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Background: The world population is still growing. The growing population caused a changes in the trend of selecting settlements location. Due to the limited flat land, people were starting to form settlements in a hilly or mountainous area which is prone to landslide. The community used to move from place to place to avoid landslides, however, it is no longer possible to implement those actions. While a lot of research has been conducted to assess the vulnerability and risk of settlements to disasters, there needs to be more research on developing settlements in landslide-prone area and their impact on disaster management. Results: High social influences can be found in the development of landslide-prone settlements of Giripurno Village. The community shows a high consideration on relatives in deciding their settlement location. Moreover, high consideration of kinship and social activity affects the arrangement of spaces in the house and directly affects the amount of space occupancy. Layout of houses in Giripurno Village were found to have large living room to accommodate family and community gatherings. Although high social dependences of one community can be beneficial in the disaster emergency response and recovery, it can also hinder the disaster mitigation effors by allowing development in an unsafe area, thus increase the risk of disasters. Conclusion: This paper discuss about how the social factors can relate to the disaster management with an emphasis on the development of settlements. This paper also highlight the aspects of space occupancy which is rarely being discussed in the disaster management related research. The result obtained by this study could provide important insight into the future disaster management in the landslide-prone settlements area. [ABSTRACT FROM AUTHOR]

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

Background: Earthquakes and landslides pose significant threats to human safety and property, necessitating early warning systems. However, the high construction costs of earthquake early warning systems present a challenge. Purpose: Landslide warnings are more prevalent, so linking them to earthquake warnings could address cost concerns. Hence, it is crucial to validate the feasibility of utilizing GNSS landslide monitoring as assistance for earthquake early warning systems. Methods: This paper analyzes acceleration anomaly data from 31 GNSS landslide monitoring points near the epicenter of the May 2, 2023, MW = 5.2 Baoshan earthquake in Yunnan. The response time was determined as the time difference between an earthquake's occurrence and GNSS's acceleration anomalies. This calculation helps measure the time delay and sensitivity between these two events. Data were obtained from the geological disaster monitoring and early warning management system. Results: GNSS landslide monitoring showed high sensitivity to nearby earthquakes. The fastest response time among the 31 data points was 8 seconds, while the slowest was 56 seconds, all falling within the one-minute mark. A linear correlation was found between acceleration anomaly response time and distance from the epicenter, indicating the feasibility of GNSS landslide monitoring-assisted earthquake monitoring. Conclusion: A proposal is made for a GNSS landslide monitoring cluster to establish a multi-dimensional landslideearthquake disaster warning system. This approach offers new methods for combining earthquake and landslide early warning systems, leveraging existing infrastructure for cost-effectiveness and enhancing disaster preparedness. [ABSTRACT FROM AUTHOR]

Over the past years, the growing number of natural hazards all over the world has led to an increasing focus on activities aimed at studying and controlling the occurrence of these phenomena. In this context, monitoring systems have become a fundamental component for Landslide Early Warning Systems, allowing to understand the evolution of these processes and assess the need for dedicated mitigation measures. This result is achieved thanks to several technological advancements that led to the introduction of more accurate and reliable sensors, as well as automatic procedures for data acquisition and elaboration. However, despite these improvements, the data interpretation process is still a challenging task, in particular when it comes to the identification of critical events and failure forecasting operations. This paper presents a methodology developed to assess if a potentially critical event is displaying a significant deviation from previously sampled data, or if it could be classified as a false alarm. The process relies on the definition of a threshold value based on the landslide behavior preceding the event of interest. In particular, the reference value derives from the evaluation of equivalent displacements, defined as the displacements previously observed in a time interval equal to the one showed by the potentially critical event. This paper reports a series of examples referring to different case studies, involving both false alarms and real collapses, underlining the effectiveness of the proposed model as a useful tool to evaluate the landslide behavior with a near-real-time approach. [ABSTRACT FROM AUTHOR]

Length (L), width (W), and aspect ratio (ε, L/W) are elementary parameters widely adopted to characterize landslide geometry. Aspect ratio (ε) had been associated with the propagation of landslide along its movement path. However, current automatic measurement algorithms have not effectively considered the path of landslide movement, therefore only measure "path-independent" instead of "path-dependent" geometric parameters. This paper proposes a new approach for measuring path-dependent landslide L, W, and ε, in which path-dependent landslide L (Lpdep) is measured as the path distance from the landslide crown to its tip along the central line of landslide movement, and path-dependent landslide W (Wpdep) is measured as the weighted average lateral extent, across the central line of landslide movement. Specifically, Wpdep is calculated indirectly through dividing the landslide area by its length (A/Lpdep), and path-dependent landslide ε (εpdep) can be calculated by Lpdep2/A. The central line of landslide movement, i.e., landslide profile, is a three-dimensional (3D) polyline generated by the freely available software ALPA (Automatic Landslide Profile Analysis), and all these path-dependent geometric parameters can be measured either in 3D space or in two-dimensional (2D) space (in the horizontal plane). A case study is presented showing that path-dependent parameters can be effectively measured by the proposed profile-based approach, and could be significantly different from path-independent parameters especially for long-narrow and tortuous landslides. This paper also practically introduces new path-dependent definitions of landslide L, W, and ε, which inspires a worthwhile revisit of nomenclature. An overview shows that significant divergencies exist between current definitions of landslide L, W, and ε. In order to moderate the raised ambiguities and confusions, this paper accordingly suggests a "physically indicative" inclusive nomenclature somehow embracing all current representative definitions, and then appeals for constituting "type-specific" nomenclatures for each landslide type. However, some open questions still exist. We therefore suggest relevant academic organizations to work further on type-specific nomenclatures and effective measurement approaches for landslide L, W, and ε. [ABSTRACT FROM AUTHOR]

This paper presents the first example of how to systematically identify the submarine landslide source of a tsunami using an innovative hybrid approach. This ground-breaking method is developed to resolve the puzzle around the source mechanism of the mysterious tsunami observed on 6th February 2023 in the Eastern Mediterranean Sea. The tsunami followed the two inland Mw 7.8 and Mw 7.5 Türkiye–Syria earthquakes, which occurred consequently with a 9 h interval on this day. The first earthquake (Mw 7.8) had an epicentral distance of 90 km from the nearest coast, which is closer than the second one (Mw 7.5) to the coast and yet its crustal deformation was almost entirely limited to inland. Therefore, the co-seismic surface displacement generated by the earthquake was ruled out as the source of the tsunami, confirmed by numerical modelling. Here, we hypothesized that the tsunami was most likely generated by a submarine landslide triggered by the earthquake. Analysis of tide gauge observations revealed that the waves arrived from 27 min to 48 min after the first earthquake (Mw 7.8) at different coastal locations, implying that the potential submarine landslide was triggered by the first earthquake (Mw 7.8). Backward tsunami travel time mapping using tide gauge observations guided us to constrain the area of the potential landslide. We approximated the dimensions of the landslide using spectral analysis of the tsunami observations. Consequently, an iterative trial-and-error approach was employed to confirm the landslide source of the tsunami by defining various informed alternative landslide scenarios and applying numerical modeling. Modelling showed that a submarine landslide can reproduce the tsunami observations reasonably well. It is located on a steep slope of the seafloor approximately 50 km from Arsuz. The submarine landslide is estimated to have caused a seafloor deformation measuring approximately 16 km in length and 4.0 km in width. [ABSTRACT FROM AUTHOR]

The reservoir impoundment can lead to accelerated deformation or reactivation of landslides, posing great threats to human lives and dam stability. The primary objective of this paper is to report the movement process of the Tuandigou (TDG) ancient landslide in the Baihetan dam area during reservoir impounding. Field investigation, borehole work, and site monitoring data are used to analyze the deformation behavior and triggering mechanism of the TDG landslide. It is found that the landslide deformation is closely related to the reservoir water level. After the reservoir water level reached the elevation of 810 m on October 12, 2022, the ground cracks continued to expand and gradually linked up, resulting in an accelerated deformation of the TDG landslide. Furthermore, the lithology in the study area also positively contributes to the slide movement. Based on these results, it is anticipated that the present research will provide guidelines for the early warning and control of the TDG landslide during and after the reservoir impounding. [ABSTRACT FROM AUTHOR]

The aim of this paper is to report on fatal landslides resulting from two extraordinary storm events in January and February 2023, in Auckland, New Zealand, including the characteristics of the terrain, the site geology, and the storms. The January 27 Auckland Anniversary storm was from an "atmospheric river," dumping Summer's worth of rain (265 mm) in one day. This was the highest 24-h total on record, estimated a 1 in 200-year event, with a peak 2-min rainfall of 4.2 mm, coinciding with Friday's evening "rush hour." This led to widespread landsliding throughout Auckland, including a fatal landslide in Parnell, central Auckland. Notably, this occurred only meters from a similarly destructive landslide in 1997. With January 2023 Auckland's wettest January on record, and with the region still in a state of emergency, Tropical Cyclone Gabrielle crossed the region on February 13–14. This also caused widespread landsliding, including a fatal landslide at Muriwai on the west Auckland coast. Fatal landslides from the same escarpment had occurred in 1965, close to the February 13, 2023, fatal landslide. Taken together, both storm events caused > 140,000 landslides across the North Island, and flooding, making hundreds of people homeless, with damage estimates of > US$8.6 billion (3.4% of NZ's GDP). For future disaster risk reduction, attention should be paid to revising legislation and local planning, which currently allows people to legally build (and re-build) houses in landslide-prone areas of New Zealand. [ABSTRACT FROM AUTHOR]

The use of Global Navigation Satellite System (GNSS) in combination with real-time kinematic technique, known as differential GNSS (dGNSS), has increased in recent years for monitoring landslide displacements and detecting early signs of potential failure, enabling an earlier response for risk mitigation than traditional monitoring techniques. GNSS offers several advantages, including high accuracy and high-frequency data collection. Although more cost-effective, their affordability may still present challenges for public organizations managing multiple landslides in their territory. The SparkFun is a suite of components for GNSS assembly designed for topographic surveying, that integrates u-blox ZED-F9P or ZED-F9R modules. The system offers the benefits of dGNSS technology while being more affordable than other market options. It also avoids relying on phone signals for data storage on a cloud server. This paper presents the SparkFun system, its components, and how it can be assembled to create a dGNSS system for landslide monitoring. The deployment and testing at the Chin Coulee landslide in Alberta are discussed. Over the 6-month testing period, the system achieved millimeter accuracy (up to 14 mm), aligning with the manufacturer's specifications. Estimated system errors were found to be comparable to a commercially available dGNSS system (Ophelia Geocube). Additionally, the system exhibits displacement trends similar to the 2018 Geocube monitoring campaign; however, for future deployments, the robustness of the power supply system and the insulation of the equipment need to be enhanced. Overall, the SparkFun system appears to be a promising and cost-effective alternative for monitoring landslide displacements. [ABSTRACT FROM AUTHOR]

Landslide hazards have significant social, economic, and environmental impact. This work provides a critical review of the main existing literature using satellite data for mapping landslides. We created and examined an extensive bibliographic database from Web of Science (WoS) consisting in 291 outputs from > 1,000 authors who studied almost 700,000 landslides across all continents, for a total of 52 countries represented with China and Italy on top of the list with more authors. The outputs are equivalent to ~ 5% of the whole landslide-related production for the period 1996–2022, with a 600% increase in the number of papers after 2014 driven by the availability of Sentinel-1 and Sentinel-2 data. Analysis of the geographical location across the 66 different countries analysed shows that, within the total number of contributions, the satellite imagery was used to detect and map two main types of landslides: flows and slides. When specified in the manuscripts, the events have been triggered by rainfall (104 cases), earthquakes (32 cases), or both (17 cases). Slope instabilities in these areas were predominantly identified through manual detection (40%); but since 2020, the advent of artificial intelligence is suppressing all other techniques. Despite the undisputed progress of EO-based landslide mapping over the last 26 years, which makes it a consolidated tool for many landslide-related applications, challenges still remain for an effective and operational use of EO images for landslide detection and mapping, and we provide a perspective for future applications considering the existing and the planned SAR satellite missions. [ABSTRACT FROM AUTHOR]

Selecting appropriate mapping units is vital for landslide susceptibility mapping as well as pattern investigation, given that various units-based analyses extensively control the prediction performances. This paper investigated the landslide development through the interpretation and field surveys along the Yunling–Yanjing segment of the Lancang River in southeastern Tibet, and fulfil LSM with the consideration of 15 conditioning factors. Two grid unit methods (single-point and multi-point patterns) and two slope unit methods were comparatively analyzed for model training and mapping of landslide susceptibility via machine learning algorithms. The results suggest that the landslides are preferentially distributed in an elevation range of 2000–4000 m, in a slope range of 20–40°, a local relief range of 1000–2500 m, and southwest-oriented slopes. The data extracted by the multi-point method denotes a higher representation of landslide development features. All models possessed positive prediction ability for landslide susceptibility, and the multi-point method based on grid unit performed the best with the AUC exceeding 0.9. The best-performing models indicated that zones of high and very high susceptibility mainly distributed adjacent to the mainstem and some tributaries of the Lancang River. Furthermore, the distribution of "safety islands" (the slopes less prone to landslides) along National Highway G214 was reasonably illustrated as well, which provides a hazard predictability for such an important transportation corridor along the deeply-incised valley in the Lancang River. This study demonstrates a theoretical basis for the regional disaster prevention and mitigation for human activity, and provides methodological references for landslide susceptibility evaluation in similar mountainous areas over the world. [ABSTRACT FROM AUTHOR]

Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations. [ABSTRACT FROM AUTHOR]

The premise of ecological protection and sustainable development in coal mining areas is to find the law of surface damage, damage speed and damage mechanism, and then put forward preventive measures in a targeted manner. Taking the landslide in the coal mining mountain area of Shanxi Province as an example, this paper comprehensively integrates the disaster-pregnant environment and disaster information of the landslide, and constructs a multi-level model of the scene through the analysis of the landslide geology scene. The object features characterize the evolution process of landslides in terms of stratigraphic structure changes, surface changes, texture changes, shape characteristics, spectral characteristics, color characteristics, etc., the typical landslide types in Shanxi coal mining areas are verified by examples. The comprehensive consideration of geology, geographical environment, ecological environment, disaster environment and the damage of landslides to other important ground features, with the advent of the era of big data, various data obtained from air, space and ground monitoring can be applied to research. Through the cognition of landslide spatial scenes, it is helpful to understand the influence mechanism of external events on landslide activities, so as to better predict the spatial and temporal characteristics of landslide activities. [ABSTRACT FROM AUTHOR]

Landslide instability occurs frequently all over the world. The specific factors affecting the loess slope instability include collapse, landslide, debris flow, etc., among which rainfall is one of the most important factors. In this paper, through field model tests, the displacement changes of soil mass at different depths inside the slope and on the slope surface under different rainfall intensity and rainfall time are studied by means of artificial rainfall and artificial water injection. The results show that under the circumstance of rainfall, the displacement of the shallow soil changes obviously, while the deep soil shows obvious hysteresis; as the rainfall process continues, the displacement of the deep soil increases gradually, but the growth rate is significantly lower than that of the shallow soil. After the rain stopped, the soil displacement continues to increase within 3 to 4 days, the deformation of the shallow soil is relatively stable, and the deformation of the deep soil converge slowly. With the increase of rainfall intensity, the closer the soil mass is to the slope toe within 2.2 m, the greater the horizontal displacement is, but the displacement change of shallow soil mass within 0.8 m is smaller, and the displacement at the slope toe is the most obvious. The closer the soil below 2.2 m to the slope toe, the smaller the horizontal displacement of the slope. The closer to the toe of the slope, the more likely the loess slope to be damaged. The slope shows different effects under the influence of rainfall, water injection, rainfall duration, and influencing region. The research results can provide guidance for loess slope reinforcement and slope restoration. [ABSTRACT FROM AUTHOR]

In the present paper, an active landslide located to the southeast of the urban area of Alcoy (Alicante, Spain) is analysed. This landslide affects the N-340 road, as well as an area in which some industrial buildings dated from the fifteenth century and catalogued as Places of Cultural Interest are placed. This region is characterised by the existence of a steep relief composed of materials from the Miocene to the present, on which many gravitational movements are developed. The displacements have been analysed using high-resolution spotlight mode SAR images captured by the Spanish SAR satellite PAZ (which operates at X-band), which were processed by advanced differential SAR Interferometry (A-DInSAR). The behaviour of the instability has been determined using InSAR-derived time series displacement maps. Additionally, the study has been completed with a detailed geomorphological mapping at 1:2000 scale and an analysis of structural damage carried out in situ. The landslide was modelled in 3D, from a deterministic and probabilistic point of view, confirming the global instability of the area and the relationship of the landslide with rainfall and the construction of a new road. The integration of all this information obtained and its subsequent analysis has made possible to precisely define the extension of the instability, its kinematics, and to identify the main triggering factors. This information will be crucial for the proper management of this landslide by the local authorities. [ABSTRACT FROM AUTHOR]

Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020). [ABSTRACT FROM AUTHOR]

The preferred plane often exists in a layered slope, which has a main influence on the slope stability. Such a plane can evolve into the failure surface, due to the internal and external triggers of landslides. This paper proposes a case study of the failure of a highway slope in Sichuan, China. The site investigations were conducted using a number of methods, such as geological survey, unmanned aerial vehicle survey, slope monitoring, in-situ and laboratory tests. Through the case study, the failure mechanism of the layered slope was discussed, where the slope stability analysis was conducted using the limit equilibrium method (slip-wedge method) and strength reduction method (SRM). According to the circumstance of the landslide area and the requirement for the highway, this paper also proposes the stabilization measures, which was subsequently validated by SRM. Finally, the sliding mechanism of the landslide was discussed. [ABSTRACT FROM AUTHOR]

A destructive landslide occurred in spring 2019, in Hosein Abad Kalpush Village, Northeast of Iran, after an unusually rainy period. Significant damages were observed in buildings and roads located on the slope failure zone. This paper represents a preliminary reconnaissance report prepared through the site visit done by the authors, a few days following the landslide occurrence. Then, the geotechnical and geophysical properties of the sliding area are reported using the site investigation tests. Subsequently, the rainfall data are incorporated for numerical slope stability analyses. This study shows the variation of the failure mechanism of the slope before and after rainfall. The results confirm that the factor of safety decreases 1.78 times after a 3-month period of rain with rainfall intensity about 6 times the long-term average. Finally, the performance of two slope stability improvement strategies is evaluated to mitigate the landslide hazard. The results of the study can provide a perspective on the climate change impacts in Iran for future geosystem designs, geohazard prediction, and the selection of rainfall-induced landslide mitigation strategies. [ABSTRACT FROM AUTHOR]

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

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

Nowadays, the 3D modelling of surfaces is widely used to provide point clouds in geosciences. To study the evolution of landslides, many point clouds are available but post-processing can be very computationally demanding, especially to analyze their dynamics. This paper proposes automatic tools to identify areas with continuous motion and those with non-continuous motion using a new continuous motion indicator. Displacements of continuously moving areas are computed by two "displacements" methods (the image correlation method and the Gefolki differential optical flow method), which are compared. The evolution of non-continuous motion areas is quantified by a distance metric. The developed method is applied on two sites with different deformation mechanisms and evolution speeds to test the ability to show the 3D spatial evolution of landslides. The comparison with instrumental data shows a good concordance between the calculated data and the reference data. The Gefolki differential optical flow method is considered as a relevant option for the image correlation method with the new continuous motion indicator. [ABSTRACT FROM AUTHOR]

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

This paper reports the results of satellite image and seismological data analysis obtained when considering the causes of a landslide on the Yelbashinskii dump at the Kolyvan anthracite deposit. The potential for determining the natural vibrations of large objects from low-frequency seismological records and monitoring thereof has been demonstrated. [ABSTRACT FROM AUTHOR]

A framework for a IoT-based local landslide early warning system (Lo-LEWS) has been proposed. Monitoring, modelling, forecasting and warning represent the main phases of the proposed framework. In this study, the first two phases have been applied to capture the hydrological behaviour of a natural unsaturated slope located adjacent to a railway track in Eastern Norway. The slope is monitored and the stability is kept under frequent observation, due to its steepness and the presence of the railway lines at the toe. The commercial software GeoStudio SEEP was used to create and calibrate a model able to replicate the in situ monitored volumetric water content (VWC) and pore water pressure (PWP) regime. The simulations conducted were divided into two main series: one with an initial calibration of the VWC profile (C) and another with no calibration (NC). The simulations have been validated using Taylor diagrams, which graphically summarize how closely a pattern (or a set of patterns) matches observations. The results show that a preliminary calibration for matching the in situ VWC, as well as considering climate conditions and vegetation, are crucial aspects to model the response of the studied unsaturated slope. A sensitivity analysis on the hydraulic conductivity and the permeability anisotropy ratio contributed to better define the input data and to improve the best-fit model result. The effectiveness of the best simulation, in back-calculating VWC, was tested for 3 different time periods: 6-month, 1-year, 1.25-year. The results show that the hydrological model can adequately represent the real monitored conditions up to a 1-year period, a recalibration is needed afterward. In addition, a slope stability analysis with GeoStudio SLOPE for the 1-year period was coupled to the hydrological model. Finally, the calculated safety factor (FS), the temperature, the precipitation, the VWC and PWP monitored were used as input dataset for a supervised machine learning algorithm. A random forest model highlighted the importance of the monitored VWC for forecasting the FS. The findings presented in this paper can be seen as a first step towards an Internet of Things (IoT)-based real-time slope stability analysis that can be employed as Lo-LEWS. [ABSTRACT FROM AUTHOR]

Most previous studies on the quantitative risk assessment (QRA) of landslides focused on the probability of slope failure at the pre-failure stage and adopted empirical models for consequence analysis. The conventional approaches simplify the relationship between the pre-failure state and the post-failure behavior and cannot reasonably account for the effects of uncertainty on the entire landslide process. In this paper, an efficient QRA method that involves the direct simulation of the entire landslide process is proposed. A QRA formula that considers the probability of only those landslides that can impact the element at risk is used. The coupled Eulerian–Lagrangian method is used to simulate the entire landslide process and to identify slopes that can impact the element at risk and determine the failure consequences. The subset simulation method is adopted to efficiently estimate the probability of landslide impact, and parameter uncertainty is considered. Two case histories of landslides are investigated. First, the 2011 Baqiao loess landslide in Xi'an, China, is investigated, and the results of the proposed method are compared with those of the conventional approaches. Second, the proposed method is applied to assess the risk of the 2015 Ganjingzi landslide in the Three Gorges Reservoir. The effects of the risk mitigation works are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, the artificial bee colony (ABC) algorithm is used to generate landslide susceptibility maps. To the best of our knowledge, it is the first time the ABC algorithm is applied as a classifier on landslide susceptibility mapping. Twelve features are considered as inputs for the ABC model in this research work. These features contain redundant and correlated information. Therefore, normalization and dimension reduction are applied in the pre-processing stage. Three different normalization methods are investigated, i.e. minimum and maximum (Min-Max), mean standard deviation (Mean-SD), and median and interquartile range (Med-IQR). Two dimensionality reduction techniques, i.e., principal component analysis (PCA) and canonical variate analysis (CVA), are utilized to deal with high-dimensional information and eliminate correlated information in the data. In total, six different schemes of input data based on the three normalization methods and two techniques of dimension reduction are applied to train the ABC model. There are a total of 382 landslide incidents occurred at various locations on Penang Island, out of which 255 are selected for training and remaining 127 are used for the validation purpose of the model. The model classification accuracies and receiver of characteristics (ROC) curve are applied to evaluate the model's performance. The results show that the ABC model trained using Min-Max normalization and CVA scheme generates the best results among these six schemes. The landslide susceptibility maps generated in this research offer significant information for landslide risk management. [ABSTRACT FROM AUTHOR]

Long-runout landslides can be extremely hazardous and unpredictable. The prediction of long-runout landslides is challenging because it is unclear what factors control their mobility, and it is unclear how to best measure their runout given that they traverse changing topography. In this paper, we document the development and statistical evaluation of a Landslide Runout Score (LRS) system to predict short, medium, and long runout using a new mobility measure, the unitless Runout Number L/A1/2 (where L is landslide length and A is landslide area). The Runout Number has previously been correlated to three geomorphological factors (planimetric curvature, sand content, and upslope contributing area normalized to landslide area), so the LRS uses these factors as inputs. These factors are readily calculated using geographic information systems (GIS) and publicly available data sources. The LRS system predicts runout categories (short, medium, and long) with one-vs.-all accuracies (i.e., percentage of correct predictions within a given class) of 75, 58, and 72%, respectively, for a total weighted accuracy of approximately 65%. The results of this work are summarized in a worksheet that can be used by geologists and engineers to develop preliminary predictions of landslide runout behavior, which can be incorporated into or used alongside regional-scale landslide hazard assessments. [ABSTRACT FROM AUTHOR]

Aspect ratio is an elementary parameter widely adopted to characterize landslide geometry. Nevertheless, it in essence reflects an overall elongation of landslide only, and cannot reflect the variation of width along the central line of movement. This paper proposes the new concept "landslide longitudinal shape" for complementing the landslide aspect ratio, and develops the methodology for interpreting it based on generation and analysis of the landslide profile. Relative width change and relative width fluctuation are defined as parameters quantitatively describing landslide longitudinal shape, and five different types of shape (rectangle, widening, narrowing, spindle, and hourglass) are predefined for qualitatively classifying it. A case study showed that landslide longitudinal shape can provide additional information and therefore is a complement to the aspect ratio. Landslides with larger aspect ratios (more elongated) are more possibly characterized by a narrowing overall varying trend and a stronger fluctuation of width. Landslides with similar longitudinal shapes of headscarp zones were found to have transition-deposition zones with diverse longitudinal shapes, which is caused by diverse topographic confinements and forces governing motion. For an unconfined and laterally confined landslide in the study area, its transition-deposition zone is most likely to have a regular and narrowing longitudinal shape, respectively. Longitudinal shape is not just a geometric parameter, but can somehow help to understand motion constraints of landslides. It must be emphasized that the new longitudinal shape concept is not applicable to landslides that show a complex geometry that prevents a clear definition of a longitudinal profile. [ABSTRACT FROM AUTHOR]

Rapid detection of landslides using remote sensing images plays a key role in hazard assessment and mitigation. Many deep convolutional neural network-based models have been proposed for this purpose; however, for small-scale landslide detection, excessive convolution and pooling process may cause potential texture information loss, which can lead to misclassification of landslide target. In this paper, we present a novel UNet model for the automatic detection of landslides, wherein the reversed image pyramid features (RIPFs) are adapted to mitigate the information loss caused by a succession of convolution and pooling. The proposed RIPF-Unet model is trained and validated using the open-source landslides dataset of the Bijie area, Guizhou Province, China, wherein the precision of the proposed model is observed to increase by 3.5% and 4.0%, compared to the conventional UNet and UNet + + model, respectively. The proposed RIPF-Unet model is further applied to the case of the Longtoushan region after the 2014 Ms.6.5 Ludian earthquake. Results show that the proposed model achieves a 96.63% accuracy for detecting landslides using remote sensing images. And the RIPF-Unet model is also advanced in its compact parameter size; notably, it is 31% lighter compared to the UNet + + model. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides are among the most fatal and destructive geological hazards in the mountainous regions where active faulting is a major geological process. So far, many studies have been conducted on the various factors that influence or trigger rainfall-induced landslides; however, lesser attention has been paid to the proximity of rainfall-induced landslides to major faults. First time, this paper is going to discuss the concepts of fault damage zone and fault zone processes that interact and influence the occurrence of rainfall-induced landslides. This is done by introducing the concept of fault damage zone and its architecture and then presenting several examples from high mountains of Alborz and Zagros in which fault zones interact proactively with mass movement processes. Fault zone processes control the rainfall-induced landslides by three ways: (1) increasing fracture density near major faults that produces more debris and reduces rock strength, (2) weak minerals growing in fault zones in which clay minerals grow and (3) topographic features produced by active faulting. Presented examples show how active faulting can influence localization of rainfall-induced landslides. Intensely weathered rocks in the fault zone are a major source of sediment flux combined with the water to form mudflows. Additionally, fault mechanism and movement direction of fault blocks influence on internal geometry and attitude of minor structures is fault zone that controls properties of rock materials in the fault zone and therefore influence on slope strength. The interaction and connectivity among tectonic faults, river systems and erosion is a complex feature that should be considered in modeling landslides in the mountainous regions. [ABSTRACT FROM AUTHOR]

The monitoring data of landslide deformation are characterized by non-smooth, nonlinear and random changes, and the cumulative changes of the monitored objects have both monotonous growth trends and short-term fluctuations. The GM(1,1) model can get better results only when the data series are monotonous. Due to the limitations of the model, the prediction accuracy of the GM(1,1) model is limited to a certain extent. An improved algorithm based on the GM(1,1) model and the empirical mode decomposition (EMD-GM(1,1) model) for deformation prediction was presented to improve the forecast accuracy in this paper. Firstly, EMD was used to effectively separate the nonlinear high-frequency and low-frequency components hidden in the deformation sequence; then the moving average method was applied to build a prediction model for high-frequency component, and the GM(1,1) was applied to build the prediction model for low-frequency one according to the characteristics of each component; finally, the predicted value of each component was superimposed. The experimental results indicate that the optimized EMD-GM(1,1) model combines the advantages of the two models to separate effectively the different frequency components of the deformation sequence, which has higher prediction accuracy. Compared with the conventional GM(1,1) model, DGM(2,1) model and the Linear fitting model, the proposed model could satisfactorily describe the landslide deformation prediction practically. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management. [ABSTRACT FROM AUTHOR]

When an active landslide is first identified in an artificial reservoir, a comprehensive study has to be quickly conducted to analyse the possible hazard that it may represent to such a critical infrastructure. This paper presents the case of the El Arrecife Landslide, located in a slope of the Rules Reservoir (Southern Spain), as an example of geological and motion data integration for elaborating a preliminary hazard assessment. For this purpose, a field survey was carried out to define the kinematics of the landslide: translational in favour of a specific foliation set, and rotational at the foot of the landslide. A possible failure surface has been proposed, as well as an estimation of the volume of the landslide: 14.7 million m3. At the same time, remote sensing and geophysical techniques were applied to obtain historical displacement rates. A mean subsidence rate of the landslide around 2 cm/year was obtained by means of synthetic aperture radar interferometry (InSAR) and ground-penetrating radar (GPR) data, during the last 5 and 22 years, respectively. The structure-from-motion (SfM) technique provided a rate up to 26 cm/year during the last 14 years of a slag heap located within the foot of the landslide, due to compaction of the anthropical deposits. All of this collected information will be valuable to optimise the planning of future monitoring surveys (i.e. differential global positioning systems, inclinometers, ground drilling, and InSAR) that should be applied in order to prevent further damage on the reservoir and related infrastructures. [ABSTRACT FROM AUTHOR]

Understanding the kinematics and triggers of a landslide is central to assessing the risk of a landslide. This understanding can be achieved through a robust instrumentation plan that addresses key factors such as meteorological conditions, groundwater conditions, and deformation trends and velocity. High-frequency landslide displacement measurements at multiple locations across a sliding mass allow for the evaluation of the impact of meteorological events in the short and long terms. Installation of a monitoring system that provides reliable and accurate high-frequency displacement data from multiple locations can be expensive, and thus, this approach is not commonly used. Single-frequency differential GNSS (dGNSS) provides a relatively low-cost alternative to achieve long-term monitoring goals at multiple locations within a landslide. Networks of single-frequency Geocube™ dGNSS units were installed at test sites at two slow-moving Canadian landslides: the Ten-mile landslide near Lillooet, BC, and the Chin Coulee landslide near Taber, AB. The Geocube system monitored surficial deformation every 60 s for the duration of the monitoring program (> 500 day) and achieved a precision in direct measurements characterized by a standard deviation of 10.5 mm or less. The quality of the collected data was compared to data provided by a high-end multi-frequency GMX 910 Leica GPS, installed at the Ripley landslide near Ashcroft, BC. The data collected from the Geocube systems revealed that displacement orientations and rates varied within each of the sliding masses. The application of multiple monitoring points provided a relatively low-cost method to evaluate the shape of the sliding shear surface and the causal factors driving movement at each site. A practical evaluation of cost-effective dGNSS systems that provide reliable information as required for enhanced understanding of landslide kinematics and the development of early warning systems is presented in this paper. [ABSTRACT FROM AUTHOR]

An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis (SSA) and stack long short-term memory (SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine (SVM) and auto regressive integrated moving average (ARIMA). The mean relative error (MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error (RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides. [ABSTRACT FROM AUTHOR]

The landslide mechanism and failure mode of the gently sloping Piedmont region are analyzed in Fangshan and Zhutoushan hills in the Nanjing area of Jiangsu Province, China. These hills are underlain by volcanic rocks and have a distinctive stratigraphic structure composed of fill or clay at the surface, coarse-grained soil or fully weathered rock in the middle, and highly or moderately weathered rock at the bottom. Landslides occurred during or after heavy rainfall, and the monitored displacements of the slope surface indicated that sliding had not stopped after a long period of continuous rainfall. Through monitoring of groundwater and simulation, we found that the low-permeability highly weathered rock can restrict the activity of the lower aquifer, resulting in a rapid rise of pore pressure and formation of confined water in response to rainfall, which can lead to slope failure. We discuss the mechanism and avulsed–drifted failure mode of the landslides considering seepage deformation and pressure balance. Finally, we propose appropriate control measures for these landslides. The results of this paper will contribute to prevention of similar landslide geological hazards. [ABSTRACT FROM AUTHOR]

Landslides are common occurrences along Chinese rail routes. Accordingly, the accurate determination of sliding-surface locations and the underlying causes become critical from the landslide-disaster-management and loss-mitigation viewpoints. In this study, the landslide in the K66 section of the Jiheng Railway (installed in Jinggangshan City, Jiangxi, China) was investigated. The causative factors and characteristics of the landslide were analyzed using geophysical methods as well as drilling and monitoring techniques. The results reveal that a combination of the transient electromagnetic method and electrical resistivity tomography can be used to identify the location of water-rich landslide-prone regions. When combined with the drilling information, these results reveal the tendency of landslide occurrence in regions comprising strongly weathered carbonaceous shale. Moreover, an uneven slope deformation is observed along the railway line. The seepage of surface water into the slope is revealed as the primary cause of landslides. This is because the reduced strength of the strongly weathered carbonaceous shale facilitates the formation of sliding surfaces. This paper presents a comprehensive survey plan for landslides occurring along railway routes. Furthermore, an assessment of the risk of future landslide occurrences in the investigation area is presented, thereby providing an appropriate reference for landslide prevention and loss mitigation. [ABSTRACT FROM AUTHOR]

Landslide is a uniquely dynamic large-deformation process that can present serious threat to human lives and infrastructures. The natural soil properties often exhibit inherent spatial variability, which affects the landslide behavior significantly. This paper focuses on combined Monte Carlo simulation and three-dimensional (3D) dynamic large-deformation finite-element (LDFE) analysis using the coupled Eulerian-Lagrangian method to investigate the whole runout process of landslide induced by the earthquake in spatially varying soil. The results from LDFE analysis show that the mean value of runout distance in spatially varying soil is significantly higher than that of the deterministic value obtained from a homogeneous slope due to the slope failure developed along the weakest path in soils. The mean runout distance increases and converges with increasing slope length in 3D-LDFE stochastic analysis. The advantages and necessities of 3D-LDFE analysis were illustrated by comparing it with two-dimensional (2D) LDFE analysis of landslide in spatially varying soil. The results show that the calculated mean runout distance using 3D-LDFE method is at least 16.1% higher than that calculated using 2D-LDFE analysis. Finally, a linear regression formula was established to estimate the mean runout distance of landslide due to horizontal inertia acceleration. Such a formula may facilitate the risk assessment of landslide in practical engineering. [ABSTRACT FROM AUTHOR]

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

Landslide failure time prediction is considered a challenging issue in landslide research. A significant goal of landslide research is to provide scientific and accurate prediction methods. In this paper, the short-term forecasting of landslides (STFL) model is proposed by analyzing landslide deformation characteristics in a known evolution process. The landslides on the southern slope of the West open pit mine (SSWOPM) in Liaoning province, northeast China, and Huangci in Gansu province, northwest China, were selected as the study cases. After pre-processing, which include eliminating abnormal data (t test), adding missing data (cubic spline interpolation), and smoothing noisy data (Savitzky–Golay filters), the displacement data are assembled to serve as the model's input parameters. The parameters of STFL can be obtained through the Levenberg–Marquardt (LM) algorithm. Using the forecasting criterion, the time of failure of landslides can be predicted and determined. Forecasting results provide evidence that the STFL model can achieve an accurate prediction of landslide displacement (correlation coefficient R2 > 0.99). The forecasting results of the SSWOPM and Huangci landslides indicate that the forecasting failure time of the STFL model is March 10, 2014 and January 31, 1995, respectively, which are near the actual failure time compared with those obtained using the Verhulst model. This finding indicates that the new method has excellent reliability and accuracy in landslide prediction and a robust description of the landslide movement. [ABSTRACT FROM AUTHOR]

Landslide warning models are important for mitigating landslide risks. The rainfall threshold model is the most widely used early warning model for predicting rainfall-triggered landslides. Recently, the rainfall threshold model has been coupled with the landslide susceptibility (LS) model to improve the accuracy of early warnings in the spatial domain. Existing coupled models, designed based on a matrix including predefined rainfall thresholds and susceptibility levels, have been used to determine the warning level. These predefined classifications inevitably have subjective rainfall thresholds and susceptibility levels, thus affecting the probability distribution information and eventually influencing the reliability of the produced early warning. In this paper, we propose a novel landslide warning model in which the temporal and spatial probabilities of landslides are coupled without predefining the classified levels. The temporal probability of landslides is obtained from the probability distribution of rainfall intensities that triggered historical landslides. The spatial probability of landslides is then obtained from the susceptibility probability distribution. A case study shows that the proposed probability-coupled model can successfully provide hourly warning results before the occurrence of a landslide. Although all three models successfully predicted the landslide, the probability-coupled model produced a warning zone comprising the fewest grid cells. Quantitatively, the probability-coupled model produced only 39 grid cells in the warning zone, while the rainfall threshold model and the matrix-coupled model produced warning zones including 81 and 49 grid cells, respectively. The proposed model is also applicable to other regions affected by rainfall-induced landslides and is thus expected to be useful for practical landslide risk management. [ABSTRACT FROM AUTHOR]

On June 24, 2017, a catastrophic landslide destroyed the village of Xinmo (Maoxian County, Sichuan, China). A 2.87 × 106 m3 rock mass in source area collapsed and entrained the surface soil layer along the run-out path. This disaster took eighty-three people's lives and destroyed more than 103 houses. It is worth noting that rock fragmentation and grinding could expand the spreading area of danger zone in a landslide event. The Xinmo landslide provided a rare opportunity to infer the dynamic fragmentation and grinding of rock masses from the particle size and shape distribution in the entrainment and deposition area. A field investigation combined with an automatic image analysis system was conducted to study the characteristics of particle size and shape distribution along the debris channel. The image analysis of these field data showed that the median size (D50) of particles ranged from 0.41 to 27.71 m in the landslide area. Particle fractal dimension (D) obtained from the Number-size distribution ranged from 1.77 to 2.97 over the entire study area. Moreover, the evolution of D50 and D along the run-out path confirmed that the degree of cumulative rock fragmentation increased as the travel distance increased. Additionally, the particle roundness (R) ranged from 0.51 to 0.88 along the run-out path, which peaked twice during the motion of granular flow, once was in the entrainment area, and another was in the end of the deposition area. Rock scraping occurred in the entrainment area could increase the degree of rock grinding, and reshape coarse stones into smooth particles of large R values (larger roundness of particles could lead to longer spreading distance in a landslide event, due to the lower internal friction among particles). Based on analysis above, the rock scraping phenomena occurred between the source materials and entrainment materials were confirmed to influence the translation and spread of granular flows in landslides. [ABSTRACT FROM AUTHOR]

Calculating the failure probability of a landslide is important in engineering related to geological process and geomorphological evolution. Strength parameters of a soil (i.e., cohesion and internal friction angle) are regarded as uncertain-but-bounded parameters. In this study, a new method is proposed for computing the landslide failure probability based on a convex set model and artificial neural network (ANN). In the new method, ANN is used to determine the limit state function of landslide stability, and the failure probability is determined using a simple iterative algorithm. The new method was applied to calculate the failure probability of the Gufenping landslide in Nanjiang, Sichuan, China. The results calculated using a Monte Carlo simulation (MCS) method confirmed that the new method accurately and quickly obtains the failure probability of a landslide. Additionally, compared with the two-dimensional calculation method, the one-dimensional analysis method overestimates the failure probability of the landslide. The results of single factor and global sensitivity analysis indicate that the average of internal friction angle is the main factor affecting the stability of the landslide. It is easy to calculate failure probability of landslides using the novel method than using the conventional methods. [ABSTRACT FROM AUTHOR]

This study evaluated effectiveness of the unprecedented rainfall index, TP, proposed by Kosugi (2022) in achieving appropriate evacuation actions by municipalities and residents for reducing victims caused by rainfall-induced landslides and debris flows. TP is defined as the time stamp representing the situation that "current rainfall is the largest since TP"; namely, if we go back to time before TP, the rainfall at the current time is of a magnitude that has been experienced in the past (i.e., the situation is within the historical range) in every evaluation criterion used in analyses. In other words, the rainfall at the current time is of a magnitude never experienced in the period from TP through the present for at least one criterion. TP was computed for all disasters that caused death and missing of people in Japan in 2021: the Atami, Unzen, and Okaya disasters. In every disaster, at the time of the landslide and debris flow occurrences, TP reached the beginning of rainfall record, indicating that the rainfall reached a magnitude never experienced in the past. Based on these results, it was confirmed that (1) TP time series has a large potential to reduce the normalcy bias in all 3 disasters studied, (2) for evaluating anomaly in rainfall, rainfall features should be analyzed based on various evaluation criteria, and (3) the time of past land alteration at an individual location, such as an occurrence time of historical storm event and a construction time of embankment slope, might be set as a TP threshold. [ABSTRACT FROM AUTHOR]

Landslides are a destructive natural phenomenon that can cause significant damage to landscapes, infrastructure, and human lives around the world. The Himalayas, with their active tectonics and varied climatic conditions, are particularly susceptible to landslide hazards. The Danna–Sahotar Landslide in the district of Muzaffarabad, Pakistan, is a prime example of this. Triggered by prolonged rainfall in April 2016, this mass movement caused extensive damage to the area's infrastructure and continues to pose a threat to nearby settlements. To better understand the nature of the Danna–Sahotar Landslide, an integrated approach was used, combining unmanned aerial vehicle photogrammetry and electrical resistivity tomography (ERT) techniques. High-resolution orthomosaic and digital surface model were generated with a spatial resolution of 3 cm/pixel and 6.14 cm/pixel, respectively. These data were then used to analyze the geological, geomorphological, and geometric characteristics of the landslide. The results of this study reveal that the Danna–Sahotar Landslide has multiple failure mechanisms, making it a complex and active mass movement. The ERT results indicate that the sliding mass of the landslide primarily consists of moisture-induced colluvium and debris, which leads to low resistivity in resistivity tomograms. The moisture content of the slope-forming material is a significant contributor to the instability of the landslide. Additionally, a well-developed linear to curvilinear slip surface between the unconsolidated sliding mass and bedrock was identified based on resistivity contrast. In conclusion, this study highlights that, in addition to active tectonics, the hydrogeology of the slope-forming mass is a major factor in reducing the stability of the Danna–Sahotar Landslide. The results of this integrated study can be used to develop effective mitigation measures to prevent future catastrophe. [ABSTRACT FROM AUTHOR]

This work demonstrates the potential of synthetic aperture radar interferometry (InSAR) based on interferometric point target analysis (IPTA) approach for landslide-induced deformation measurement. Twenty-eight Sentinel-1A images acquired between October 14, 2014, and October 27, 2016, are employed in order to measure the surface deformation associated with Kahroud landslide, northern Iran. Due to the lack of geodetic measurements and thanks to capability of processing small baseline highly coherent interferograms in the study area, the conventional small baseline subset (SBAS) time series analysis is exploited for investigation of the IPTA performance. The cross-comparison indicates some unwrapping errors in the IPTA results and removal of a large number of point target candidates due to the remained nuisance terms in the phase measurements, the deviation of the deformation behavior from a simple linear model employed by IPTA and the temporal gap in the available dataset. Two different sliding mechanisms, however, are revealed based on the SBAS time series analysis results. In the second part of the research, the slope stability analysis to characterize the slope dynamics is finally performed based on the deformation field extracted from two ascending and descending single interferograms. Accordingly, the soil shear strength parameters as well as a 3D model of the sliding surface are estimated followed by the calculation of the soil mass involved by the Kahroud landslide. [ABSTRACT FROM AUTHOR]

Large rocky landslides induced by underground mining are the primary type of geological disaster in the karst mountains of southwestern China. In this study, by integrating field surface surveys, interferometric synthetic aperture radar (InSAR) monitoring, and discrete element simulations, we analyzed the deformation evolution of steep cliffs with karst structural planes under mining disturbance. Based on the stress transfer, deformation response, and instability precursors, a landslide instability model was developed. The results revealed that the instability boundaries and the instability mode were controlled by the large structural plane at the top of the mountain. Underground mining accelerated the uneven subsidence of the overlying strata, which led to expansion and penetration of the karst fissure structural planes and fracturing of the rock base, which are the main reason for the instability of the mountain. Underground mining caused internal stress fluctuation of the overlying strata, and the mountain experienced four stages during this process, i.e., development of karst fissure structural planes, fissures expansion and rock base fracturing, further fissures expansion and rock base crashing, and fissures penetration and shear failure. The mining-induced failure of mountain with large karst structural planes can be described as tensile shear-fracturing crashing failure. The InSAR monitoring results revealed that accelerated deformation occurred in the source area starting 20 days before the Baiyan landslide. In addition, 25 potential deformation landslides were identified in Zhijin County, Guizhou Province. Hence, InSAR technology can be used for early-stage detection and warning of similar landslides. [ABSTRACT FROM AUTHOR]

The Nepal Himalayas is one of the world's most active mountain belts and home to widespread natural hazards of various types, including landslides, which claim numerous lives and result in massive property damage in the country. Landslides occur due to the combined effects of seismic activity, monsoon rainfall, and improper land-use practices. The prevention and mitigation of landslides are challenging for countries such as Nepal. However, low-cost techniques such as bioengineering combined with affordable early warning systems have been implemented in recent decades. The Methum landslide in Lalitpur district, central Nepal, was selected as a case study to investigate the landslide geomorphology along with triggering factors and to evaluate the effectiveness of a landslide early warning system (LEWS). Multiple field visits were conducted to learn the patterns of landslide evolution, assess landslide risk, and identify potential triggers. This study analyzed aerial photos, satellite images, and precipitation records. Heavy rainfall and past earthquake events have been major landslide triggers, while sloped terrace farming has acted as a preparatory factor. LEWS, installed, measures rainfall, soil moisture, and displacement activity and generates an alarm to alert nearby inhabitants if any of these parameters exceed the threshold set. This monitoring system is a cost-effective technique and exemplifies the reduction of landslide risk at the community level in the landslide-prone mid-hills of Nepal. [ABSTRACT FROM AUTHOR]

With the increasingly adverse impact of global warming on extreme weather conditions, including landslides, it is more important than ever to alert the public to landslide risks so that people can take precautionary measures. We report the first major project in Hong Kong assessing the public's understanding of landslides and perceptions of the Landslip Early Warning System ("current LEWS") and exploring the perceived usefulness of the concept of a multi-tiered LEWS ("multi-tiered LEWS"). In Study 1, we gauged the public's understanding of landslides and knowledge of the current LEWS by collecting information from five focus groups. That information was used to construct the survey that we administered in Study 2, in which 1834 individuals participated in face-to-face interviews. The results show that only 37% of the participants saw the connection between global warming and landslides. The majority of the sample believed that slope safety has clearly improved over the last decade (88%) and that landslides are a remote concern (91%). Although 90% of the participants were aware of the current LEWS, only 28% were concerned about it because it had little impact on their residential or activity areas. The concept of a multi-tiered LEWS was positively received, although there is an urgent need for further research to demonstrate how to implement this concept with sufficient public education to ensure that it will improve public alertness of landslides. [ABSTRACT FROM AUTHOR]