Characteristics Of Landslides Research Articles

Analysis of Soil–Water Characteristics and Stability Evolution of Rainfall-Induced Landslide: A Case of the Siwan Village Landslide

This paper aimed to study the soil–water characteristics and stability evolution law of rainfall-induced landslide. Taking the two landslide events in Siwan village as an example, the formation conditions of the disaster and landslide characteristics were analyzed. Additionally, the deformation characteristics and destruction mechanisms of landslides were discussed in-depth. The soil–water characteristics and hydraulic conductivity of the landslides were analyzed based on TRIM experiment results. Geo-Studio numerical software was further used for typical sections to analyze the stability of the evolution of the landslide events under rainfall conditions. The results showed that (1) The soil–water characteristic curve (SWCC) inversely varies with water content volume, and the sliding body has lower saturated water content and matrix suction than the sliding zone. The hydraulic conductivity function (HCF) increases with water content volume, and the sliding body has higher hydraulic conductivity (0.43 m/d) than the sliding zone (0.03 m/d). (2) Rainfall is the primary cause of landslides, and there is a hysteretic effect. Heavy rainfall will inevitably accelerate the formation of landslides in the analysis of the deformation characteristics and destruction mechanisms of rainfall-induced landslides. (3) Compared with the engineering analogy of the Fredlund and Xing (FX) model, the Van Genuchten–Mualem (VGM) model of the soil–water characteristics test based on the TRIM experimental system can better reflect the actual field situation. The numerical simulation method based on the TRIM experiments of the soil–water characteristics test is scientifically sound and reliable for the stability evolution of overburden rainfall-induced landslides.

Application of Stress Parameter from Liquefaction Analysis on the Landslide Induced Tsunami Simulation: A Case Study of the 2018 Palu Tsunami

The accuracy of numerical simulations of a landslide-induced tsunami depends on the landslide characteristics, such as landslide geometry and geotechnical parameters. However, owing to the difficulty in sampling and measuring submarine landslides, rough assumptions of landslide parameters typically lead to significant uncertainties. In the 2018 Palu event, the earthquake was followed by immediate cascading disasters of coastal subsidence, both land and submarine landslides and a tsunami. This scenario provides opportunities to analyze landslide phenomena on land to characterize the submarine landslide causing the tsunami. This study proposes a new approach of using shear-stress parameters obtained from liquefaction analyses as input for landslide-induced tsunami simulation. To obtain the submarine landslide parameter, using the finite element method we modeled the liquefaction happened in Jono-Oge located near Palu Valley area. The shear-stress in this area was quite small with the range 1.5–3.5 kPa. We found that tsunami simulation yielded better accuracy by applying the stress value range obtained from the liquefaction analysis on land (1.5 kPa) rather than the typically adopted stress value for general cases (20 kPa). The result from the tsunami simulation using two-layer method with identical landslide location and geometry showed that shear-stress value of landslide mass gave quite a significant effect to the tsunami height.

Insights into spatial differential characteristics of landslide susceptibility from sub-region to whole-region cased by northeast Chongqing, China

Landslides have differential characteristics in different regions. This study explores landslide susceptibility mapping (LSM) based on different evaluation units and proposes a strategy for landslides’ differential characteristics in different sub-regions. Based on data of lithology, elevation, and historical landslides, terrain units (TUs) and slope units (SUs) were obtained. LSM was developed using the Random Forest (RF) model and Light Gradient Boosting Machine (LGBM) model. The LGBM-TUs showed the highest performance and were therefore, selected to obtain LSM. The study area was divided into four sub-regions using the geographically weighted regression (GWR) model, along with spatial differential characteristics of topography conditions. The distribution and characteristics of landslides within each sub-region were assessed using GeoDetector. The results illustrated the reliability of the LGBM-TUs model. Lithology, elevation, and average annual rainfall were the dominant factors, while the influence of other factors on the occurrence of landslides was strengthened only when these factors interacted. This study proposed a new method for LSM research to insight the spatial differential characteristics of landslides in various sub-regions. Our results provide novel insights into landslide mitigation.

Disaster Mechanism and Evolution Characteristics of Landslide–Debris-Flow Geohazard Chain Due to Strong Earthquake—A Case Study of Niumian Gully

Taking the Niumian Gully landslide–debris-flow geohazard chain as an example, we analyze the disaster mechanism of the landslide–debris-flow geohazard chain and its evolution process through field investigation as a reference for disaster prevention and risk reduction in areas with strong earthquakes by analyzing the accumulative sliding amount and the critical rainfall for debris flow formed by loose deposits. Due to the 8.0 Ms earthquake in Wenchuan, the cumulative sliding of the slope reached 230.51 m, much greater than the critical sliding in the Longmen Mountain earthquake area. Permanent damage to the slope occurred, resulting in a landslide, which provided the material source for the occurrence of debris flows. When the study area experienced concentrated rainfall (e.g., exceeding 10.31 mm/h or 54.67 mm/d), loose deposits in the basin began to form a debris flow, causing a landslide–debris-flow geohazard chain. During the evolution process of the geohazard chain, the following stages were observed: pre-earthquake gestation, landslide starting during a strong earthquake, landslide blocking a channel and forming a dammed lake, bursting of the dammed lake flood–debris-flow, sliding of loose deposits, and reciprocating loose-deposit sliding and debris flows. Thus, the formation of the Niumian Valley geohazard chain can be attributed to the combined result of strong earthquake activities and rainfall. In addition, under the action of rainfall, the occurrence of the debris flow and unstable sliding of loose deposits also caused the reciprocating occurrence of debris flows, loose-deposit sliding, and flood-induced debris flows in the study area.

A geodatabase of historical landslide events occurring in the highly urbanized volcanic area of Campi Flegrei, Italy

Abstract. The analysis of geological processes threatening people and properties in a populated region should be based on a comprehensive knowledge of historical events and related characteristics. This type of information is essential for predisposing event scenarios, validating prediction models, and planning risk mitigation measures. Such activities may be more complex in some geological settings, where urban settlements are exposed to multi-hazard conditions. This is the case of the densely populated Campi Flegrei volcanic area located in the Campania region, southern Italy. Here, volcanic and seismic hazards are associated with landslides, floods, and coastal erosion, which are playing a relevant role in the landscape modification. The CAmpi Flegrei LAndslide Geodatabase (CAFLAG), here presented, provides information related to 2302 landslides that occurred in the continental, coastal, and insular sectors of the study area during the 1828–2017 time interval. Data associated with the collected landslide events highlight the characteristics of both landslides and of the affected sites. Most of the cataloged mass movements consist of rock falls affecting rocky slopes formed by lithified volcanic rocks, such as tuff or ignimbrite. In addition, rainfall-induced earth and debris slides translating into debris avalanches or rapid flows are widespread along steep slopes mantled by weakly welded pyroclastic airfall deposits, similarly to other areas of the region. The highest density of landslides is concentrated along the coastline, where mass movements are contributing to the retreat of coastal cliffs, and along inland slopes exposed towards the western directions, where most of the storm systems come from. Temporal information shows peaks of landslides in the years 1986, 1997, and 2005. A total of 127 people lost their lives as a consequence of 53 cataloged landslides, with a frequency of deadly events observed however to have decreased since the early 1980s. This information will be useful to analyze mortality and risk conditions still affecting the population of the Campi Flegrei caldera, which should be fully addressed with advanced knowledge and accurate scenarios. The full database is freely available online at https://doi.org/10.4121/14440757.v2 (Esposito and Matano, 2021).

Numerical Modeling of Kinetic Features and Stability Analysis of Jinpingzi Landslide

The kinetic features of a slow-moving landslide situated above the Wudongde hydropower station were analyzed using particle flow code 3D (PFC3D) software. This research was based on geological investigations, remote sensing interpretation, and digital elevation models to build the structure of the Jinpingzi landslide. Finite element analysis (FEM) was used to determine the sliding surface. Strength reduction theory (SRT) and particle flow code coupling were used to invert the macro-strength parameters into micro-strength parameters. Finally, the slope failure process was simulated. Meanwhile, the displacement vector angle (DVA) and velocity were used for stability analysis. The simulation results of the kinetic features of slow-moving landslides show that the initial stage begins with accelerated movement, followed by constant-velocity movement and instability failure. The larger the reduction coefficient is, the shorter the duration of each stage is. A two-parameter instability criterion is proposed based on velocity, DVA, and reduction coefficient. Using this criterion, the critical velocity was 200 mm/s, and the critical DVA was 28.15°. The analysis results agree with the actual field monitoring results and motion process. This work confirms that the PFC3D modeling method is suitable for simulating the motion features of landslides.

An identification method of potential landslide zones using InSAR data and landslide susceptibility

Landslides are destructive to property, infrastructure and people in potential landslide zones. Identifying potential landslides is an important step in landslide preparedness and will help develop sustainable landslide risk management. Interferometric synthetic aperture radar (InSAR) and landslide susceptibility assessment (LSA) have poor reliability in individually identifying potential landslide zones. This study proposes a threshold model for identifying potential landslide zones that fuses the InSAR two-dimensional (vertical direction and east-west direction) deformation rates and LSA results. The deformation rate threshold for this threshold model is |DU| or |DE|>10 mm/year (DU and DE are the vertical and the east-west deformation rates, respectively), with threshold levels of LSA set to high and very high susceptibility. The criterion of potential landslide zones is ((|DU| or |DE|>10 mm/year) ∩ (high or very high susceptibility of LSA)), and points with similar deformation and susceptibility are clustered by the K-means algorithm, and the potential landslide zones are obtained by elimination, smooth and speckle removal operations. The results showed that the InSAR two-dimensional deformation rates DU and DE were −32.71 − 12.72 mm/year and −14.88 − 24.81 mm/year, respectively, during 2015–2020 in Lanzhou city. The LSA showed that very low, low, medium, high, and very high susceptibility accounted for 55.36%, 10.54%, 21.37%, 9.63%, 3.1% of the total area, respectively. Using the proposed threshold model, 117 potential landslide zones were identified in Lanzhou. The overlap rate between potential landslide zones and the landslide inventory was 40.17%, indicating that about 40% of the potential landslide zones overlapped with the landslide inventory and that about 60% were new potential landslide zones in Lanzhou. The feasibility of the threshold model in identifying potential landslides was confirmed by field research and time-series InSAR analysis on typical areas (L1, L2, L3, and L4), which had large deformation variables and landslide features. The proposed method can quickly determine the spatial location of potential landslides, providing targeting data for landslide field investigations, technical support for rapid early landslide identification, and data support for landslide management and prevention in Lanzhou.

Influencing Physical Characteristics of Landslides in Kuala Lumpur, Malaysia

Landslide is one of the natural disasters that commonly occurs in terrestrial environments with slopes throughout the world. Located among countries with tropical climates, the hot and humid conditions expose Kuala Lumpur, Malaysia, to the risk of landslides. This paper aims to delineate the influencing physical characteristics of landslide occurrences in Kuala Lumpur. In this study, a 100 landslides historical data set and eight landslide factors were obtained from proper field validation and maps provided by those concerned in the government, such as distance to roads, distance to streams, elevation, slope angle, curvature, slope aspect, land use, and lithology. These factors were processed using GIS as geospatial analysis provides a useful tool for planning, disaster management, and hazard mitigation. By using ArcMap 10.8.2, a GIS software, different spatial analyses in which maps for each physical factor were layered with landslide events distribution. The weights for each factor were determined using the ANN approach resulting in the slope angle having the highest relative importance with a 100.0% value. In comparison, 8.3% represents the slope aspect as the most insignificant factor out of the eight selected characteristics for this study area. Therefore, a proper perspective and a thorough understanding of the certain slope condition have to be established for future mitigation action to support the agenda of SDG 15.

A Scenario-Based Case Study: Using AI to Analyze Casualties from Landslides in Chittagong Metropolitan Area, Bangladesh

Understanding the complex dynamics of landslides is crucial for disaster planners to make timely and effective decisions that save lives and reduce the economic impact on society. Using the landslide inventory of the Chittagong Metropolitan Area (CMA), we have created a new artificial intelligence (AI)-based insight system for the town planners and senior disaster recovery strategists of Chittagong, Bangladesh. Our system generates dynamic AI-based insights for a range of complex scenarios created from 7 different landslide feature attributes. The users of our system can select a particular kind of scenario out of the exhaustive list of 1.054 × 1041 possible scenario sets, and our AI-based system will immediately predict how many casualties are likely to occur based on the selected kind of scenario. Moreover, an AI-based system shows how landslide attributes (e.g., rainfall, area of mass, elevation, etc.) correlate with landslide casualty by drawing detailed trend lines by performing both linear and logistic regressions. According to the literature and the best of our knowledge, our CMA scenario-based AI insight system is the first of its kind, providing the most comprehensive understanding of landslide scenarios and associated deaths and damages in the CMA. The system was deployed on a wide range of platforms including Android, iOS, and Windows systems so that it could be easily adapted for strategic disaster planners. The deployed solutions were handed down to 12 landslide strategists and disaster planners for evaluations, whereby 91.67% of users found the solution easy to use, effective, and self-explanatory while using it via mobile.

Simulating Landslide Generated Tsunamis in Palu Bay, Sulawesi, Indonesia

The results of an extensive series of numerical experiments of the GNOM-LS model for modelling the physical and energy characteristics of tsunami waves generated by landslides are presented. Based on the published data on the tsunami on 28 September 2018 in Palu Bay, we analysed the sensitivity of the distribution of wave heights along the coastline formed by the landslide system, depending on the characteristics of these landslides and model parameters. The complexity of the work lies in the lack of a holistic picture of the initial information about landslides, their number and accurate measurement data on the height of the waves of the event. We attempted to restore these conditions by comparing numerical simulations for various initialisations of the landslide system with available observational data. It is revealed that the simulated system has a very high sensitivity to the initial conditions and characteristics of landslides. An essential task of the work is interpreting a complex picture of the nonlinear interaction of tsunami waves with minor changes in the initial characteristics of landslides. Based on the numerical simulation of single landslides and a complete system of landslides, an analysis of the complex structure of the nonlinear interaction of tsunami waves is carried out.

A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images

ABSTRACT Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing images (such as U-Net) have received a lot of attention. However, because of the variable shape and texture features of landslides in remote sensing images, the rich spectral features, and the complexity of their surrounding features, landslide extraction using U-Net can lead to problems such as false detection and missed detection. Therefore, this study introduces the channel attention mechanism called the squeeze-and-excitation network (SENet) in the feature fusion part of U-Net; the study also constructs an attention U-Net landside extraction model combining SENet and U-Net, and uses Sentinel-2A remote sensing images for model training and validation. The extraction results are evaluated through different evaluation metrics and compared with those of two models: U-Net and U-Net Backbone (U-Net Without Skip Connection). The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%, which is about 2% and 3% higher than U-Net and U-Net Backbone, respectively, with less false detection and more accurate extraction results.

Lightweight Landslide Detection Network for Emergency Scenarios

Landslides are geological disasters that can cause serious severe damage to properties and lead to the loss of human lives. The application of deep learning technology to optical remote sensing images can help in the detection of landslide areas. Traditional landslide detection models usually have complex structural designs to ensure accuracy. However, this complexity leads to slow detection, and these models often do not satisfy the rapid response required for the emergency monitoring of landslides. Therefore, we designed a lightweight landslide target detection network based on a CenterNet and a ResNet50 network. We replaced the BottleNeck in the backbone network of ResNet50 with a Ghost-BottleNeck structure to reduce the number of parameters in the model. We also introduced an attention mechanism module based on channel attention and spatial attention between the adjacent GhostModule modules to rich the landslide features. We introduced a lightweight multiscale fusion method in the decoding process that presented a cross-layer sampling operation for the encoding process based on Feature Pyramid Network. To down-sample from a low resolution to a high resolution and up-sample from a high resolution to a low resolution, thus skipping the medium-resolution levels in the path. We added the feature maps obtained in the previous step to the feature fusion. The Conv module that adjusts the number of channels in the multiscale feature fusion operation was replaced with the GhostModule to achieve lightweight capability. At the end of the network, we introduced a state-of-the-art Yolov5x as a teacher network for feature-based knowledge distillation to further improve the accuracy of our student network. We used challenging datasets including multiple targets and multiscale landslides in the western mountains of Sichuan, China (e.g., Danba, Jiuzhaigou, Wenchuan, and Maoxian) to evaluate the proposed lightweight landslide detection network. The experimental results show that our model satisfied landslide emergency requirements in terms of both accuracy and speed; the parameter size of the proposed lightweight model is 18.7 MB, namely, 14.6% of the size of the original CenterNet containing the ResNet50 network. The single image detection time is 52 ms—twice as fast as the original model. The detection accuracy is 76.25%, namely, 12% higher than that of the original model.

Revival Mechanism and Prevention Measures of Composite Landslides: A Case Study of the Wenma Expressway Composite Landslide

The resurrection of landslides often occurs in complex strata, where there are often multiple water-resisting zones and weak interlayers. The groundwater level has a significant influence on landslide stability and can lead to the formation of composite landslides and increase the probability of landslide resurrection. In a field investigation of the K39 +400 composite landslide of Wenma Expressway, the deformation characteristics of the landslide surface were obtained by analyzing 34 cracks on the landslide surface. The deep deformation characteristics of the landslide were analyzed by studying the deformation data obtained from deep borehole monitoring. The deformation zone characteristics of landslides were assessed by integrating surface and deep deformation data in the landslide area. The resurrections of shallow landslides in Area I and deep landslides in Area III were due mainly to the long-term high underground water level in the landslide. The stability of the landslide was calculated under various depths of drainage tunnel, and the results showed that the slope was in critical condition when the depth of the drainage tunnel was 15 m; the slope was basically stable when the depth of the drainage tunnel was 20 m and 25 m. When the depth of the drainage tunnel reached more than 30 m, the slope was in a stable state. Groundwater level was found to be the main factor affecting landslide deformation. This case study shows the importance of zoning the deformation characteristics of composite landslides, and the important influence of the groundwater level on landslide resurrection.

Reactivation mechanism and evolution characteristics of water softening-induced reservoir-reactivated landslides: a case study for the Three Gorges Reservoir Area, China

Reactivation mechanism and evolution characteristics of water softening-induced reservoir-reactivated landslides: a case study for the Three Gorges Reservoir Area, China

Comparison of earthquake-induced shallow landslide susceptibility assessment based on two-category LR and KDE-MLR

Geological hazards caused by strong earthquakes have caused continuous social and economic losses and destruction of the ecological environment in the hazard area, and are mostly manifested in the areas with frequent occurrence of geological hazards or the clustering of geological hazards. Considering the long-term nature of earthquakes and geological disasters in this region, this paper takes ten earthquake-stricken areas in Wenchuan earthquake zone as examples to collect shallow landslide data in 2010, combined with the spatial location of landslides and other factors. Kernel density estimation (KDE) method is used to analyze the spatial characteristics of shallow landslide. Taking the space of shallow landslide as the characteristic variable and fully considering the regulating factors of earthquake-induced landslide: terrain complexity, distance to river, distance to fault, distance to road, lithology, normalized vegetation difference index (NDVI) and ground peak acceleration (PGA) as independent variables, based on KDE and polynomial logistic regression (MLR), A quantitative model of shallow landslide in the earthquake area is constructed. The results show that: (1) PGA has the greatest impact on landslide in the study area. (2) Compared with the two-category logistic regression (two-category LR) model, the susceptibility map of landslide prediction results based on the KDE-MLR landslide susceptibility prediction model is more consistent with the actual situation. (3) The prediction accuracy of the model validation set is 70.7%, indicating that the landslide susceptibility prediction model based on KDE-MLR can effectively highlight the spatial characteristics of shallow landslides in 10 extreme disaster areas. The research results can provide decision-making basis for shallow landslide warning and post-disaster reconstruction in earthquake-stricken areas.

The formation mechanism of landslides in typical fault zones and protective countermeasures: A case study of the Nanpeng River fault zone

Landslides are frequent in the Nanpeng River fault zone in southwest Yunnan. Based on a Zhen Qing Expressway survey, the geological conditions and characteristics of landslides in this area were studied. The conclusions are as follows: 1) the geological background was the internal cause of local highly steep slopes and broken rock masses. The main landslide types in study area are fault-type and accumulation landslides. 2) The formation of fault-type landslides is related to endogenous causes, such as tectonic movements, but they are less easily triggered than accumulation landslides. Accumulation landslides are related to broken rock masses and are sensitive to external factors such as rainfall and artificial unloading. 3) The coordinated control of permanent support engineering, local deformation control, and overall support reinforcement should be considered as preventive measures.

Characteristics of landslides induced by an earthquake from a hidden strike-slip active fault in the Cianjur Area of West Java

On November 21, 2022, the Cianjur area in West Java, Indonesia, experienced devastating landslides caused by a magnitude 5.6 earthquake, with the epicenter traced to a hidden active fault approximately 11 kilometers beneath the surface, known as the Cugenang Fault. This study investigates the post-disaster landslides and aims to discover the characteristics of the landslides triggered by this earthquake. The methodology involves literature review, lithology descriptions, landslide slip surface analysis, soil analysis, slope assessment, land cover and land use change examination, landslide orientation evaluation, and distance measurements to the earthquake's epicenter and the Cugenang fault. Drones aided spatial landslide analysis. The study highlights the strong link between intense shaking along the active fault and landslide occurrences. Due to lithological bedding control, these landslides all head southwest and are located 0.35 to 0.67 kilometres from the fault. The residual soil, highly weathered breccia, and possible tuff volcanic deposits, which act as a sliding plane, lie in a similar direction to the slope, creating a vulnerable setting for landslides. The morphology and exposures of the Sarampad and Rawacina landslides showed similar phenomena to liquefaction. Land use changes, including slope cutting without proper stabilization measures, contribute to landslides to a certain degree.

InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River

Landslides have attracted extensive attention and research worldwide because of their tremendous destructiveness, and many catastrophic landslides have occurred along river corridors and reservoir bank slopes. The detection and detailed monitoring of potential landslides are prerequisites for landslide disaster prevention, with recent advance in spaceborne Interferometric Synthetic Aperture Radar (InSAR) having effectively addressed this challenge. In this study, a wide-area landslide detection and monitoring framework, combining multiple InSAR techniques, is proposed to investigate active landslides along the upper reaches of the Yellow River from Longyang Gorge to Liujia Gorge (UYRLL), north-west China. A total of 597 active landslides have been mapped in this region for the first time. Our analyses suggest that a large percentage of the detected landslides are distributed at an elevation of 2,000-3,000 m with slope angles of 5-30°and their spatial distribution density is correlated with regional tectonic activity. Multi-Temporal InSAR (MTI) techniques have also been adopted to analyze the surface motion characteristics of two typical landslides (i.e., the Lijia landslide and the Xijitan landslide), showing that these two landslides are dominated by linear motion. The landslide movements also contain slight non-linear oscillations, inferred from wavelet analysis to be associated with reservoir water area changes and seasonal rainfall. The proposed framework herein can be extended to the detection and monitoring of active landslides in other areas with similar geomorphic features, and the above findings on landslide characteristics can also contribute to landslide prevention and mitigation in the upper Yellow River area.

Extreme precipitation on dry ground in western Norway – characteristics of induced landslides call for adaptation of the Norwegian practice in landuse planning

Following a particularly dry summer, a torrential rain event struck Western Norway on Tuesday 30 July 2019. The resulting floods and shallow landslides caused one fatality and severe damages to public and private infrastructure in the former Jølster municipality. Building on earlier work, in which we identified characteristics of the shallow landslides induced by torrential rains on unsaturated soils, we here present suggestions for adaptation of the Norwegian practice in landuse planning.

Application of Smoothed Particle Hydrodynamics (SPH) for the Simulation of Flow-Like Landslides on 3D Terrains

Flow-type landslide is one type of landslide that generally exhibits characteristics of high flow velocities, long jump distances, and poor predictability. Simulation of its propagation process can provide solutions for risk assessment and mitigation design. The smoothed particle hydrodynamics (SPH) method has been successfully applied to the simulation of two-dimensional (2D) and three-dimensional (3D) flow-like landslides. However, the influence of boundary resistance on the whole process of landslide failure is rarely discussed. In this study, a boundary condition considering friction is proposed and integrated into the SPH method, and its accuracy is verified. Moreover, the Navier-Stokes equation combined with the non-Newtonian fluid rheology model was utilized to solve the dynamic behavior of the flow-like landslide. To verify its performance, the Shuicheng landslide event, which occurred in Guizhou, China, was taken as a case study. In the 2D simulation, a sensitivity analysis was conducted, and the results showed that the shearing strength parameters have more influence on the computation accuracy than the coefficient of viscosity. Afterwards, the dynamic characteristics of the landslide, such as the velocity and the impact area, were analyzed in the 3D simulation. The simulation results are in good agreement with the field investigations. The simulation results demonstrate that the SPH method performs well in reproducing the landslide process, and facilitates the analysis of landslide characteristics as well as the affected areas, which provides a scientific basis for conducting the risk assessment and disaster mitigation design.