Landslide Studies Research Articles

Evaluation of wave-induced instability of nearly saturated sandy slopes under partially undrained condition: A case study of landslide in Fraser River delta front

A significant amount of Methane gas in soil deposits at Fraser River Delta (FRD), Canada, was identified as it adversely impacted the stability of the sloping seabed in the area under waves and tides. In this study, an integrated FEM model is utilized to investigate the instability of FRD front due to 1985 storm waves. Despite earlier studies that focused only on the role of residual pore pressure reached at late wave cycles in inducing the slide of FRD front (using empirical correlations), the transient response of sediments at early wave cycles is considered herein. The effect of coupled-flow-and-deformation processes within the sediments on the extent of the sliding plane is considered. For wave-seabed interaction modeling, Biot’s poroelasticity and linear wave theory are employed to represent the fluid and seabed domains, respectively. The slide evaluation of the sloping seabed is conducted using Strength Reduction Technique with Mohr-Coulomb failure criterion. The results indicate that the storm waves cause a shallow slide within the slope, and the proximity of the unsaturated layer to mudline exacerbated the instability condition. Moreover, instantaneous liquefaction zones are found to emerge away from the slope; thus, they may not lead to an exacerbated sliding of the slope.

Geographical Information System (GIS)-Based Landslide Susceptibility Mapping in Malaysia: A Review of Past, Current and Future Trends

Landslides are very serious natural disasters that cause much damage and threaten human lives. Landslide susceptibility mapping studies are very common in landslide assessments. Geographic information systems (GISs) have been widely used in many fields, especially in landslide studies. This paper provides a review of GIS-based landslide susceptibility mapping in Malaysia from 2000 to 2022 and analyses the number of publications, study areas involved, parameters and models applied in this field over the past 22 years. The number of publications based on the topic shows an increasing trend, with an average of 2 articles published every year. This paper focused on Malaysia’s territory only. The study areas involved in landslide susceptibility studies are Penang, Perak, Selangor, Sabah, Sarawak, Pahang, Kelantan and Kuala Lumpur. Pahang is the study area of the most articles (10), followed by Penang (8). Moreover, 21 parameters were identified, and the slope was the most used parameter (33 articles). Twenty-five models were used, and logistic regression was the most used model, followed by the frequency ratio, ANN and weighted overlay methods.
 
 Received: 24 January 2023 / Accepted: 22 April 2023 / Published: 5 May 2023

Coupling Progressive Deep Learning with the AdaBoost Framework for Landslide Displacement Rate Prediction in the Baihetan Dam Reservoir, China

Disasters caused by landslides pose a considerable threat to people’s lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a challenge in the study of landslides. Lately, ensemble deep learning algorithms have shown promise in delivering a more precise and effective spatial modeling solution. The core aims of this research are to explore and evaluate the prediction capability of three progressive evolutionary deep learning (DL) techniques, i.e., a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) ensemble AdaBoost algorithm for modeling rainfall-induced and reservoir-induced landslides in the Baihetan reservoir area in China. The outcomes show that the ensemble DL model could predict the Wangjiashan landslide in the Baihetan reservoir area with improved accuracy. The highest accuracy was achieved in the testing set when the window length equaled 30. However, assembling two predictors outperformed the accuracy of assembling three predictors, with the mean absolute error and root mean square error reaching 1.019 and 1.300, respectively. These findings suggest that the combination of strong learners and DL can yield satisfactory prediction results.

A Comparative Analysis for Defining the Sliding Surface and Internal Structure in an Active Landslide Using the HVSR Passive Geophysical Technique in Pujilí (Cotopaxi), Ecuador

Geophysical techniques were employed to analyze one of the landslides that affected the main access road to Pujilí (Ecuador). A passive seismic technique was utilized to test a total of 70 horizontal to vertical spectral ratio (HVSR) points, complemented by an active seismic-refraction profile and a multichannel analysis of surface waves (MASW) survey. The results from the active geophysical surveys facilitated the determination of the shear-wave-velocity value for the surface materials that were in motion. However, the HVSR provided the fundamental frequency fo and amplification Ao values of the ground. The Nakamura (1989) relationship was applied to obtain the thickness of the sediments over a compact material from the fundamental frequency of the terrain in a two-layer model. Additionally, constrained models of the shear-wave velocity (Vs) distribution in the landslide area, obtained from the active seismic surveys, were used to invert the ellipticity curves. The results from this inversion were compared with those obtained by applying the Nakamura equation. The landslide-rupture surface was delineated for each type of analysis, which verified the correlation and minimal differences between the results of the three proposed studies, thus validating the procedure. The directivity of the microtremor HVSR signals was also analyzed, demonstrating a relationship with the internal structure of the sliding material. Furthermore, the ability to slide concerning the Kg parameter (vulnerability index, Nakamura, 1989) was studied. The usefulness of the directivity analysis in defining the internal structures in landslide materials and in determining the areas with the most significant instability was demonstrated. Overall, the HVSR is considered valuable when conducting early landslide studies and is helpful in determining the rupture plane while remaining a simple, fast, and economical technique.

Dynamic Process Study of Earthquake-Induced Landslides Applying an Improved Discontinuous Deformation Analysis Method Considering Site Response

Dynamic Process Study of Earthquake-Induced Landslides Applying an Improved Discontinuous Deformation Analysis Method Considering Site Response

The longest tree-ring based chronology of mass movements in Central Europe and their meteorological triggers

Knowledge of meteorological triggers of mass movements is crucial for determining the degree of hazards, but also for predicting their occurrence. Dendrogeomorphic methods (tree-ring based) have repeatedly provided data on historical landslide activity as a basis for detailed trigger analysis. However, the construction of long dendrogeomorphic chronologies encounters limits in the sensitivity of growth disturbances in trees as well as their age dependence. Moreover, accurate meteorological instrumental data usually do not cover the entire length of long tree-ring based chronologies of landslide movements. To resolve these uncertainties, this study has compiled the longest tree-ring-based chronology of mass movements in Central Europe for Mt. Kněhyně in Outer Western Carpathians, spanning more than a quarter of a millennium and based on 228 tree-ring series of disturbed individuals of Norway spruce (Picea abies (L.) Karst.). The resulting chronology is a combination of two sub-chronologies that we constructed from different tree growth disturbances (reaction wood and tree-ring eccentricity), combining the advantages of both approaches. To identify potential meteorological triggers, we combined instrumental data from the nearest meteorological station together with reconstructed data from the wider landslide study area and documentary records. This gave us a uniquely long overlap of the two datasets across the full length of the mass movement chronology, allowing for more robust results compared to significantly shorter overlays. The studied mass movements followed up to three years of above-mean precipitation and were immediately triggered by short (several days) precipitation extremes. Snowmelt lasting several days to weeks in selected cases further modified this pattern.

Ultrahigh-resolution shear-wave reflection imaging of vertical-component data in a quick-clay prone to landslide area in southwest Sweden

We have investigated the potential of ultrahigh-resolution seismic reflection imaging of vertical-component data to identify hazardous conditions in a quick-clay landslide area in southwest Sweden. A high-fold survey with a fine shot and receiver spacing has been acquired using a 5 kg sledgehammer, vertical hit, with the objective of producing a high-quality unaliased shear-wave reflection section retrieved from vertical-component data. On the receiver side, 1C wireless recorders are deployed in a fixed geometry juxtaposed to a landstreamer of 3C-microelectro mechanical system (MEMS)-based units, which recorded in a roll strategy to cover most of the profile length. Particle motion analysis of main reflections has been conducted on the 3C MEMS sensors, given their side-by-side location with the wireless recorders, to inspect the wave polarization. Distinct shear-wave reflections in an extremely slow shear velocity medium (70–100 m/s) imply a vertical super resolution of 1–2 m down to a bedrock level of 40 m deep. Interestingly and as shown in earlier studies, quick clays appear to overlie the coarse-grained horizons justifying why their high-resolution imaging can be significant for quick-clay landslide studies and a better understanding of potential risks. Two shallow reflections are delineated through a combination of first-break traveltime tomography for P and S waves and reflection seismic processing for P- and S-reflections of the vertical-component data. After seismic imaging and borehole data correlation, the shallowest reflection is interpreted to be from coarse-grained materials interbedded within normally consolidated clays and the deepest one from an undulating bedrock. Interpretations of the S-S processing outcomes indicate that the coarse-grained layers are not homogeneous because of the generation of strong shear-wave diffractions, suggesting the presence of either large boulders or patchy sandy-silty conditions.

Deformation and failure mechanism of colluvial landslide under sustained rainfall-a case study of Xinzhan landslide in Tongzi County, China

The failure mechanism of Xinzhan landslide were investigated systematically based on multiple-integrated geotechniques, including field investigations, monitoring, and numerical simulations. Xinzhan landslide is a typical colluvial landslide, composed of gravelly soil in upper part and sandstone interbedded with mudstone in underlying part. The sliding surface is closing to the soil-rock interface with average depth about 8 m and the main sliding direction 287°. The occurrence of Xinzhan landslide is attributed to its unique geological environment and sustained rainfall. The hydro-mechanical coupling results indicate that under sustained rainfall, the rainwater infiltrates into gravelly soil, rising the pressure and forming transient saturation zone near the soil-rock interface, where the strength parameter is weakened under effect of water. As a consequence, the plastic deformation is intensified. Additionally, the deformation evolution trend of landslide under different rainfall intensities is also studied. The results show that the greater rainfall intensity, the shorter rainfall duration required for the landslide to produce accelerated deformation. The rainfall intensity-duration curve is used to construct the critical rainfall early warning criterion of Xinzhan landslide. The R2 of the fitting formula is 0.971, which indicates that the formula can effectively serve the early warning and provide convenience for highway safety construction.

Application of airborne laser scanning and electrical resistivity tomography in the study of an active landslide and geology of the cliff, Jastrzębia Góra, Poland

The cliff in Jastrzębia Góra is one of the most an active landslide areas along the Polish Baltic coast. The aim of these studies was to determine the dynamics of displacements in an active landslide and to identify the geology of the cliff. Two methods, ALS (Airborne Laser Scanning) and ERT (Electrical Resistivity Tomography), were used for this purpose. Multitemporal ALS data were used to determine the geomorphological changes within the cliff and find the causes of the rapid rate of cliff edge landslides. ALS differential models were the sources of new information about the dynamics of vertical displacement in the landslide and helped calculate the volume of displaced rock masses that occurred over 12 years. The cliff was found to become significantly an active in 2010. This process was observed by analysing the relief of multitemporal digital elevation models, differential models, AND morphological sections and by conducting long-term field observations. The ERT surveys made it possible to generate two 3D ERT electrical resistivity models that provided much new information about the geological structure of the cliff. Additionally, a 2D ERT profile was made through the landslide. The internal structure of the landslide was recognized, and the depth of the slip surface was estimated. The results permitted clarifying the cause of the high landslide activity and the rapid rate of retreat of the cliff edge over the past 12 years. In addition, by means of the results of electrical resistivity surveys and the use of archival boreholes, it was possible to extrapolate a model of the surface relief of the clay hill using geostatistical methods. It was found that at the boundary with the active landslide—the top of the clay layer—is tilted towards the north, i.e. towards the sea, which favours the activation of the landslide. The proposed research methodology, as well as the obtained information, may be of significant assistance in further diagnosis and prognosis of the dynamics of landslide development and the causes of landslide formation within cliff coasts.

Numerical investigation of landslide-induced waves: a case study of Wangjiashan landslide in Baihetan Reservoir, China

Numerical investigation of landslide-induced waves: a case study of Wangjiashan landslide in Baihetan Reservoir, China

3D Discrete Fracture Network Modelling from UAV Imagery Coupled with Tracer Tests to Assess Fracture Conductivity in an Unstable Rock Slope: Implications for Rockfall Phenomena

The stability of a rock slope is strongly influenced by the pattern of groundwater flow through the fracture system, which may lead to an increase in the water pressure in partly open joints and the consequent decrease in the rock wall strength. The comprehension of the fracture pattern is a challenging but vital aspect in engineering geology since the fractures’ spatial distribution, connectivity, and aperture guide both the water movement and flow quantity within the rock volume. In the literature, the most accepted methods to hydraulically characterise fractured rocks in situ are the single borehole packer test, the high-resolution flow meters for fractures, and the artificial tracer tests performed in boreholes. However, due to the high cost a borehole requires and the general absence of wells along coastal cliffs, these methods may not be appropriate in rockfall-prone areas. In this study, an unsaturated rocky cliff, strongly affected by rockfalls, was investigated by combining kinematic analysis, Discrete Fracture Network (DFN) modelling, and artificial tracer tests. The DFN model and potential rock block failure mechanisms were derived from high-resolution 3D virtual outcrop models via the Structure from Motion (SfM) photogrammetry technique. An artificial tracer was injected using a double ring infiltrometer atop the recharge zone of the slope to determine the infiltration rate and validate the DFN results. The DFN and tracer test methods are frequently used at different spatial scales and for different disciplines. However, the integration of digital photogrammetry, DFN, and tracer tests may represent a new step in rockfall and landslide studies. This approach made possible the identification of groundwater flow patterns within the fracture system and revealed about a 10-day tracer transit time from the injection area and the monitored slope, with similar conductivity values gathered from both the DFN and tracer test. Planar and wedge failures with volumes ranging from 0.1 and 1 m3 are the most probable failure mechanisms in the areas. The results were consistent with the delay between the intense rainfall and the slope failures previously documented in the study area and with their mechanisms.

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.

A regional scale coseismic landslide analysis framework: Integrating physics-based simulation with flexible sliding analysis

This paper describes a framework for regional scale coseismic landslide evaluation that combines physics-based wave propagation simulation at a regional scale, with flexible sliding analysis at a site scale. The physics-based simulation incorporates fault rupture process, complex topography and dynamic site response. The sliding displacements of flexible masses are calculated by integrating seismic parameter k obtained from the physics-based simulation over seismic resistance.The framework is applied to the case study of coseismic landslides during the 2016 Mw 7.0 Kumamoto earthquake in Japan, which has well documented fault mechanism, geologic information and landslide inventory. The performance of the model prediction is evaluated by receiver operating characteristic (ROC) analysis. This study highlights the near-fault effect and soil nonlinear effect on landslide distribution, and it is demonstrated that the landslide prediction can be notably improved with the consideration of topographic amplification of ground motions. Empirical correlations between topographic amplification of key intensity measures and parameterized topographic features are developed. Overall, the simulation captures 58% of observed landslides in the inventory (using a displacement threshold of 15 cm), showing the proposed framework is a promising tool for regional coseismic landslide analysis.

A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia

Landslides have been classified as the most dangerous threat around the world, causing huge damage to properties and loss of life. Increased human activity in landslide-prone areas has been a major contributor to the risk of landslide occurrences. Therefore, machine learning has been used in landslide studies to develop a landslide predictive model. The main objective of this study is to evaluate the most suitable sampling ratio for the predictive landslide model in the Langat River Basin (LRB) using Artificial Neural Networks (ANNs). The landslide inventory was divided randomly into training and testing datasets using four sampling ratios (50:50, 60:40, 70:30, and 80:20). A total of 12 landslide conditioning factors were considered in this study, including the elevation, slope, aspect, curvature, topography wetness index (TWI), distance to the road, distance to the river, distance to faults, soil, lithology, land use, and rainfall. The evaluation model was performed using certain statistical measures and area under the curve (AUC). Finally, the most suitable predictive model was chosen based on the model validation results using the compound factor (CF) method. Based on the results, the predictive model with an 80:20 ratio indicates a realistic finding and was classified as the first rank among others. The AUC value for the training dataset is 0.931, while the AUC value for the testing dataset is 0.964. These attempts will help a great deal when it comes to choosing the best ratio of training samples to testing samples to create a reliable and complete landslide prediction model for the LRB.

Application of the Qualitative Method to the Study of Landslides in the Wilaya of Mila North-East Algeria

Application of the Qualitative Method to the Study of Landslides in the Wilaya of Mila North-East Algeria

Distribution and Dynamic Behaviors of Landslide in Rangun Khola Watershed of the Western Nepal

Being a major devastating hazard, the study of landslides in Nepal Himalaya is very essential. For controlling and mitigate measures, understanding the behaviors and distribution of landslides over the temporal and spatial range is indispensable. The current study is carried out in the Rangun Khola watershed of western Nepal which spreads from Mahabharat Range (2,500m) to Dun valley covering an area of 489.39 km2. Polygon-based landslide inventory within the temporal range of 18 years (2003 to 2020 AD) was prepared by using temporal series of Google Earth Pro, Sentinel-2 images, and Landsat images, which were verified during the field visit. The number of landslides and area covered in different spatial units and temporal intervals were analyzed using the Q-GIS. In total, 494 landslides were identified and the area covered by the landslide was 0.47% of the total study area. Landslides in this area are highly dynamic with different activity states and temporal fluctuation. The number of landslides were highest, i.e., 143, in 2005 and the Upper Siwalik region consist of a large number of landslides making them highly prone to landslide events. The presence of thrust and faults was also found to be influencing the landslides and size distribution. The study will be useful for further researches to map susceptibility and hazard and also for policymakers to understand landslide status to reduce the risk.

Geomorphological study of landslides in Halabja district

استخلصت الدراسة إن الخصائص التضاريسية والمناخية والجيولوجية هي السبب الرئيسي وراء حصول الانهيارات الأرضية يؤازرها في ذلك عوامل طبيعية تتعرض لها المنطقة مثل حدوث الزلازل المتوسطة الشدة، ونشاط التجوية الكيميائية وعمل المجاري المائية وزيادة أحمال المنحدرات الناتج عن تراكم الثلوج خلال فصل الشتاء وأخرى بشرية ممثلة بزيادة شق الطرق وإقامة المنشآت الهندسية وما يرفقها من إزالة للنبات الطبيعي وتقويض لقاعدة المنحدرات وزيادة الأحمال. تركز التوزيع الجغرافي بشكل خاص لكافة أصناف الانهيارات الأرضية شرق وجنوب منطقة الدراسة، وقد شملت أنواع الانهيارات الأرضية الزحف، الانزلاق بنوعيه (ألدوراني، والانتقالي)، التدفق بأشكاله المتعددة والمعتمدة بشكل أساسي على المحتوى المائي (الطيني، الأرضي، الحبيبي)، فيما خلا وسط المنطقة من إي نوع من أنواع الانهيارات الأرضية بسب استواء السطح. اختلفت حركة المواد باختلاف نوع المادة المتحركة وسرعة حركتها وطبيعة الحركة.

A Study of Network Roads Landslides and Their Stabilization Methods in Fahs Anjra Province: North of Morocco

A Study of Network Roads Landslides and Their Stabilization Methods in Fahs Anjra Province: North of Morocco

Zoning and Field Study of Landslid es along Forest Roads of Darabkola-Sari

Zoning and Field Study of Landslid es along Forest Roads of Darabkola-Sari

Predicting landslide runout paths using terrain matching-targeted machine learning

Landslide debris will travel certain distances and threaten people and properties along its runout path, highlighting the importance of runout path prediction in landslide risk management. Conventional landslide runout models, either statistical or machine learning-based, only consider the geographic characteristics at the source without fitting the terrain features along the path. A novel terrain matching-targeted machine learning model is proposed for predicting landslide runout paths, in which a consistent terrain matching strategy is introduced for both training and prediction. The model first forward predicts multiple travel distances based on geographic characteristics of all cells along a possible runout path, and then determines the termination cell whose predicted travel distance fits the terrain features best to backward estimate model parameters. Such a terrain matching process not only accounts for geographic characteristics along the paths but also enables the incorporation of three-dimensional terrain reality into model training. A case study of natural terrain landslides in Hong Kong is conducted to validate the proposed machine learning model. Results indicate that the terrain matching-targeted machine learning models significantly outperform conventional statistical models in terms of prediction accuracy. The fall height and landslide scale are the most critical physical factors affecting travel distances of channelized landslides and open hillslope landslides, respectively. The landslide runout model is applied to the Mid-Levels at the foot of Victoria Peak to identify high-risk urban areas vulnerable to landslides, which provides guidelines for designing landslide prevention and mitigation measures.