Landslide Movement Research Articles

Two-Phase MPM Simulation of Surge Waves Generated by a Granular Landslide on an Erodible Slope

A high-speed debris flow sliding into a reservoir can cause a huge disaster. Consequently, predicting landslide movement accurately and its potential interaction with water is crucial. This paper developed a computational model based on a two–layer two–phase material point method (MPM) to simulate surge waves generated by granular landslides on an erodible slope. By comparing granular landslide on a rigid and erodible slope, the effect of the slope erodibility on the process of landslide movement and the waves generated is investigated. The model takes full account of the large deformations, fluidisation and settlement of granular material in soil–water interactions. The numerical model is validated by comparing the simulated results with experimental data. The influences of internal friction angle, density, elastic modulus, Poisson ratio and dilatancy angle on wave height are also studied. The validated model was then used to investigate the surge waves generated by dry and saturated granules sliding along a rigid and erodible slope. The results show that both the erodible slope and saturated granular slide can increase the first wave crest height generated by the landslide.

Critical area identification and dynamic process simulation for landslide hazard chain formation in the upstream Jinsha River

The upper reaches of the Jinsha River, with their complex terrain and active tectonic movements, are vulnerable to landslide-induced hazard chain events, which endanger the safety of residents and infrastructure construction. Based on the analysis of the development background of the hazard chain in the upstream area of the Jinsha River, five factors, including the lithology, distance to faults, distance to rivers, peak ground acceleration, and slope degree, were selected to identify the critical landslide-prone areas. Principal component and grey correlation analyses were then conducted to determine the contributions of these different factors. Based on ArcGIS, the study zone was categorized into five classes of landslide susceptibility: very high, high, moderate, low, and very low. The identification of the critical target areas for landslide hazard chain formation showed satisfactory accuracy. The very high- and high-susceptibility areas are concentrated along the Jinsha River. The dynamic process of a typical landslide in a very high-susceptibility area was numerically simulated using OpenLISEM. The high-precision Baige landslide data of the study area were used to calibrate the practicality of the input mass parameters, including cohesion, internal friction angle, D50, and D90. The movement and accumulation processes of a typical landslide were then numerically simulated with the verified data. The entire landslide accumulation covers an area of 0.45 km2, with a length of 1,600 m and a width of 270 m. Thus, the OpenLISEM model, which combines mass, topography, and landcover parameters, is feasible for the numerical simulation of landslide dynamic processes. The prediction of the dynamic processes and accumulation morphology of landslides can provide a reference for the formation processes and mechanisms of the landslide-induced hazard chain in the upper Jinsha River.

Mapping landslides through a temporal lens: an insight toward multi-temporal landslide mapping using the u-net deep learning model

ABSTRACT Repeated temporal mapping of landslides is essential for investigating changes in landslide movements, legacy effects of the landslide triggering events, and susceptibility changes in the area. However, in order to perform such investigations, multi-temporal (MT) inventories of landslides are required. The traditional approach of visual interpretation from cloud-free optical remote sensing imageries is time consuming and expensive. Recent endeavors exploring Convolutional Neural Networks and deep learning models have made rapid and accurate mapping of landslides feasible but have not been applied for multi-temporal landslide mapping in the Himalayas, yet. Earlier models used a standard supervised learning approach, with a small landslide inventory over a limited area used for training, which is then utilized to predict landslides in nearby areas. We propose a new strategy, using geographically separate training samples to design a standard approach which can be utilized to create multi-temporal landslide inventories. RapidEye images of 5-meters spatial resolution are used to generate MT landslide inventories in the study area of Rasuwa district, Nepal. We test the effectiveness of the model by training with only 55 landslides and predicting for a different area. Then, using the weights attained from this first training phase, we use transfer learning to map landslides over a time period between 2013 and 2019 in the Rasuwa district attaining an average F1-score of 0.69 for the study area. We also perform a spatial comparison between the manual (observed) and predicted inventories to evaluate the differences between landslide densities and overall landslide statistics of landslide area distribution. The benefit of a transfer learning-based model training is that it circumvents the need for generating annual inventories for training a deep learning. A single event-based inventory is enough to generate landslide inventories over a number of years, at least until landslide conditioning factors do not change significantly. This application can enable automated workflows to generate MT landslide inventories of particular areas as the basis for landslide evolution and movement change analysis.

Dynamics of creeping landslides controlled by inelastic hydro-mechanical couplings

Slow-moving landslides affect proximal infrastructures and communities, often causing extensive economic loss. While many of these landslides exhibit slow and episodic sliding for decades or more, they sometimes accelerate rapidly and fail catastrophically. Although it is known that the landslide dynamics are controlled by hydro-mechanical processes, few analytical models enable a versatile incorporation of the inelastic behavior of the shear zone materials, thus hindering an accurate quantification of how their properties modulate the magnitude and rate of coupled fluid flow and landslide motion. To address this problem, we develop a simulation framework incorporating rainfall-induced, deformation-mediated pore-water pressure transients at the base of active landslides. The framework involves the computation of two sequential diffusion processes, one within an upper rigid-porous landslide block, and another within the inelastic shear zone. Although the framework can be linked to any elastoplastic constitutive laws, here we model landslide motion through an elastic-perfectly plastic frictional model, which enables us to account for standard properties of earthen materials such as elastic moduli, friction angle, dilation angle, and hydraulic conductivity. Numerical case studies relevant to slow-moving landslides in the California Coast Ranges show that the proposed formulation captures different temporal patterns of movement induced by precipitation. In each of the case, we achieved a relatively accurate match between data and simulations by incorporating positive dilation coefficients, which leads to spontaneous generation of negative excess pore-water pressure and self-regulating motion. Conversely, simulations with no dilation (hence, reflecting the approach of critical state) produce sharp acceleration, typical of catastrophic runaway acceleration. Our findings encourage the use of the proposed framework in conjunction with constitutive laws tailored to site-specific geomaterial properties and data availability, thus favoring a versatile representation of the variety of creeping landslide trends observed in nature.

Numerical modeling of a potential landslide-generated tsunami in the southern Strait of Georgia

We report results of numerical simulations of a potential subaerial landslide on the coast of Orcas Island and the resultant tsunami waves in the southern Strait of Georgia near the US/Canada border. A likely trigger is strong ground shaking during large earthquakes on the nearby Holocene active Skipjack Island fault zone. For a worst-case scenario, we assume a 0.17 {textrm{km}}^3 rigid subaerial failure on the steep northeast coast of the island, spanning the sim 5 km between previous landslide deposits on the adjacent seafloor. The landslide motion and resulting tsunami generation are modeled using the three-dimensional (3D) non-hydrostatics physics-based NHWAVE model. The simulated failure moves downslope with a peak velocity of 13.64ṁ/s and travels 732 m before coming to rest after 85 s in 75-m water depth. Tsunami propagation is then continued using the 2D fully nonlinear and dispersive Boussinesq wave model FUNWAVE-TVD in a succession of layered and nested grids. The modeling reveals susceptible locations, particularly as waves will arrive with little or no warning. In the near-source region, modeled waves have peak amplitudes of 15–20 m, current speeds of up to 10 m/s, and runup of up to 30 m. Smaller, but significant, wave amplitudes and runup occur throughout the region surrounding Orcas Island. In the tsunami propagation direction, runup reaches 7.5 m at Neptune Beach near Lummi Bay. Both initial and reflected waves cause significant runup (> 1.5 m) along much of the shoreline between Point Roberts and Lummi Bay.

Numerical Study on the Influence of Block Physical Characteristics on Landslide Migration Using Three-Dimensional Discontinuous Deformation Analysis

The physical characteristics of blocks have an important impact on the migration and deposition in landslides, so the damage of landslides to the surrounding environment often has great uncertainty. To explore how the physical properties of blocks affect the landslide movement, we used Compaq Visual Fortran software with a DDA program to simulate the movement of a block landslide in our study. The velocity and location changes of two types of blocks (triangular and square blocks) were simulated by comparison. The contents of the simulation included the following points: (1) the influence of block density and weight on migration, (2) the influence of the elastic-slip characteristics and spring penalty coefficient on velocity, and (3) interaction between block vertices and the sliding surface. The results showed how the shape and physical properties of the blocks affect the migration of those blocks. Although the triangular blocks appeared to be more stable than the square blocks, they only exhibited greater stability on the gentle slope. The weight and elastic modulus of the blocks could improve the velocity and destructive force, whereas the spring penalty coefficient between the blocks and the contact surface had an obscure effect on the velocity. However, the changes of the above three parameters all led to non-linear changes of velocity. The simulation results indicated that the blocks with different numbers of edges and surfaces had complex trajectories because of contact frequency and mobility. The results show that the physical properties of the blocks could be one of the factors that could ultimately change the displacement of landslides.

A Non-Hydrostatic Model for Simulating Weakly Dispersive Landslide-Generated Waves

The aim of this study is to develop an efficient numerical scheme that is capable of simulating landslide-generated waves. The numerical scheme is based on the one-layer non-hydrostatic (NH-1L) model, a phase-solving model that can account for weakly dispersive waves. In this paper, the model is extended to include a time-varying solid bed. This NH-1L scheme is very efficient because, at each time step, only a tridiagonal Poisson pressure matrix needs to be solved. In this study, the capability of the NH-1L scheme to simulate landslide-generated waves is demonstrated by executing two types of landslide motion: constant speed and with acceleration and deceleration. Validation was performed using analytical solutions of the linear weakly dispersive (LWD) model, as well as experimental data. The NH-1L model was capable of describing the generation and propagation of water waves by a submarine landslide from relatively intermediate water to shallow water depths.

Landslide susceptibility evaluation based on active deformation and graph convolutional network algorithm

Disastrous landslides have become a focus of the world’s attention. Landslide susceptibility evaluation (LSE) can predict where landslides may occur and has caught the attention of scientists all over the world. This work establishes integrated criteria of potential landslide recognition and combines the historical landslides and newly-identified potential landslides to improve the accuracy, rationality, and practicability of a LSE map. Moreover, slope units can well reflect the topographic constraint to landslide occurrence and development, and Graph Convolutional Network (GCN) can well portray the topological and feature relation among various slope units. The combination of slope units and GCN is for the first time employed in LSE. This work focuses on Wanzhou District, a famous landslide-serious region in the Three Gorges reservoir area, and employs multisource data to conduct potential landslide recognition and LSE and to reveal the distribution characteristics of high landslide susceptibility. Some new viewpoints are suggested as follows. 1) The established criteria of potential landslide recognition consist of the characteristics of active deformation, stratum and lithology, tectonics, topography, micro-geomorphology, environment, meteorology, earthquakes, and human engineering activity. These criteria can well eliminate 4 types of false alarm regions and is successfully validated by field survey. 2) 34 potential landslides are newly discovered, and the movement of these potential landslides were controlled or induced by the combined action of soft-hard interbedding rock mass, steep topography, frequent tectonic movement, strong fluvial erosion, abundant precipitation, and intensive road and building construction. 3) The GCN algorithm reaches a relatively high accuracy (AUC: 0.941) and outperforms the other representative machine learning algorithms of Convolutional Neural Network (AUC: 0.926), Support Vector Machine (AUC: 0.835), and CART Tree (AUC: 0.762). 4) High landslide susceptibility is caused by the coupled action of weathered rock cavities, soft rock and swelling soil, strong river erosion, abundant rainfall, and intensive human engineering activity.

Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques

Many techniques are available for estimating landslide surface displacements, whether from the ground, air- or spaceborne. In recent years, Unmanned Areal Vehicles have also been applied in the domain of landslide hazards, and have been able to provide high resolution and precise datasets for better understanding and predicting landslide movements and mitigating their impacts. In this study, we propose an approach for monitoring and detecting landslide surface movements using a low-cost lightweight consumer-grade UAV setup and a Red Relief Image Map (a topographic visualization technique) to normalize the input datasets and mitigate unfavourable illumination conditions that may affect the further implementation of Lucas–Kanade optical flow for the final displacement estimation. The effectiveness of the proposed approach in this study was demonstrated by applying it to the Ruinon landslide, Northern Italy, using the products of surveys carried out in the period 2019–2021. Our results show that the combination of different techniques can accurately and effectively estimate landslide movements over time and at different magnitudes, from a few centimetres to more than several tens of meters. The method applied is shown to be very computationally efficient while yielding precise outputs. At the same time, the use of only free and open-source software allows its straightforward adaptation and modification for other case studies. The approach can potentially be used for monitoring and studying landslide behaviour in areas where no permanent monitoring solutions are present.

Propagation Modeling of Rainfall-Induced Landslides: A Case Study of the Shaziba Landslide in Enshi, China

Geological disasters, especially landslides, frequently occur in Enshi County, Hubei Province, China. On 21 July 2020, a large-scale landslide occurred in Enshi due to continuous rainfall. The landslide mass blocked the Qingjiang River, formed a dammed lake and caused great damage to surrounding roads and village buildings. In this study, the geomechanical properties of the landslide mass were obtained through field surveys. A three-dimensional topography model of the slope was established using the particle flow code (PFC) and the numerical parameters of the model were calibrated. A 3D discrete element model (DEM) was used to simulate the propagation of Shaziba landslide, and the dynamic behavior of the landslide was divided into five stages. The simulation results show that the landslide movement lasted approximately 1000 s. The maximum average velocity of the landslide reached up to 7.5 m/s and the average runout distance was about 1000 m. The simulated morphology of the landslide deposits was in good agreement with the field data. In addition, the influence of effective modulus on the calculation results was analyzed. The results indicate that the propagation behavior of a landslide and the morphology of landslide deposits are closely related to the effective modulus in the contact model of the PFC3D.

The Creep-Sliding Deformation Mechanism of the Jiaju Ancient Landslide in the Upstream of Dadu River, Tibetan Plateau, China

The Jiaju ancient landslide is a giant landslide located upstream of the Dadu River, eastern Tibetan Plateau, with a volume of approx. 7.04 × 108 m3. The Jiaju ancient landslide is complex and comprises five secondary sliding bodies, e.g., the Jiaju landslide (H01), Niexiaping landslide (H02), Xiaobawang landslide (H03), Niela landslide (H04), and Mt.-peak landslide (H05). Affected by regional neotectonic movement, heavy rainfall, river erosion, and lithology, the secondary sliding bodies of the Jiaju ancient landslide are undergoing significantly different creep-sliding deformation, which will cause great damage to villages, roads, and rivers around the sliding bodies. Combined with the SBAS-InSAR method, Sentinel-1A data from June 2018 to August 2021, remote sensing and field surveys, this study obtained the Jiaju ancient landslide deformation characteristics and deformation rate in the line-of-sight direction (VLOS), slope (VSlope), and vertical (VVertical). It is concluded that the maximum deformation rate of the Jiaju ancient landslide is significant. The maximum of VLOS, VSlope, and VVertical are −179 mm/a, −211 mm/a, and −67 mm/a, respectively. The Niela landslide (H04), Jiaju landslide (H01), and Mt.-peak landslide (H05) are very large and suffer strong deformation. Among these, the Niela landslide (H04) is in the accelerative deformation stage and at the Warn warning level, and the Jiaju landslide (H01) is in the creep deformation and attention warning level, especially heavy rainfall, which will accelerate landslide deformation and trigger reactivation. Because the geological structure is very complex for the Jiaju ancient landslide and strong neotectonic movement, under heavy rainfall, the secondary landslide creep-sliding rate of the Jiaju ancient landslide is easily accelerated and finally slides in part or as a whole, resulting in river blocking. It is suggested to strengthen the landslide deformation monitoring of the Niela landslide and Jiaju landslide and provide disaster mitigation and prevention support to the government and residents along the Dadu River watershed.

Numerical study on landslide dynamic process and its impact damage prediction to brick-concrete buildings, a case from Fenghuang street landslide in Shaanxi, China

The study of landslide dynamic process and impact damage has important theoretical and practical significance for landslide risk quantitative assessment. Taking Fenghuang Street landslide in Ningqiang County, Shaanxi Province, China as an example, the dynamic process of landslide and its damage to brick-concrete structure buildings are predicted by using discrete element method. Firstly, a three-dimensional numerical landslide model is established by means of the particle flow code system (PFC3D), which is based on landslide investigation, surveying, engineering exploration and geotechnical testing. Secondly, the whole process of landslide deformation, failure, movement and impact damage was simulated, and the velocity, displacement and impact force of the landslide in the motion process were quantitatively studied. Thirdly, the building model (brick-concrete structure) located at the foot of the landslide was constructed by PFC3D and finite element software (Midas/gen), respectively. The characteristics of deformation and displacement of the buildings after the landslide impact are analyzed, and the impact damage of the landslide is predicted. The results show that the rear edge of Fenghuang Street landslide first deforms and fails, and the leading edge is gradually pushed out. After the locking section of the front edge is broken, the landslide begins to slide as a whole, which is a typical push landslide. The main sliding time of the landslide is about 30 s, the maximum average velocity is 3.2 m/s, and the maximum displacement is about 40 m. After the landslide hits the building, the building is displaced in the moving direction of the landslide, and the wall of the building impacted by the landslide is destroyed, resulting in an collapse evident. The relevant research methodologies and findings in this paper can provide a reference for the risk assessment of the same type of landslides, especially the quantitative assessment of the vulnerability for the brick-concrete buildings at risk.

The development characteristics and mechanisms of the Xigou debris flow in the Three Gorges Reservoir Region

Debris flow is a common geological hazard in mountainous areas of China, often causing secondary disasters and seriously threatening residents and infrastructure. This paper uses the Xigou debris flow in the Three Gorges Reservoir Region (TGRR) as an example case study, the development characteristics and initiation pattern of which were analyzed based on field investigation. The disaster dynamics software DAN-W was then used to simulate the entire initiation-movement-accumulation process of the debris flow and conduct the debris flow dynamics analysis. The paper also simulated and predicted the movements of landslides in the formation area of a debris flow after its initiation. The results show that the movement duration of the Xigou debris flow was approximately 40 s, the maximum velocity was 37.1 m/s, the maximum thickness of the accumulation was 18.7 m, and the farthest movement distance was 930 m, which are consistent with the field investigation. When the volumes of landslide transformed into a new source material of debris flow are 5 × 104, 10 × 104, 15 × 104, 20 × 104, and 26 × 104 m3, the movement distances of the debris flows are 250, 280, 300, 340, and 375 m, respectively. When the volume of the source material exceeds 20 × 104 m3, debris flow movement can seriously impact the residential houses at the entrance of the gully. This paper can provide a scientific basis for the prevention and mitigation of the Xigou debris flow.

Anatomical growth response of Fagus sylvatica L. to landslide movements

Determining the age of landslide events is crucial for determining landslide risk, triggers, and also for predicting future landslide occurrence. Currently, the most accurate method for dating historical landslide events is dendrogeomorphic analysis. Unfortunately, the standard use of macroscopic growth responses of damaged trees for dating landslide activity suffers from many shortcomings. Thus, the aim of this study is to analyze in detail the growth response of trees to landslide movements at the anatomical level, a completely groundbreaking methodological approach. Ten specimens of European beech (Fagus sylvatica L.) were analyzed at two sampling heights, growing in two morphologically contrasting zones of the landslide area. Detailed anatomical analysis was focused on changes in morphometric parameters of the vessels and in the number of radial rays. The period (2008–2012) with the occurrence of the largest landslide movement (2010) recorded by long-term monitoring was analyzed. The results obtained revealed different anatomical responses in trees growing in different morphological zones of landslide. The tree responses on the ridge corresponded to the manifestations of tension wood formation, which corresponded to the stem tilting due to the landslide block movement. In the case of the trees in the trenches, root damage due to the subsidence of the landslide block blocked the flux of phytohormones, and their accumulation caused a significant reduction in the parameters of vessels and an increase in the number of rays. The study also includes recommendations in the future application of anatomical analyses in landslide research resulting from the obtained results. Thus, the obtained findings will improve the acquisition of chronological data for the purpose of landslide risk assessment.

Inclinometer and Improved SBAS Methods with a Random Forest for Monitoring Landslides and Anchor Degradation in Otoyo Town, Japan

Kochi Prefecture is located in an active zone of Japan that is frequently subjected to landslides due to heavy precipitation in typhoon seasons. Slow-moving landslides have been reported by both the local prefectural authorities and the National Government of Japan. We observed landslide movements in Otoyo Town by using ground- and satellite-based tools. Despite the high cost of establishing a borehole inclinometer survey to obtain accurate ground-based measurements, no previous InSAR study has been conducted in Otoyo Town, and the capacity for regional discrimination between active and inactive slow-moving landslides when using these tools remains unclear. We found that the horizontal velocity component was dominant at a rate of 21.4 mm/year across the whole of Otoyo Town. Satellite-based monitoring of ground-anchor efficiency may be possible in combination with ground-based inclinometer surveys. Three types of land cover are present in the study area—urban, field, and forests—and we selected a random forest (RF) model to extract low-coherence pixels by using optical and radar satellite sensors to identify important features and precisely remove pixels causing decorrelation. Long-term monitoring results from ground-based surveys, including inclinometer (boreholes) and anchor tension distribution data, were compared with the results of synthetic radar by using coherence-based small baseline subset (CB-SBAS) measurements. Generally, landslide occurrence was investigated across the whole of Otoyo Town, and we specifically evaluated the reliability of InSAR measurements in the Kawai landslide as a study site scale. The activity of the Kawai landslide channel was evaluated with borehole inclinometer displacement measurements (15.46 mm) and an anchor pressure survey (736 kN) from 2016 to 2019, as well as the steady state of the area (1.7 mm for the borehole inclinometer and 175 kN for the anchor pressure measurements), although a high cumulative precipitation of 3520 mm was reached during 2020 due to the ground anchor efficiency, which showed a consistent tendency with respect to the InSAR displacement measurements (14 mm during 2018 and 2019 and 0.7 mm during 2020). This comparison showed a consistent time-series displacement correlation, which was strengthened after introducing the RF mask into the analysis procedure, as the RF model correction reduced the standard deviation from the line-of-sight (LoS) average velocity estimation by 1.9 mm/year. Our research will help mitigate landslide impacts in Otoyo Town and its surroundings.

A modified friction-viscous solid boundary of the SPH method for landslide simulation

Bed friction characteristics are important factors controlling landslide movement processes. To address existing boundary condition deficiencies in describing the bed friction characteristics of landslide movements on complex terrain for the SPH method, a modified friction-viscous solid boundary condition is proposed. Within the solid boundary condition, the solid boundary is composed of line segments in 2D conditions and triangular segments in 3D conditions. The solid boundary is treated as a rigid material, and the SPH particle is treated as an elastic particle. The method used to calculate the values and directions of the contact, friction and viscous forces is presented. The proposed solid boundary condition is validated through water and granular material column collapse experiments. The dynamic process of a realistic landslide on complex terrain is simulated to demonstrate the applicability of the proposed solid boundary condition. Compared with the existing solid boundary condition, the proposed solid boundary condition has advantages in embedding friction model and wall function, exerting friction and viscosity force, keeping the stability of contact force, and establishing a complex terrain model.

Landslide modelling and susceptibility assessment by soil moisture sensors at Katteri watershed in Southern India

The goal of this investigation is to identify how disastrous occurrences affect the soil in watersheds. As a result, the focus of this investigation is the Katteri watershed in the Indian state of Tamil Nadu's Ootacamund region. The process was examined in the field and the lab using GIS advances and remote sensing. The momentum analysis was started in the Katteri watershed by creating several thematic guides using enrolment maps made with GIS technology that was appropriate for the request. Since most large-scale experiments could not have been conducted in the field, landslide movement at various slopes and precipitation levels was investigated using a lab model of a landslide zone. According to the investigation, more steep inclines with deeper soil are more likely to experience landslides, and precipitation is the primary trigger for these events. The model successfully implemented the use of Soil Moisture Sensors (SMS) as a landslide alarm structure. Avalanche or Landslide Alarm Mechanism may therefore be a useful technique for handling disastrous circumstances.

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.

Modeling determination of landslide pressure of Akhangaran reservoir ground masses

Analyzing the landslide situation is one of the urgent scientific and practical problems in determining the conditions for carrying out construction works and protecting human life and economic sectors from danger. Landslides can cause economic damage, disable large structures and roads, destroy large areas of crops, and destroy villages and cities. In this study, for Akhangaran reservoir lands, techniques for assessing slope stability, ground mass stability during landslides, and landslide pressure were established for the scenario when soils exhibit both frictional and viscous qualities. These approaches took into consideration the internal factors impacting landslides. The obtained results are based on the development of activities that suppress the factors affecting the movement of landslides.

WSN-Based Smart Landslide Monitoring Device

In this paper, we present a smart monitoring wireless sensor networking node device, referred to as ‘SMARTMODE’. The indigenously developed SMARTMODE is energy-efficient, context-adaptive, reliable, and accurate. We have custom-designed it to monitor landslide movement and causative parameters and to ensure the system’s longevity, reliability and robustness in harsh environmental conditions. When in sleep mode, the SMARTMODE is designed in such a way that it consumes less power (i.e., 0.034 mA at 4.2 V) while is still able to detect minor slope variations. This is achieved by enabling low-power interrupt-based sensing in the SMARTMODE. The SMARTMODE is also capable of automatically varying its data acquisition frequency based on the severity of the environmental context, thereby making it context-adaptive. Furthermore, we designed the SMARTMODE in such a way that it can detect and filter faults such as noise and outliers in the acquired sensor’s data. Additionally, to enhance the reliability, SMARTMODE comprises of energy harvesting and backup approach to deal with power outage issues. This approach enables automatic recharging of SMARTMODE’s battery even during nighttime to ensure reliable and continuous landslide monitoring. Finally, we evaluated the performance of the proposed SMARTMODE by deploying it in an actual landslide location.