Otsu's algorithm processing, and Canny edge detection processing, to identify the outline of the TEL. Then, we propose using the azimuth and displacement to characterize the motion of the cracks and using the IMCA to calculate the changes before and after motion of any two groups of cracks. Finally, we design a computer program using a free and open-source widget toolkit (named QT platform) based on the calculation model that corresponds to the proposed method, and we apply the crack monitoring test to a 3D simulation model, a gravel model, a soil model, and a collapsed body of the Panzhihua Airport landslide in southwestern China. From the results, it can be assessed that the method can identify the outline of the TEL and calculate the azimuth and displacement of two crack curves before and after motion. These two parameters can describe the movement of the trailing edge cracks of the monitored landslide. Thus, this method can be used in early warning system for landslide hazards. [ABSTRACT FROM AUTHOR]

The accurate modeling and predicting of landslide deformation is crucial to the prevention of landslide hazard. This paper presents a pioneering study of modeling and predicting the reservoir landslide displacement with deep learning algorithm. A data-driven framework using deep belief network and control chart has been introduced to explore the temporal patterns of displacement and potential of identifying seasonal faster displacement. First, the continuous wavelet analysis has been applied to decompose the time-series precipitation, reservoir water level, and displacement into seasonal and residual components. Second, the deep belief network has been constructed to predict the future displacement. Third, it utilizes the exponentially weighted moving average (EWMA) control chart to derive the boundaries as alarm conditions of seasonal faster displacement. A group of tests are conducted to compare the performance of the deep belief network with other state-of-the-art machine learning algorithms. Computational results demonstrated the effectiveness of the deep belief network in extracting highly non-linear data features. In addition, the advantage of utilizing control charts has been further validated by the accuracy of examining the seasonal faster displacement based on the case study in Baishuihe landslide in Three Gorges Reservoir, China. [ABSTRACT FROM AUTHOR]

In March 15, 2019, a small-scale loess landslide occurred at Zaoling, Shanxi Province, China, destroying several buildings and killing 20 people. Based on a digital orthophoto map (DOM) and digital elevation model (DEM) derived from Dajiang Pro4 UAV aerial images, coupled with field investigations, this paper describes the geometric features of this slope failure and analyzes its possible inducing factors. Results show that this landslide has a circular chair shape with a sliding direction of 355°. The total projected area of the landslide is 11,800 m2, in which the source is 9880 m2, and the main scarp is 1995 m2. The volume of the source material and loose deposit debris are about 10 × 104 m3 and 12 × 104 m3, respectively. Our research suggests that the inducing factors of this event may include the low strength and structure of the loess under a wet condition, special landforms of the slope, and human activities such as water discharge from a nearby bath center building and coal mining. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is vital for landslide risk management and urban planning, and the scientific community is continuously proposing new approaches to map landslide susceptibility, especially by hybridizing state-of-the-art models and by proposing new ones. A common practice in landslide susceptibility studies is to compare (two or more) different models in terms of AUC (area under ROC curve) to assess which one has the best predictive performance. The objective of this paper is to show that the classical scheme of comparison between susceptibility models can be expanded and enriched with substantial geomorphological insights by focusing the comparison on the mapped susceptibility values and investigating the geomorphological reasons of the differences encountered. To this aim, we used four susceptibility maps of the Wanzhou County (China) obtained with four different classification methods (namely, random forest, index of entropy, frequency ratio, and certainty factor). A quantitative comparison of the susceptibility values was carried out on a pixel-by-pixel basis, to reveal systematic spatial patterns in the differences among susceptibility maps; then, those patterns were put in relation with all the explanatory variables used in the susceptibility assessments. The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility were identified. The results shed a new light on the susceptibility models, identifying systematic errors that could be probably associated either to shortcomings of the models or to distinctive morphological features of the test site, such as nearly flat low altitude areas near the main rivers, and some lithological units. [ABSTRACT FROM AUTHOR]

Hydraulic parameters are key data for calculating groundwater level, which is critical for assessing reservoir landslide stability. However, the quantitative understanding the influence of hydraulic parameters on the groundwater level of a reservoir landslide remains limited. In this paper, we apply a novel global sensitivity analysis method, PAWN, to quantify the sensitivity of hydraulic properties. The Shuping landslide, which is a typical reservoir colluvial landslide located in the Three Gorges Reservoir area, China, is used as a study case. The hydraulic parameters are first sampled within their entire feasibility space by the Latin hypercube sampling method. These samples are then used as inputs into a nonintrusive finite element program to automatically compute the corresponding groundwater level outputs. Finally, sensitivity indices are calculated based on the input–output dataset via the PAWN method. The global sensitivity analysis results provide useful guidelines for site investigation and reservoir colluvial landslide model simplification and calibration. [ABSTRACT FROM AUTHOR]

The environmental factors of landslide susceptibility are generally uncorrelated or non-linearly correlated, resulting in the limited prediction performances of conventional machine learning methods for landslide susceptibility prediction (LSP). Deep learning methods can exploit low-level features and high-level representations of information from environmental factors. In this paper, a novel deep learning–based algorithm, the fully connected spare autoencoder (FC-SAE), is proposed for LSP. The FC-SAE consists of four steps: raw feature dropout in input layers, a sparse feature encoder in hidden layers, sparse feature extraction in output layers, and classification and prediction. The Sinan County of Guizhou Province in China, with a total of 23,195 landslide grid cells (306 recorded landslides) and 23,195 randomly selected non-landslide grid cells, was used as study case. The frequency ratio values of 27 environmental factors were taken as the input variables of FC-SAE. All 46,390 landslide and non-landslide grid cells were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide/non-landslide data, the performances of the FC-SAE and two other conventional machine learning methods, support vector machine (SVM) and back-propagation neural network (BPNN), were compared. The results show that the prediction rate and total accuracies of the FC-SAE are 0.854 and 85.2% which are higher than those of the SVM-only (0.827 and 81.56%) and BPNN (0.819 and 80.86%), respectively. In conclusion, the asymmetric and unsupervised FC-SAE can extract optimal non-linear features from environmental factors successfully, outperforms some conventional machine learning methods, and is promising for LSP. [ABSTRACT FROM AUTHOR]

This paper reports on a typical slow-moving landslide that occurred in Shuitianba village in Wanyuan County, Sichuan Province, China, on September 16, 2017. The landslide in Wanyuan County is regionally outstanding for its features and well-developed landslide morphology. Hence, this event can provide a good case study for understanding the possible triggering conditions of the occurrences of all the landslides examined in Wanyuan County. The 225 residents of Shuitianba village did not flee the village because the deformation of the landslide was extremely slow. However, the primary emergency tasks are still necessary to reduce the landslide risk that may affect the residents. Detailed field investigations, boreholes, and exploratory trenches were performed to investigate the characteristics and triggering factors of this landslide. Rainfall and anthropogenic activities (such as informal, unplanned settlements and deforestation) were revealed to be the main triggering factors. The main engineering control measures, including anti-sliding piles, an anti-sliding retaining wall, and intercepting ditch, were applied to stabilize the landslide. However, landslide mitigation measures have not been finished, and hence, some interim measures are suggested to effectively reduce landslide risk. [ABSTRACT FROM AUTHOR]

High-locality landslides primarily occur in the upper zone of the failed slope, in which the slip surface is considerably higher than the foot of the slope. This paper discusses a high-locality landslide that occurred on August 8, 2017, on the left bank of Zagunao River in Tonghua Town, southwest China. The landslide, called the Tonghua landslide, originated on a 716-m-high hillslope, which was formed by preexisting glacial deposits. The main body of the landslide is currently suspended on hillslope, with a relative relief of 353 m and is undergoing progressive failure. The town of Tonghua is located downslope of this landslide, and hence, the landslide could cause severe damage, including casualties and the destruction of infrastructure. Very few mitigation measures have been implemented to reduce the potentially devastating consequences due to the high locality and continuous movement of the landslide. We conducted comprehensive studies including field investigations, aerial photographic interpretations, pluviometric regime analysis, and superficial displacement monitoring to understand the failure mechanism and evolutionary process of this ongoing landslide. Considering that a Ms 7.0 earthquake occurred in Jiuzhaigou, approximately 220 km northeast of Tonghua Town, on the same day as the landslide, and that sporadic but persistent rainfall occurred before slope failure, we believe that both the rainfall and the earthquake were potential triggers for this landslide. However, further studies are necessary to better understand the characteristics and to develop hazard mitigation strategies for this type of landslide. [ABSTRACT FROM AUTHOR]

This paper reports on a recent landslide event, which occurred in Yanyuan County at Xichang City, Sichuan Province, China, on 19 July 2018. Due to the continuous and heavy rainfall between 8 July 2018 and 18 July 2018, approximately 18 million m3 of soil detached from an ancient landslide and buried 65 buildings, the entire sliding process lasted for several minutes. Here, based on field investigations and unmanned aerial vehicle (UAV) photography, we describe the characteristics and failure mechanism of this landslide. The results showed that the landslide was triggered by the continuous and heavy rainfall. Additionally, during the first post-failure stage, the motion of this landslide involved typical successive movement, and over time, the style of movement progressively turned flow-like, which can be classified as debris slide by using the 1978 Varnes classification scheme. The landslide event described here is an outstanding case for landslide studies. In particular, this landslide represents a good illustration of the transform mechanism of landslide movement from sliding to flow-like. [ABSTRACT FROM AUTHOR]

The resettlement of residents to new urban areas in the Three Gorges Reservoir region, China, has generally adopted the local resettlement method, but engineering disturbances in conjunction with heavy rainfall have contributed to landslide instability. This paper discusses the influential factors and formation mechanism of the Jinjiling landslide in Jiangdong District, Wushan County, Chongqing, China. This study concludes that the cutting and filling of slopes have led to loading of the rear edge and open cut excavation at the downhill side of landslide. More importantly, the filling of channels with surface soil has blocked the surface water discharge channel along the landslide body, thereby transforming surface water into groundwater. Therefore, under heavy rainfall, the groundwater level in the Jinjiling landslide rose substantially, resulting in large deformation in approximately August 1, 2018. After emergency treatment, including groundwater pumping, the landslide deformation slowed considerably. The rising groundwater caused by filling of the slope was the key factor influencing the landslide. Based on the deformation characteristics and formation mechanism of the landslide, comprehensive control measures referred to as the "drainage+unloading+support" scheme are proposed, and drainage should be the focus of this type of landslide protection project. The results of this study may provide a reference for similar slope engineering projects. [ABSTRACT FROM AUTHOR]

The Yangtze River is one of the most important rivers in China due to its large basin size, the large population along the river, and the numerous large dams and reservoirs on the river. The Jinsha River, the upper reach of the Yangtze River, was dammed twice recently at Baige, Tibet, one on 10 October 2018 and the other on 3 November 2018 (UTC + 8). Accordingly, two large landslide dams, 61 m and 96 m in height to the lowest dam crest, were formed in a 3-week interval. Due to the large inflow rates at the time of damming, the barrier lake level rose rapidly, posing huge risks to the downstream residents and properties. In managing the landslide dam risk, one of the important tasks is to predict the dam breaching flood beforehand. This paper focuses on rapid prediction of the dam breaching hydrograph and breach geometric parameters of the two landslide dams. The predictions were made timely before the breaching of the two landslide dams using both erosion-based empirical equations and numerical simulation and were refined based on detailed field investigation at the site after breaching. Comprehensive field investigations were conducted to determine the geological structures of the landslide dams, characterize the erodibility of dam materials, and measure the final beach dimensions. The simulated dam breaching processes, outflow hydrographs, lake water level changes, and final breach dimensions were validated by field observations. Compared with the hypothetical scenario without a diversion channel on the second landslide dam, a diversion channel 15 m in depth successfully lowered the peak flood discharge by about one third and helped to mitigate the flood risk significantly. The analysis outcome serves as basis for warning and evacuation of the downstream residents and making appropriate engineering risk mitigation plans. [ABSTRACT FROM AUTHOR]

Local-scale landslide susceptibility mapping (LSM) provides detailed information for decision making and planning. Most published landslide susceptibility maps lack reliable information at the local scale due to the spatial heterogeneity being ignored. To enrich the local-scale information of LSM, multiple information fusion methods for the local spatial heterogeneity and regional trends of control factors are needed. However, no information fusion method has been proposed for LSM yet. In this paper, we developed a new integrated statistical method, named B-GeoSVC, under the hierarchical Bayesian framework for LSM. Specifically, this model applied the GeoDetector method to fit the regional trends of control factors and employed spatially varying coefficients (SVC) model to fit the local spatial heterogeneity of each control factor. Then, the regional trends and local spatial heterogeneity information were fused within the hierarchical Bayesian framework. The B-GeoSVC model was verified using data from the Duwen basin of China, which was in the central region affected by the MS 8.0 Wenchuan earthquake that occurred on May 12, 2008. Under a cross-validation experiment, the prediction accuracy rate of the B-GeoSVC model was 86.09%, and the area under the curve was 0.93, which suggested that the B-GeoSVC model was able to achieve relatively accurate local-scale LSM and provide richer local information than traditional regional scale LSM. More importantly, not only the B-GeoSVC model could be employed as a general solution to fuse both regional and local-scale information for landslide mapping, but also offer new insights into the broader earth science and spatial statistics. [ABSTRACT FROM AUTHOR]

The focus of this paper is on two large sequential landslides which formed a dam and the resulting lake that occurred along the Jinsha River on October 11 and November 3, 2018. About 24 and 9 × 106 m3 of material failed and rushed into the river, respectively. Both landslides totally blocked the river and induced hazard chains. Four multi-temporal unmanned aerial vehicle (UAV)-based high-resolution images and digital elevation models (DEMs) before and after the landslides were obtained and merged. The initial deformation of the two landslides can be clearly captured by optical remote-sensing images and interferometric synthetic aperture radar (InSAR) analysis. The depth-integrated continuum method was adopted to analyze the dynamic process of both landslides, using the same basal friction angle and cohesion. The computational results matched well with field measurements and observations. It is shown that the evaluation of potentially landslide-prone areas by the depth-integrated continuum method is feasible and is able to provide significant information before a possible event. [ABSTRACT FROM AUTHOR]

Since lots of underground and slope excavation works were conducted during the urbanization process, an increasing number of sites in ravines around a city have been used to stockpile a large amount of excavated soils. This brings a huge challenge for researchers and managers in the risk evaluation and mitigation of potential dangers of these man-made construction waste landfills. This paper describes a recently large landslide of the construction waste landfill, which occurred at a site of Guangming new district in Shenzhen, China, on December 20, 2015. This catastrophic landslide caused the death of 69 persons and 8 persons are still missing. In this paper, this landslide was numerically simulated and analyzed. In spite of neither high-intensity rainfall nor antecedent rainfall, a slope of this landfill with a relative height of 111 m sided and caused about 2.34 million cubic meters of the soils to travel over a gentle terrain more than 1.2 km. This means that the landslide mobility index (H/L = 0.092) is much lower than a general designed value and the values in most other cases. A depth-integrated continuum method and a MacCormack-TVD finite difference algorithm are adopted, in this paper, to numerically simulate the dynamic process of this large landslide. It is found that a Coulomb friction model with consideration of the pore water pressure effects can well reproduce the main characteristics of the dynamic process of this landslide. Sensitivity analysis has demonstrated that the high pore water pressure in the soils plays a significant role in its mobility and is a key factor to the severity of this landslide. [ABSTRACT FROM AUTHOR]

Two successive landslides within a month started in October 11, 2018, and dammed twice the Jinsha River at the border between Sichuan Province and Tibet in China. Both events had potential to cause catastrophic flooding that would have disrupted lives of millions and induced significant economic losses. Fortunately, prompt action by local authorities supported by the deployment of a real-time landslide early warning system allowed for quick and safe construction of a spillway to drain the dammed lake. It averted the worst scenario without loss of life and property at least one order of magnitude less to what would have been observed without quick intervention. Particularly, the early warning system was able to predict the second large-scale slope failure 24 h in advance, along with minor rock falls during the spillway construction, avoiding false alerts. This paper presents the main characteristics of both slope collapses and damming processes, and introduces the successful landslide early warning system. Furthermore, we found that the slope endured cumulative creeping displacements of > 40 m in the past decade before the first event. Twenty-five meter displacement occurred in the year immediately before. The deformation was measured by the visual interpretation of multitemporal satellite images, which agrees with the interferometry synthetic aperture radar (InSAR) measurement. If these had been done before the emergency, economic losses could have been reduced further. Therefore, our findings strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods (i.e., multitemporal satellite or UAV images) and in situ monitoring system as a way to reduce risk. It is expected that this success story can be replicated worldwide, contributing to make our society more resilient to landslide events. [ABSTRACT FROM AUTHOR]

Natural disasters often show highly heterogeneous character due to complex geo-environmental settings. The spatial distribution of landslides is generally clustered at different scales. In this paper, we proposed a methodology for landslide susceptibility mapping (LSM) with consideration of spatial correlation and distribution of clustered landslide data. To quantify the spatial correlation of landslides, a normalized spatial-correlated scale index (NSCI) was introduced. Based on the definition of landslide frequency ratio, calibrated landslide potential index (CLPI) was proposed to account for the effect of landslide clustering. Considering the fractal distribution of landslides, the variable fractal dimension model (VFDM) was introduced to measure the spatial association between clustered landslides and conditional factors. Based on the definition of fractal dimension (D), the weights of the factors were obtained from fractal perspective. We proposed a weighted calibrated landslide potential model (WCLPM), obtained by the combination of CLPI values and weights of the factors. The proposed method is illustrated by example in Xinjiang, NW China, where landslide points are clustered at regional scale. In the example, the landslides were randomly split into two groups: one for building landslide model (training dataset) and the other for validating the model (validating dataset). Five landslide conditional factors (lithology, tectonic faults, elevation, slope, aspect) were selected, processed, and analyzed in a geographic information system (GIS) environment. Predictive accuracy of the WCLPM was evaluated and compared based on the calculation of area under the prediction-rate curve (AUPRC). The example shows that the proposed WCLPM provides good prediction for the study area (AUPRC = 0.8700). This study provided a novel and practical method for LSM. [ABSTRACT FROM AUTHOR]

The phase separation mechanism in a natural debris flow as a solid-liquid mixture is an important aspect in its movement and deposition. In this paper, the relationship between debris flow discharges and densities is studied based on the field observations of the Jiangjia Gully, China. A number of experiments are conducted to study the phase separation mechanism of debris flows, and the characteristics of their depositions over deceleration baffles with varying flow density and bottom slopes. The phase separation mechanism of natural debris flows is analyzed based on the mentioned experimental and field observations. During the decreasing debris flow, the flow density reduces with reduction of the flow discharge, indicating the important role of the flow discharge in changing the flow density as a driving factor. Temporal variations of the debris flow density can have two chronological orders: (a) dilute-viscous-dilute debris flow and (b) viscous-dilute debris flow. The variation order of the flow density has an important influence on the phase separation mechanism and the final deposition morphology. The experimental results show that the bottom slope and the flow density have opposite effects on the phase separation mechanism. Increasing the flow density increases the viscosity of the debris flow, decreasing the phase separation of the solid-liquid mixture. Increasing the bottom slope promotes phase separation by increasing the shear rate inside the debris flow. As a result, the phase separation mechanism of natural debris flow is mostly related to the temporal and spatial variations of the flow pattern and the flow density. [ABSTRACT FROM AUTHOR]