Landslide Studies Research Articles

Regional-scale assessment of earthquake-induced slope displacement considering uncertainties in subsurface soils and hydrogeological condition

Realistic prediction of coseismic landslides is crucial for the design of civil infrastructures and geotechnical systems in seismically active regions. Coseismic landslides are complicated nonlinear and large-deformation processes triggered by ground shaking. To date, many gaps still remain in the scientific understanding of the landslides, particularly in areas lacking information on the subsurface stratigraphy. Analytical methods for estimating coseismic landslides have been based on highly simplified models, where many key influential factors have been overlooked, leading to large uncertainty in using empirical models for landslide prediction. In addition, more evidence recently underscores the necessity of modeling coupled effects between topography and soil amplification, which gives rise to complex wave propagation patterns due to scattering and diffracting of waves within the low-velocity near-surface layers. Neglecting topographic amplification and soil layers could result in significantly underpredicted regional-scale hazards of landslides. In this study, a regional-scale coseismic landslide simulation is carried out using the integral Spectral Element Method (SEM)-Newmark method for Hong Kong Island to quantify the coupled effect of 3D topography, subsurface soil condition as well as hydrogeological conditions. By leveraging extensive geological borehole data, a rigorous statistical model that characterizes uncertainties associated with subsurface soils is established. Influence of key factors that are not well considered in previous coseismic landslide studies, such as variation in irregular rockhead and spatially distributed shear-wave velocity are quantitatively analyzed. Numerical results demonstrate that by rigorously incorporating multiple sources of subsurface soil uncertainties, the total area of landslides in the study region can increase up to 30% compared with benchmark model as evidenced in the physics-based numerical simulation. These case studies will provide a valuable opportunity to revisit key factors that influence coseismic landslides using measured field data.

GIS-based study on the susceptibility of shallow landslides: a case study of mass shallow landslides in Sanming, Fujian in 2019

GIS-based study on the susceptibility of shallow landslides: a case study of mass shallow landslides in Sanming, Fujian in 2019

Litologiczny wskaźnik przemieszczeń materiału skalnego jako element oceny podatności stoków na osuwanie

The use of multi-temporal LIDAR data in the study of landslides gives many possibilities. The increasing-over-timeavailabilityofALSdataencouragedresearchondevelopmentoflandslidesusingDifferentialDigital Terrain Models. An important passive factor that determines the development of landslides (especially in the Carpathians) is lithology. The author propose a new lithological displacement index, that dscribes the volume of displaced ground (m3) per area of 1 m2 of a given lithological unit. The example of the test area of Ro¿nowskie Lake has shown that lithological displacement index can assess the landslide susceptibility, which is variable and depends on the amount of rainfall. During periods of heavy rainfall, large volumes of displaced rock masses were recorded in the thick-bedded formations. In dry periods, the trend is reversed.

N-BEATS deep learning method for landslide deformation monitoring and prediction based on InSAR: a case study of Xinpu landslide

N-BEATS deep learning method for landslide deformation monitoring and prediction based on InSAR: a case study of Xinpu landslide

Testing the performances of different fuzzy overlay methods in GIS-based landslide susceptibility mapping of Udi Province, SE Nigeria

Landslides are still wreaking havoc in many parts of the world. Several previous landslide susceptibility mapping (LSM) studies have proven that fuzzy logic methods provide reliable models for effective risk analysis and management. Various fuzzy operators are available for LSM. Globally, few studies conducted over a decade ago tested and compared the performances of several fuzzy operators in LSM and they revealed that the performances of the fuzzy operators vary due to a number of factors. Such comparative landslide studies have not been reported in Nigeria and Africa. In the current study, advanced mapping technologies were utilized in testing the performances of eight fuzzy operators in LSM of Nigeria's Udi Province, a region known for incessant soil erosion and landslide events. A total of 258 landslide locations were identified and used for area under curve (AUC) model validation performed using MATLAB and statistical analysis tools in Arc-Map software. Eleven landslide conditioning factors were chosen for the LSM. Correlations exist between the landslide locations and conditioning factors. Fuzzy PRODUCTandfuzzy ANDclassified the landslide susceptibility of the province to range from very low to moderate whereasfuzzy ORandfuzzy SUMcategorized the susceptibility to range from low to very high. However, all fourfuzzy gammas(0.8, 0.9, 0.95, and 0.975) classified the landslide risk to range from very low to high. All the fuzzy gammas and the fuzzy PRODUCT have the highest accuracy (AUC = 90.5%) compared to the fuzzy AND (AUC = 89.60%), fuzzy SUM (AUC = 89.10%), and the fuzzy OR (AUC = 86.7%). The findings and insights provided by this updated comparative study demonstrate that the fuzzy operators have global applicability. Thus, the study approach can be successfully used in other regions and related settings, for effective landslide mapping, monitoring, and management.

Review on the Geophysical and UAV-Based Methods Applied to Landslides

Landslides (LS) represent geomorphological processes that can induce changes over time in the physical, hydrogeological, and mechanical properties of the involved materials. For geohazard assessment, the variations of these properties might be detected by a wide range of non-intrusive techniques, which can sometimes be confusing due to their significant variation in accuracy, suitability, coverage area, logistics, timescale, cost, and integration potential; this paper reviews common geophysical methods (GM) categorized as Emitted Seismic and Ambient Noise based and proposes an integrated approach between them for improving landslide studies; this level of integration (among themselves) is an important step ahead of integrating geophysical data with remote sensing data. The aforementioned GMs help to construct a framework based on physical properties that may be linked with site characterization (e.g., a landslide and its subsurface channel geometry, recharge pathways, rock fragments, mass flow rate, etc.) and dynamics (e.g., quantification of the rheology, saturation, fracture process, toe erosion, mass flow rate, deformation marks and spatiotemporally dependent geogenic pore-water pressure feedback through a joint analysis of geophysical time series, displacement and hydrometeorological measurements from the ground, air and space). A review of the use of unmanned aerial vehicles (UAV) based photogrammetry for the investigation of landslides was also conducted to highlight the latest advancement and discuss the synergy between UAV and geophysical in four possible broader areas: (i) survey planning, (ii) LS investigation, (iii) LS dynamics and (iv) presentation of results in GIS environment. Additionally, endogenous source mechanisms lead to the appearance of deformation marks on the surface and provide ground for the integrated use of UAV and geophysical monitoring for landslide early warning systems. Further development in this area requires UAVs to adopt more multispectral and other advanced sensors where their data are integrated with the geophysical one as well as the climatic data to enable Artificial Intelligent based prediction of LS.

Pre-landslide topographic reconstruction in Baetis Chaos, mars using a CaSSIS Digital Elevation Model

Planview detailed morphological analysis of martian landslides is usually performed using orbital imagery such as from the ConTeXt camera (CTX) at 6 ​m/pix, the Colour and Stereo Surface Imaging System (CaSSIS) at 4.5 ​m/pix or the High-Resolution Imaging Science Experiment (HiRISE) at 0.25–0.5 ​m/pix. However, topographic information is key to fully understand a landslide's formation mechanism and its mobility, by estimating the material volumes mobilised and the spatial distribution of erosion and deposition. Digital Elevation Models (DEM) are required to carry out these analyses; nevertheless, there is currently a gap in landslide-volume studies between those using Mars Orbiter Laser Altimeter (MOLA) dataset at ∼450 ​m/pix or HRSC at 50–200 m/pix and those using HiRISE data at 1–2 m/pix, which is only partially filled by CTX elevation data at ∼20 ​m/pix. The CaSSIS camera on board the ESA/Trace Gas Obiter (TGO) can be used to produce DEMs, but so far, such data have not yet been used to conduct a landslide volume analysis. Here, we use three reconstruction methods (semi-automatic, morphology-based and tilted) on a CaSSIS DEM to estimate the initial topography and hence the volume and the distribution of erosion and deposition of a 6 ​km long landslide in Baetis Chaos. Despite the complex topography of the surrounding area due to the presence of an ejecta deposit beneath the landslide, we were able to estimate the landslide's volume and mass distribution. Using a tilted plane as part of estimating the initial topography produced the best results. We evaluated the success by considering the quantifiable balance between erosion and deposition (given the uncertainties) and more subjectively by considering whether the volume distribution matched with what was expected based on the morphology in images alone. Therefore, we recommend the use of this method for individual landslide studies in complex topography where detailed knowledge of the deposit-thickness distribution is required. The semi-automatic reconstruction method produces satisfactory volume estimates and would be better suited to studies where hundreds of landslides are present. We found that CaSSIS data can be used to successfully conduct such analyses, providing additional DEM coverage to study martian medium-scale landslides or other landforms of similar scale (5–15 ​km) with the notable benefit that it provides single-pass stereo image acquisition. • Digital Elevation Model are needed to estimates landslide's material volumes and distribution in order to fully understand their mobility. • CaSSIS (Colour and Stereo Surface Imaging System) DEM have not yet been used to conduct such landslide analysis. • Three reconstruction on a CaSSIS DEM have been used to estimate the volume and the material distribution of a landslide in Baetis Chaos on Mars. • CaSSIS data can be used to conduct analysis and provide additional coverage to study medium-scale landslides or other landforms (scale 5–15 km).

A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset

As artificial intelligence (AI) techniques are becoming more popular in landslide modeling, it is important to understand how decisions are made. Fairness, and transparency becomes ever more vital due to ethical concerns and trust. Despite the popularity of machine learning (ML) algorithms in landslide modeling, the explainability of these methods are often considered as black box. This paper aims to propose an explainable artificial intelligence (XAI) for landslide prediction using synthetic-aperture radar (SAR) time-series data, NDVI (normalized difference vegetation index) time-series data and other geo-environmental factors such as DEM (digital elevation model) derivatives. We employed a Shapley Additive Explanations (SHAP) approach to understand how and what decisions ML-based models are making. 37 features were extracted from various sources such as ALOS-PALSAR (ALOS Phased Array type l-band Synthetic Aperture Radar), ALOS-2 (SAR), Landsat-8, topographic maps, and DEM for landslide susceptibility mapping in a landslide prone area in Chukha, Bhutan as a test site. The result was then compared using two standard ML methods: random forest (RF) and support vector machine (SVM). As per results, the RF model outperformed (0.914) the SVM. Moreover, the higher reliability of the RF model was proved by the area under the curve (AUC) of 0.941. XAI results revealed, features like altitude, aspect, NDVI-2014, NDVI-2017, and NDVI-2018 were the most effective features for landslide prediction by both models. Interestingly, among those features, NDVI-2014, aspect, and NDVI-2017 negatively correlated with the landslide prediction; whereas positively correlated when SVM was utilized. This interpretation ability indicates the advantages of XAI over the conventional methods as it measures the impact, interaction and correlation of conditioning factors within a model. The current research finding can provide more transparency and explainability when working with MLs in landslide studies. This could help to build trust among the geoscientists and decision-makers while making geohazard prediction.

The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy)

Shallow landslides due to the soil saturation induced by intense rainfall events are very common in northern Italy, particularly in the Alps and Prealps. They are usually triggered during heavy rainstorms, causing severe damage to property, and sometimes causing casualties. A historical study and analysis of shallow landslides and mud-debris flows triggered by rainfall events in Lombardy was carried out for the period of 1911–2010, over an area of 14,019 km2. In this study, intensity–duration rainfall thresholds have been defined using the frequentist approach, considering some pedological characteristics available in regional soil-related databases, such as the soil region, the textural class, and the dominant soil typological units (STU). The soil-based empirical rainfall thresholds obtained considering the soil regions of the study area were significantly different, with a lower threshold for landslide occurrence in the soil region M1 (Alps), where soils developed over siliceous parent material, with respect to the whole study area and the soil region M2 (Prealps), where soils developed over calcareous bedrocks. Furthermore, by considering textural classes, the curves were differentiated, with coarse-textured soils found more likely to triggerlandslides than fine soils. Finally, considering both texture and main soil groups, given the same rainfall duration, the rainfall amount and intensity needed to initiate a landslide increased in the following order: “coarse-skeletal” Cambisols < Umbrisols < Podzols < “fine” Cambisols. The results of this study highlighted the relevant role of pedological conditioning factors in differentiating the activation of rainfall-induced shallow landslides in a definite region. The information on soils can be used to define more precise rainfall–pedological thresholds than empirical thresholds based solely on meteorological conditions, even when they are locally defined. This knowledge is crucial for forecasting and preventing geo-hydrological processes and in developing better warning strategies to mitigate risks and to reduce socio-economic damage.

Dynamic process, influence, and triggering mechanism of slope remodelling by landslide clusters in the South Jingyang Tableland, China

Recently, the concept of landslide path dependence has been proposed and well confirmed in landslide studies in central Italy and the Nepal Himalaya, providing a novel perspective for understanding landslide activity patterns and assessing susceptibility. Previous studies have shown that landslides in some loess tableland regions of China have obvious clustering effect, but whether landslides in these regions also have path dependence have not been well discussed. From the perspective of landslide path dependence, multidisciplinary survey methods (including remote sensing, satellite interpreting and monitoring, geographic information system mapping, terrestrial laser scanner survey, electrical resistivity tomography survey, remote online monitoring, physical experiments, numerical simulations, mathematical statistical analysis, and geomorphic change detection, etc.) were jointly applied in the study of landslide clusters in the South Jingyang Tableland, Shaanxi Province, China. We reveal the temporal-spatial distribution and deformation law of landslide clusters in the study area, analyze the effect of consecutive landsliding on the geomorphic evolution of the loess tableland and its triggering mechanism, and find the evidence of the existence of landslide path dependence in loess environment. The results show that the rise of groundwater level caused by human activities is the main culprit for the landslide clusters formation in the study area. Different path dependent models (reactivation or continuation landsliding, local activation landsliding, and remote activation landsliding) repeatedly acted alone or interacted with each other, leading to the formation of large-scale landslide clusters, and continuously remodeled the landscape of the loess tableland, thus accelerating the evolution process of the loess tableland. This study provides a scientific basis for further understanding the distribution law, sliding mode, clustering effect and formation mechanism of landslide clusters in loess tableland.

Application of geographical information system (GIS) using artificial neural networks (ANN) for landslide study in Langat Basin, Selangor

The landslide was recognized as the most common geologic hazard around the world. The assessment of the relationship landslide conditioning factors is a critical step in managing landslide hazards and risks. Several models have been made to develop the landslide model in recent years. The Artificial Neural Networks (ANN) model was used in this study to develop a landslide model and to identify the most important landslide conditioning factors. Eight conditioning factors, including elevation, slope, aspect, curvature, lithology, soil series, Topographic Wetness Index (TWI), and rainfall, were selected and analyzed using the Geographical Information System (GIS) approach. The multilayer perceptron module and one hidden layer method extracted weighted conditioning factors. The landslide model was validated using the area under the curve (AUC) method. This model validation showed a success rate for training and testing is 0.876, respectively. This study found curvature is the most crucial factor affecting landslide occurrence in the Langat Basin with a 0.213 weight index, followed by rainfall (0.143) and elevation (0.141). Finally, the landslide model can be used as an indicator to identify the most important landslide conditioning factors and assess the relationship between these factors and landslide occurrences.

Study of Sustainable Techniques for Effective Risk Management and Control: A Review

Abstract: A geological disturbance is a broader term that refers to a variety of ground motions such as rock falls, slope failure, and shallow debris flows. The study of landslides has fetched a lot of interest recently, owing to a growing awareness of the socioeconomic consequences associated. The land in the mountainous region is especially susceptible to landslides due to its complicated geological context. Landslides are generally caused by the slope changing from a stable to an unstable condition, which can be caused by a variety of factors such as pore water pressure destabilising the slopes, loss or absence of vertical vegetative structure (after a wild fire or fire in forests lasting two or four days), erosion of toe of slopes by river, significant rain falls, earthquakes causing liquefaction of slope, blasting, earth work which alters the slope and imposes new load on it. If the slope is not stable and must support a large soil mass, the entire wedge may slide, resulting in the second type of landslide. Although climate change has also contributed to the occurrence of such disasters like landslides. As we all are aware about the relative causes of landslides, still it is a major concern for governmental organisations, authorities, geotechnical engineers and sub-urban planners for both public and structural safety. Landslides are a major issue in hilly areas especially in Himalayan region of India, where populace is more dependent on native resources and are prone to landslides too.

Regional-scale modeling of rainfall-induced landslides under random rainfall patterns

The importance of rainfall patterns in triggering landslides has been recognized worldwide. In this study, the triggering and evolution of rainfall-induced landslides were investigated by a regional-scale physical model based on one-dimensional Richard equation and infinite slope model, where a bounded random cascade model (BRCM) was adopted to generate random rainfall patterns (RRPs) for rainfall events derived from extreme value analysis. The results indicated that the RRPs affect the evolution of triggered landslide and the cumulative landslide area. The shape of RRPs determined the time evolution of triggered landslide. Due to the randomness of rainfall peaks under RRPs and limited hydraulic conductivity of soil, an upper bound existed for the cumulative landslide area under RRPs and the dispersion of the cumulative landslide area increased with an increase in the return period. This study aids in the understanding of the diversity and discipline of the evolution of rainfall-induced landslides.

Machine learning and landslide studies: recent advances and applications

Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies.

Ground and Satellite-Based Methods of Measuring Deformation at a UK Landslide Observatory: Comparison and Integration

With the advances of ESA’s Sentinel-1 InSAR (Interferometric Synthetic Aperture Radar) mission, there are freely available remote sensing ground deformation observations all over the globe that allow continuous monitoring of natural hazards and structural instabilities. The Digital Environment initiative in the UK aims to include these remote sensing data in the effort at forecasting and mitigating hazards across the UK. In this paper, we present a case study of the Hollin Hill landslide in North Yorkshire where a variety of ground-based geophysical measurements are available for comparison with InSAR data. To include Sentinel-1 data in the UK’s Digital Environment, it is important to understand the advantages and limitations of these observations and interpret them appropriately. The Hollin Hill landslide observatory (HHLO) is used by the British Geological Survey to understand landslide processes, and to trial new technologies and methodologies for slope stability characterisation and monitoring. In July 2019, six corner reflectors were installed to improve the coherence of the InSAR measurements. We use Sentinel-1 InSAR data acquired between October 2015 and January 2019 to study the behaviour of this landslide, and find that the line-of-sight component of the down-slope movement is 2.7 mm/year in the descending track, and 7.5–7.7 mm/year in the ascending track. The InSAR measurements also highlight the seasonal behaviour of this landslide. Using InSAR data after the installation of the six corner reflectors, we are able to track the most recent movement on the landslide that occurred in January 2021. This result is in agreement with other ground-based measurements such as tracking of pegs, and soil moisture data derived from electrical resistivity tomography.

InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation—A Case of Xiaojiang River Basin, China

Landslides, a major natural geohazard, obstruct municipal constructions and may destroy villages and towns, at worst causing significant casualties and economic losses. Interferometric Synthetic Aperture Radar (InSAR) technique offers distinct advantages on landslide detection and monitoring. In this paper, a more systematic workflow is designed for InSAR study of landslides, in terms of three levels: (i) early detection on regional scale, (ii) three-dimensional (3D) surface displacement rates estimation on detailed scale, and (iii) time series analysis on long-term temporal scale. The proposed workflow is applied for landslide research on the Xiaojiang River Basin, China, using ascending and descending Sentinel-1 images acquired from March 2017 to May 2019. First, the landslide inventory has been mapped and updated using InSAR stacking method, supporting geohazard prevention on a regional scale. A total of 22 active landslides are identified, ranging from medium to super large scale. Compared with the existing inventory, three unrecorded landslides are newly detected by our approach, and five recorded landslides are detected significant expansion of their boundaries. Then, specific to a detected landslide, Baobao landslide, a Total Least Squares–Kalman Filter-based approach is presented. Two outcomes are provided for further spatial-temporal pattern analysis: 3D displacement rates, providing an intuitive insight on the spatial characteristics and sliding direction of landslide, which are analyzed to deep the understanding of its kinematic mechanism, and long-term time series, which contribute to deduce the dynamic evolution of landslide, presenting benefits in landslide forecasting.

Multiscale nonlinear analysis of failure mechanism of loess-mudstone landslide

The sliding mechanism of loess landslides is unique and complex; a single-scale and linear mechanical model cannot describe their failure process scientifically, thus necessitating a multiscale and nonlinear research idea. In order to monitor and observe the whole failure process of loess landslide more conveniently, a physical model test (2.0 × 0.6 × 1.5 m) is designed to simulate the landslide failure process under rainfall condition. Twelve pore water pressure sensors and twelve soil pressure sensors were arranged in three loess layers, and were used to record the numerical variation characteristics of pore water pressure and soil pressure during the failure process of Landslide physical model. The results show that, the failure process of loess landslides and the data from sensors are very random, making it impossible to use a single-scale or linear mechanical model to analyze their mechanics. However, we can well describe the failure process of loess landslide through the micro, meso, and macro scales. Therefore, the mechanical problems of loess landslides should be analyzed from six aspects, including the liquid bridge, force chain, soil mechanics test, a physical model, real landslides, and tectonic stress. In the future, nonlinear and multiscale research ideas can make loess landslide studies more scientific and effective.

Integrated numerical study of rainfall-induced landslides, from initiation to propagation using saturation-based rheological model

Integrated numerical study of rainfall-induced landslides, from initiation to propagation using saturation-based rheological model

Scenario simulation of the geohazard dynamic process of large-scale landslides: a case study of the Xiaomojiu landslide along the Jinsha River

Large-scale landslides often cause severe damage due to their long run-out distances and having disaster chain effects. Scenario simulation has been adopted in the current work in order to analyze the Xiaomojiu landslide dynamic processes. The landslide characteristics and topography data are obtained via field investigations, whereas high-resolution topographic data (0.17 m) are obtained using an Unmanned Aerial Vehicle. The landslide sliding velocity, deposition characteristics, and flood outburst after a landslide dam failure were obtained using Particle Flow Code (PFC-3D) which introduced the changeable friction coefficient and the HEC-RAS software. The results showed that: 1. The landslide presents a scallop shape with a length of 1110 m, an average width of 950 m, and an area of 1.05 × 106 m2. The average thickness and volume of the sliding body are approximately 50 m and 5.45 × 107 m3. The InSAR (Interferometric Synthetic Aperture Radar) deformation analysis showed that the Xiaomojiu landslide has a maximum annual displacement rate of 60 mm/y and a maximum accumulation deformation of 180 mm since November 25, 2017. 2. The failure process of the Xiaomojiu landslide lasted for 65 s with a maximum velocity of 78.2 m/s. According to the landslide simulation results, the deposited area is approximately 2023 m long, 900 m wide, and has a maximum height of approximately 149 m. 3. A landslide-dammed lake with an elevation of 2940 m and a storage capacity of 4.13 × 109 m3 is formed after the landslide blocks the Jinsha River, and the maximum peak flow rate of the breach is 12051.7 m3/s, 43,451.4 m3/s, 148,635.6 m3/s, and 304,544.7 m3/s for the landslide-dammed failure degrees of 15%, 25%, 50%, and 75%, respectively. These results provide a reference for the risk analysis and mitigation of the landslide.

UAV Survey for Landslide Hazard Assessment in the Former Min-Kush Uranium Processing Site

In March 2013 a large landslide of almost one million cubic meters happened in the Min-Kush uranium processing site in the Kyrgyz Republic. This hazardous landslide area is connected to radioactive and toxic tailings of mining industry located in the center of Tien-Shan mountain system. The present research represents the results of work done by using Unmanned Aerial Vehicle (UAV) for photographing the landslide area, which was made for monitoring of this potentially hazardous phenomenon. Based on these images and geodetic measurements of the landslide, orthophotos as well as generated Digital Surface Model (DSM) with an accuracy of less than ±5 cm resulted. The application of the UAV in the man-made landslide study has been established as a cost and time effective monitoring tool that provides researchers and engineers with high-resolution geospatial data.