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Slow and very slow landslides can cause severe economic damage to structures. Due to their velocity of propagation, it is possible to take action such as programmed maintenance or evacuation of affected zones. Modeling is an important tool that allows scientists, engineers, and geologists to better understand their causes and predict their propagation. There are many available models of different complexities which can be used for this purpose, ranging from very simple infinite landslide models which can be implemented in spreadsheets to fully coupled 3D models. This approach is expensive because of the time span in which the problems are studied (sometimes years), simpler methods such as depth‐integrated models could provide a good compromise between accuracy and cost. However, there, the time step limitation due to CFL condition (which states that the time step has to be slower than the ratio between the node spacing Δx$\Delta x$ and the physical velocity of the waves results in time increments which are of the order of one‐10th of a second on many occasions. This paper extends a technique that has been used in the past to glacier evolution problems using finite differences or elements to SPH depth‐integrated models for landslide propagation. The approach is based on assuming that (i) the flow is shallow, (ii) the rheological behavior determining the velocity of propagation is viscoplastic, and (iii) accelerations can be neglected. In this case, the model changes from hyperbolic to parabolic, with a time increment much larger than that of classic hyperbolic formulations. [ABSTRACT FROM AUTHOR]

A massive landslide occurred in Domeshi area, District Muzaffarabad, Pakistan, in two distinct phases: an initial movement on August 1, followed by complete failure on August 4, 2023. The landslide movement persisted for 96 h, with a runout distance of 500 m. The event destroyed numerous residential structures, impacting multiple families, and causing extensive damage to cultivated land and road infrastructure. To comprehensively understand the failure mechanisms, a detailed study was undertaken, encompassing site investigations, unmanned aerial vehicle (UAV) photography, geotechnical and geophysical investigations, petrographic analysis, kinematics, and numerical simulations. The field evidence indicates that the active deformation along the Jhelum Fault (JF) within the landslide's main body weakened the surrounding rock formations. Intense rainfall saturated pre-existing fractures, creating critical zones of weakness. Highly plastic clays along fault plane contributed significantly to volume changes, especially during and after rainfall events. Kinematic analysis identified bedding joints as prevalent failure planes for planar sliding. Geophysical survey revealed a layer of unconsolidated material extending 25–30 m below the landslide's scarp, accompanied by various fractures, including a deep fracture (i.e., JF) up to 300 m depth. Petrographic investigations showed microfractures, micro faults, and intragranular mineral breakage, indicative of intense tectonic stresses. Slope stability analysis indicated factors of safety (FoS) and strength reduction factor (SRF) less than 1, suggesting the potential for further failure in the lower sections of the landslide. Multiple factors, including slope geometry, active tectonics, material composition, and anthropogenic factors (i.e., slope loading and cutting for road and building construction, improper drainage distribution), contributed to the landslide's occurrence, however, the rainfall emerged as the primary triggering event. [ABSTRACT FROM AUTHOR]

Extreme rainfall events, within the context of climate change, pose a heightened risk of geohazards to mountainous regions. On 22 June 2022, a rainstorm-induced landslide-mudflow occurred in a terraced field in Longsheng County, Guangxi Zhuang Autonomous Region, China. The disaster began as a rotational slide, and mobilized into a mudflow with high mobility and long runout, causing significant damage to the local community. This event served as a wake-up call not only for the safety of mountain settlements, but also for the protection of terraced fields as Globally Important Agricultural Heritage Systems. To elucidate the trigger and mudflow mobilization of the event, field investigation, hydrological and agricultural analyses, and laboratory tests were conducted. It was found that the persistent and record-breaking rainfall directly triggered the disaster by increasing pore water pressure. The transition from paddy terraces to dry terraces was deduced to have contributed to a lack of maintenance in the terrace drainage system, thereby heightening the likelihood of landslides. The mudflow mobilization was attributed to excess pore water pressure generated by soil contraction and an undrained condition maintained by low permeability soil. Soil experiencing sliding may be more susceptible to shear contraction, consequently resulting in long-runout motion. Under conditions of increasing extreme rainfall, greater attention needs to be paid to geo-disaster prevention and terraced field protection in mountainous regions. [ABSTRACT FROM AUTHOR]

The dating of landslide movements is a unique opportunity to learn about their past in the absence of archival data. Dendrogeomorphic methods, based on the study of tree rings, offer an exceptional combination of accuracy and temporal range of dating. However, most landslide studies to date have been based on the analysis of macroscopic growth disturbances in trees. In doing so, the use of anatomical growth responses may be more advantageous in some aspects. Thus, this study aims to introduce the spectrum of anatomical growth responses in trees induced by landslide movements for their dating, substantiating their advantages and limitations, and comparing them with each other. Samples from roots and trunks of sycamore maple (Acer pseudoplatanus L.) growing on a landslide of known time of occurrence (May 2010) were used. Geophysical ERT measurements were performed to test the assumption of subsurface influence of landslide movements on tree growth. Based on the results focused on the intensity of changes in vessel lumen area (VLA) and percentage changes of VLA within the tree ring, anatomical changes identified in four different geomorphic situations on the landslide with expected different effects of landslide movements on tree growth were compared with each other. Responses in tree roots were generally significantly stronger compared to those in tree trunks. However, the short time series and difficult cross-dating of roots can be considered as a disadvantage. On the other hand, the identification of anatomical responses in trees with straight trunks (until now practically not used in landslide research) allows a significant expansion of the number of trees usable for landslide dating in the future. [ABSTRACT FROM AUTHOR]

The article discusses the landslides triggered by an extreme rainstorm in the Haihe River Basin, China, in July 2023. The heavy rainfall led to widespread landslides, causing casualties and significant property losses in the Beijing-Tianjin-Hebei region. The study highlights the increasing frequency of extreme weather events and emphasizes the need for vigilance in monitoring and early warning systems for rain-induced geological disasters to prevent future losses. [Extracted from the article]

The reactivation mechanism of multi-slide landslides entails high complexity, and the shear mechanical properties of high groundwater-level landslides are crucial for analyzing the formation mechanism of reactivated landslides. Taking the K39 landslide of Wenma Expressway in Yunnan Province as the research object, we identified the geological and hydrogeological conditions of the landslide, the physical and mechanical properties of the slip zone soil, and the landslide deformation law using geological mapping, geotechnical engineering, indoor testing, and in situ monitoring. The results show the landslide exhibited alternating acceleration and deceleration movements under seasonal heavy rainfall and high groundwater levels. The shear strength of the soil in the deep sliding zone was greater than that of the soil in the shallow sliding zone. The deep and shallow sliding zone soils showed a decrease in shear strength with increased water content. Moreover, the residual strength of the deep sliding zone soil displayed a negative rate with an increased shear rate. In contrast, the residual strength of the shallow sliding zone soil exhibited a positive rate. Furthermore, under different shear rates, the residual internal friction angle and cohesion of the deep sliding zone soil decreased with increased water content, whereas only the residual internal friction angle of the shallow sliding zone soil followed this pattern. Finally, we performed a sensitivity analysis using the GA-BP neural network for the ring shear test parameters of the deep and shallow sliding zone soils, which included consolidation pressure, water content, and shear rate. Our analysis revealed that the residual strength of deep sliding zone soils is most affected by water content, whereas the residual strength of shallow sliding zone soils is most affected by consolidation pressure. Furthermore, it was found that the effect of water content on residual strength is much greater than the effect of shear rate on residual strength for both deep and shallow sliding zone soils. The study results contribute to a unified understanding of how shear rate affects residual strength mechanisms, support research on shear mechanical properties for multiple landslide revivals, and inform engineering practices and policies in landslide-prone areas. [ABSTRACT FROM AUTHOR]

The giant Cuola landslide is one of the largest and highest landslide dams in the upper reaches of the Jinsha River, which brought about a substantial influence on the geomorphological evolution of the Jinsha River valley. Through field investigations, UAV photogrammetry, OSL, and 10Be dating methods, the topographic and geological characteristics of the Cuola landslide dam were examined, and the evolution features of formation and failure were investigated as well. The results indicate that the Cuola landslide, with a volume of ~ 4 × 108 m3 and an age of ~ 50.9 ka BP, was primarily due to a seismic event, and the major failure mode of the ~ 1590-m-high source area on the left bank was attributable to a wedge sliding. The landslide body slid into the valley and climbed ~ 540 m up the right bank with a long runout of 3740 m. The relict landslide deposits demonstrate a large-scale natural dam with a height of 272 m, a length of 1780 m, and a width of 1940 m, which completely blocked the river, and formed a dammed lake with an inundation length of 190 km and an impoundment volume of 10.817 km3. The longevity of the landslide dam was discussed in terms of glacial climate, dam morphology, and material characteristics, and fault dislocation was deduced to induce the overtopping dam break at ~ 4 ka BP. The overflowing water preferentially undercut the weak zone created by fault displacement within the dam until the onset of the final breach, facilitated by the warm climatic conditions of Holocene Climatic Optimum. The findings in this study illustrate the dramatic longevity of a natural dam produced by a giant landslide in one of the most deeply incised valleys in the world and provide a conceptual model for a better understanding of landslide-induced landscape evolution of high-relief river catchments. [ABSTRACT FROM AUTHOR]

Submarine landslides exhibiting extreme geometrical and run-out characteristics have been identified and mapped along most continental margins; raising concerns about potential risks to populations should similar events occur. Hazards associated with such events have frequently been assessed using approximations, resulting in data unsuitable for mitigation strategies. Three approximations appear consequential: (i) addressing the problem in two dimensions, thereby neglecting the effect of complex morphology; (ii) employing a deterministic approach that disregards uncertainty related to the heterogeneity of sediment properties; and (iii) treating the sediment as a perfectly elastic–plastic material, simplifying the mechanical behaviour and overlooking the degradation of sediment mechanical properties (strain softening) during different phases of slope movement. Here, we introduced the strain-softening behaviour into a 3D slope stability model. Identification of the critical failure surface was conducted in terms of the probability of failure, considering the influence of sediment parameter variability and uncertainty on the likelihood of failure. The developed model was then used to assess the slope stability of a well-studied example from the literature, the Nice slope (SE France). Our findings indicate that neglecting lateral morphological changes leads to an overestimation of the probability of failure. Additionally, we demonstrated that strain-softening behaviour could significantly affect the factor of safety and the probability of failure for the studied slopes. We argue that a risk assessment and definition of a mitigation strategy require well-advanced characterisation of the mechanical behaviour of sedimentary layers and an analysis incorporating the complex morphology of submarine slopes. [ABSTRACT FROM AUTHOR]

An extreme rainstorm hit Fuyang District, Zhejiang Province, China, on 22 July 2023. A record-breaking 320 mm of rain fell in less than 48 h. In the early evening of 22 July, more than 145 shallow landslides emerged in a 20 km2 hilly area of Fuyang amid turbulent flash flooding and claimed five lives. Post-event field investigations and laboratory experiments were urgently carried out, targeting two geologically typical shallow landslides, to decipher the initiation mechanism and distributing features of the regionally distributed landslides. The main findings are summarized as follows: (1) The week-long rainfall preceding the event and the intense storm on 22 July are presumably primary triggers for the landslides. Besides rainfall, convergent topography of hollows may be another important factor influencing the initiation of landslides. (2) The dominant moso bamboo has an extensive and interconnected root system, which could significantly stabilize slopes by enhancing soil strength and restricting cracks. (3) The sedimentary Heshangzhen Group area is more susceptible to landslides than the igneous Shuangxiwu Group area. Bedrock lithologies affect landslide distribution through controlling soil structures and properties to shape the hillslope hydrology and also through influencing landscape morphology. This study offers new evidence and insights into the lithological control on landslide initiation and distribution. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of a slope stability method for rainfall-induced shallow landslides in CRITERIA-1D, which is an agro-hydrological model based on Richards' equation for transient infiltration and redistribution processes. CRITERIA-1D can simulate the presence and development of roots and canopies over space and time, the regulation of transpiration activity based on real meteorological data, and the evaporation reduction caused by canopies. The slope can be considered composed of a multi-layered soil, leading to the possibility of simulating the bedrock and of setting an initial water table level. CRITERIA-1D can consider different soil horizons characterized by different hydraulic conductivities and soil water retention curves, thus allowing the simulation of capillarity barriers. The validation of the proposed physically based slope stability model was conducted through the simulation of the collected water content and water potential data of an experimental slope. The monitored slope is located close to Montuè, in the north-eastern sector of Oltrepò Pavese (northern Apennines—Italy). Just close to the monitoring station, a shallow landslide occurred in 2014 at a depth of around 100 cm. The results show the utility of agro-hydrological modeling schemes in modeling the antecedent soil moisture condition and in reducing the overestimation of landslides events detection, which is an issue for early warning systems and slope management related to rainfall-induced shallow landslides. The presented model can be used also to test different bioengineering solutions for slope stabilization, especially when data about rooting systems and plant physiology are known. [ABSTRACT FROM AUTHOR]

As a response to rapid tectonic uplift and intense river incision, giant river-damming landslides have frequently occurred in the mountain and canyon region of the southeastern Tibetan Plateau. These landslides in turn can transiently affect the longitudinal profile evolution of rivers. The ca. 440 Mm3 channelized Linka rock avalanche initiated as a giant translational rockslide on the south-facing dip slope of the NW-trending divide of the Waqu catchment. The detached rock mass involved the thick and resistant Wada limestone, and the underlying Basu red-bed unit, comprising sandstone, conglomerate, and siltstone. The NWW-striking bedding planes and the SE-dipping joint set bounded the landslide source area at its base and sides, respectively, and a buckling failure controlled by the high-angle SW-dipping joints at the slope toe induced this giant mass-movement event. The rock slope failure was likely triggered by an earthquake that occurred at ca. 5.5 ka before present, probably corresponding to a prehistorical rupture on the active Bianba-Luolong Fault Zone. The confined rock avalanche deposits, covering ca. 5.5 km2, rode up both of the constraining ridges that laterally constrained the motion path. The instantaneous velocity as the rock avalanche traveled through the distal bend of the motion path was calculated to be ca. 34 m/s using a superelevation-based equation. The rock avalanche traveled a horizontal distance of ca. 5970 m with a vertical drop of 1380 m, displaying a high mobility (H/L index value of 0.231), which can be attributed primarily to dynamic rock fragmentation. This giant mass-movement event completely blocked the Waqu River with a ca. 170-m high dam. This natural dam has experienced breaches at least 6 times (including 1 seepage failure and 5 subsequent overtopping failures), and the largest peak discharge from the 1st seepage failure was estimated to be ~ 19,500 m3/s. The formation and breaching of the natural dam have caused a transient but extremely profound influence on the longitudinal profile evolution of the Waqu River. This research involving landslide initiation, displacement, deposition, and the formation and breaching of the landslide dam can help enhance the understanding of effective risk assessments of landslide-induced disaster chains in high-relief regions. [ABSTRACT FROM AUTHOR]

The sudden and unpredictable breach of landslide dams in the Yarlung Tsangpo river basin usually causes megafloods, posing great risks to human lives and infrastructures in the downstream areas. This study proposed an efficient and quantitative risk assessment framework of breach floods caused by landslide dam failures in the mainstream and tributaries of the Yarlung Tsangpo river basin with limited data. The impact of dam breach floods on human risks was evaluated. The flood attenuation along rivers, strategies for mitigating overlapping floods, and sensitivity analysis of human risks were also discussed. The results show that the developed framework successfully assessed flood risks caused by the breach of landslide dams. The flood attenuation ratio increased with river length but decreased with the peak discharge at dam site. A higher peak discharge and a larger inundated area downstream were predicted when the breach floods of two landslide dams, one in mainstream and the other in a tributary, overlapped at the confluence. The overlapping flood could be mitigated by reducing peak discharges of the two landslide dams or increasing time interval between the two peaks. The simulations also outlined the downstream peak discharge resulting from the cascading breach was larger than that of a single dam. However, it was smaller than the combined peak discharges of two separate dams, because the erosion during the breach of the downstream dam incurred energy dissipation. The human risks in the Pasighat village were greater when overlapping flood occurred due to the increased water depth and more hazardous inundated buildings. In the case of multi-peak floods, the warnings for the former peak flood would also warn the peak flood thereafter when individuals were notified multiple peaks. Otherwise, individuals might be misled by the warning of the previous peak flood, resulting in catastrophic flood impacts. A parametric analysis indicated that early evacuation warnings were needed to avoid serious loss of life and flood damages, especially in cases of dam breaches occurring at nighttime or for areas in close proximity to the dam site. [ABSTRACT FROM AUTHOR]

Landslides frequently produce dams that can cause widespread destruction when breached. Several factors influence the formation of landslide dams. This study used redundant analysis and hierarchical cluster analysis to investigate geomorphic features contributing to the formation of landslide dams. Eight landslide geomorphic features corresponding to the width of the river beneath the landslide and four geomorphic indices were selected to explore the geomorphic characteristics of 28 river-damming and 59 nondamming landslides in Taiwan. The redundancy analysis revealed that the geomorphic features derived for the river-damming landslides differed from those derived for the nondamming landslides. The width of the river beneath the landslide was noted to be strongly correlated with the aspect of nondamming landslides. Hierarchical cluster analysis was used to cluster the river-damming landslides into three clusters based on the geomorphic features influencing dam formation. Based on these clusters, the landslide site may be damming rivers under different geomorphic combination conditions. [ABSTRACT FROM AUTHOR]

The present study uses the Jiweishan landslide in Wulong District, Chongqing City, China, as an example to design a numerical simulation technique to examine two environmental consequences: corrosion deterioration and underground mining. A shear strength prediction model for the corrosion rate of the structural plane was thus proposed by mimicking the time-dependent weakening characteristics of structural planes by using strength reduction. The discrete element simulation method was applied to reproduce the complete process of dangerous rock body movement and slope instability under the effects of these two factors to clarify the influence of environmental and engineering effects on slope deformation behaviour. This was intended to clarify the overburden movement process, fracture evolution law, and rock formation subsidence characteristics of karst mountains affected by mining from the perspective of geomechanics, and the results showed that the deformation evolution process of the Jiweishan landslide can be divided into two stages: the slow chronic deformation caused by long-term corrosion deterioration and the more severe acute deformation caused by short-term underground mining. The overburden movement and fracture evolution seen under corrosion deterioration and underground mining thus represent a progressive evolutionary process with full spatial and temporal continuity. Corrosion is a long-term synchronous process that creates the transformation of numerous structural planes around a dangerous rock body. Underground mining, in contrast, is typically a short-term trigger factor that can nevertheless easily cause significant differential adjustment of the movement characteristics of different parts of a dangerous rock body that result in distinct spatial differentiations of dangerous rock body movement. Further, the subsidence deformation characteristics of the rock formation plane mainly depend on the spatial position and scale of the goaf created by such mining. Analysing the stress characteristics of the relevant inclined rock strata helps clarify the control mechanism of a mountain's spatial structure's adaptive adjustment. The simulation results in this case were also compared with the results of centrifuge tests and physical model tests to verify the rationality of the simulation scheme, and the conclusions thus remove provide an important reference for the study of landslide development processes and instability mechanisms under multi-factor coupling conditions. [ABSTRACT FROM AUTHOR]

Studying the formation mechanisms and influencing factors of typical regional landslides is a crucial step in landslide control and disaster prediction. The mechanism of formation and the influence of hydrodynamic factors on accumulated landslides in the Three Gorges Reservoir have always been a focus of attention. Although rainfall and reservoir water level factors interact with each other, refining the impact areas and characteristics of these two factors will be beneficial for future research on accumulated landslides in the Three Gorges Reservoir area. Taking the Sanmendong landslide as an example and relying on its geological background and professional monitoring data, this study employs gray correlation analysis to analyze the sensitivity between changes in hydrodynamic factors and changes in landslide safety factor. It discusses the regions affected by rainfall and the descending speed of the reservoir water level under different conditions, as well as the changing patterns of data. The ILF-FFT landslide prediction model is utilized to study the relationship between real monitoring data of the Sanmendong landslide and fluctuations in hydrodynamic factors, obtaining the weight coefficients of hydrodynamic factors in the model and separating the fluctuation curve under the influence of rainfall and reservoir water level fluctuations. A comparative analysis of the contribution of hydrodynamic factors to the deformation of the Sanmendong landslide is conducted. The results indicate that the overall deformation of the Sanmendong landslide is more sensitive to rainfall factors, and as the descending speed of the reservoir water level increases, its sensitivity to landslide deformation will also increase. The local deformation of the landslide toe is greatly affected by the factors of reservoir water level descent, and with the increase of the descending speed of the reservoir water level, the correlation results also increase. It is important to note that there is a sudden change in the correlation results at a descending speed of 1.0 m/d in the local stability analysis, which is related to the most dangerous sliding surface identified in the simulation calculation and requires special attention during the process of reservoir water level descent. Furthermore, the deformation of the landslide toe in Sanmendong is greatly affected by the descent of the reservoir water level, while the deformation of the landslide head is more influenced by rainfall factors. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) is a remote sensing tool used for monitoring urban areas affected by geological hazards. Here we analysed the effectiveness of stabilization works on a slow-moving landslide in Arcos de La Frontera (Cádiz, Spain) using a persistent scatterer interferometric approach. The works consisted on jet grouting of cement-based injections and were applied locally to stabilize the most damaged neighbourhood. We processed a large stack of Sentinel-1 SAR satellite acquisitions covering the period January, 2016, to March, 2023, and obtained surface velocity and displacement trends measured along the line of sight (LOS) of the satellite on both ascending and descending orbits. The results show a clear deceleration of the landslide head after mid-2018, suggesting the local stabilization works were effective after that time. Prior to mid-2018, the maximum LOS velocity of the landslide head was 2.2 cm/year in ascending orbit and 1.3 cm/year in the descending orbit, decreasing to 0.43 cm/year and 0.23 cm/year, respectively. The InSAR results were compared to in-situ monitoring data and revealed that the extent of the stabilization has influenced a much larger area beyond the zone of the local interventions. Overall, InSAR has proved a powerful and versatile tool to be implemented in operational geotechnical monitoring. [ABSTRACT FROM AUTHOR]

Catastrophic landslides often occur along the southeastern margin of the Tibetan Plateau because of strong earthquake/faults and heavy rains. In this study, 26 large-scale landslides were collected from the middle segment of the Yalong River to analyze landslide features and possible formation mechanism. The investigation results revealed that landslides featuring a linear distribution along the riverbanks can be classified into three failure types: tensile cracking-sliding, sliding-bending (crushing and buckling), and toppling. Among them, the Xiamajidian landslide at the junction area between the river and the Qianbo fault is being dangerous with obvious deformation, including different subzones and different failure types. The landslide body is delineated into three distinct zones (A, B, and C) based on different deformation features and material compositions. Among them, the Zone A with the largest deformation is dangerous, the front of which is obviously moving toward the river channel. The deformation monitoring data indicated that the 2008 Ms 8.0 Wenchuan earthquake caused only slight disturbances to the Xiamajidian landslide body, but the subsequent 2008 Ms 6.1 Huili earthquake caused the deformation to increase quickly. The distinct-element method is then used to determine the importance of strong earthquakes and heavy rainfall during landslide failure. The results suggest that the landslide may have been broken to form a large landslide event, and finally to form a large landslide dam to block the Yalong River. The results presented in this paper are helpful for disaster prevention and risk evaluation. [ABSTRACT FROM AUTHOR]

Multiple barriers are usually installed to effectively mitigate large-volume debris flows. Existing multiple-barrier design recommendations ignore the effects of solid–fluid interaction and may cause uncertainties in estimating the flow impact force on subsequent barriers. In this study, a fully coupled, two-layer, two-phase material point method with an incompressible fluid phase is implemented and validated using the experimental results of dry sand, water and sand–water mixture flows impacting on rigid barriers. Numerical parametric study is carried out using the validated model to investigate the effects of Froude number (Fr) and barrier spacing on second barrier impact force. A debris flow volume of 500 m3 is modelled, with the Fr ranging from 2 to 6; these are values relevant to the gentle and steep terrains in Hong Kong. Simulation results show the importance of changes in fluidisation ratio, which is the ratio of basal pore fluid pressure and total stress. The debris flow, after impacting the first barrier, overflows and lands on the slope between the two barriers. The fluidisation ratio of debris flow after landing increases by up to 30%, leading to an increase of impact velocity at the second barrier by up to 80%. Consequently, the impact force of debris flows on the second barrier is underestimated by up to 50% compared with existing design guidelines. This implies that neglecting the fluidisation ratio may lead to non-conservative design of multiple barriers. [ABSTRACT FROM AUTHOR]

It is well recognised that the rheology of an interstitial fluid has profound effects on debris flow dynamics. Existing design guidelines for multiple resisting barriers, however, do not consider the rheological behaviour of debris flows explicitly while estimating barrier spacing. A modified overflow equation is proposed, considering an improved overflow trajectory and a new yield stress-based velocity attenuation model, to estimate the overflow distance in two-phase flows. This study experimentally investigates the effects of yield stress of an interstitial fluid on the impact behaviour of two-phase flows with similar Froude numbers against dual-rigid barriers. The flow composition is varied to model a wide range of natural flows based on dimensionless yield stress. The results reveal that the hydrodynamic impact coefficient increases with increasing dimensionless yield stress and exceeds the existing international design guidelines by more than 13%. The existing guidelines underestimate the overflow distance by up to 115%. The modified formulation is verified and conservatively predicts overflow distance for water floods to debris flows. It is recommended that the dimensionless yield stress of interstitial fluid together with Froude number should be used to characterise the impact and overflow dynamics of two-phase flow for a conservative design of dual barriers. [ABSTRACT FROM AUTHOR]

Assessing the stability and the determining the critical slip surfaces (CSS) represent two significant endeavors concerning 3D seismic landslides. The minimum potential energy method is applied to evaluate the stability, with specific improvements proposed. Specially, the mobilized shear stress on slip surface is derived through the static equilibrium condition on x-axis direction of the sliding mass. The movement trajectory of a landslide (moving direction) is jointly determined by the forces acting on the sliding mass and is equivalent to the vector sum of these forces. Seismic acceleration is characterized using the pseudo-dynamic method, capturing the temporal and spatial variations in seismic forces. By considering the total potential energy of landslide as the objective function, a novel insight for identifying CSS of seismic-induced landslides is developed. A comparison and analysis show that the relative errors between the safety factor(SF) obtained using the proposed method and limit equilibrium method are less than 3%. Results also indicate that the proposed model can effectively determine the location and shape of CSS against 3D seismic landslides. A real case study shows that the proposed method not only accurately evaluates the stability and location of the slip surface of an actual slope but also yields spatial characteristic parameters of the sliding mass that are close to the measured values. Finally, a sensitivity analysis is conducted on several parameters affecting the stability of seismic-induced landslide. [ABSTRACT FROM AUTHOR]

Landslides resulting in complete river blockage have frequently occurred in the Three River Region (TRR) during the geomorphological evolution of the Tibetan Plateau. River-damming landslides occurring in low-relief regions of the TRR have received less attention compared to those in deeply-incised valleys. The 2.5 Mm3 Cuoduoqin rockslide originated from the south-facing hillslope of a southeast-east-trending ridge, leading to complete blockage of the Quzhaqu River. The original failure mainly involves blocky metamorphic limestone and phyllite. The Quzha Lake Fault providing rear rupture and two other groups of joints facilitating sidewise and toe releases are considered predisposing factors contributing to slope instability. Ongoing tectonic uplift and cyclic glaciations are considered preparatory factors, shifting the slope from stable to marginally unstable. A prehistoric earthquake, likely corresponding to an ancient rupture event on the active Nujiang Fault Zone (NJFZ), is deemed as the most probable trigger for this large rock slope failure. The 2D discrete element method (DEM) software UDEC is utilized to analyze the static slope stability and to reproduce the kinematic process of the rockslide. The static analysis indicates that the original rock slope was in equilibrium under natural conditions. The kinematic process can be divided into three phases: initial detachment within seconds after applying seismic load, downslope acceleration after crossing the slope knickpoint, and accumulation after traveling into the valley bottom. This case study, focusing on the development and kinematics of the Cuoduoqin rockslide, can help enhance the understanding of effective risk assessments of landslides in high-altitude and low-relief regions. [ABSTRACT FROM AUTHOR]

Damage to buried pipes under seismic landslide actions has been reported in many post-earthquake reconnaissance. The landslide-pipe problem in the technical literature has been often investigated using simplified analytical methods. However, the analytical methods ignore the real mechanism of pipe response under natural dynamic slope instability. The dynamic slope instability is significantly influenced by its lateral boundary interface (LBI) characteristics. In this study, slope-pipe interaction (SPI) under seismic loading, focusing on the effect of LBI properties, is evaluated by continuum numerical simulation using the SANISAND constitutive model in FLAC3D. The results show that the geometry of the failure mass varies from 2D to 3D by increasing the stiffness at the slope boundaries (from smooth to hard) and the maximum pipe deformation decreases by around 40%. Moreover, the response components of maximum axial stress, bending moment, and shear stress of the pipe occur at the end sections of the buried pipe and near the boundaries of the landslide zone. However, the maximum pipe deflection occurs in the middle section of the pipe. The results of shear force-shear displacement curves demonstrate that the soil-pipe interaction stiffness is variable along the pipe length and can be estimated by a hyperbolic equation. [ABSTRACT FROM AUTHOR]

By 2022, the total length of roads in Tibet Autonomous Region reached 121,447 kilometers. Due to the unique geological conditions in Tibet, various natural disasters such as earthquakes, mudslides, landslides, avalanches, and strong winds frequently occur. Along the Sichuan-Tibet Highway alone, over 300 disasters happen each year, significantly impacting the region's economic development. This study focuses on the complexity and randomness of natural disaster mechanisms and combines Markov chain theory to improve the accuracy of prediction data for mudslides, landslides, and earth subsidence etc. The main method is to modify the state interval of the prediction model parameter-Markov chain based on the distribution of discrete points on the number axis. The following state interval division methods are proposed:(1) If the relative error of the predicted value exceeds 50%, adjust the prediction model. (2) Obtain the lower bound of state E1 by taking the floor value downward. (3) The width of each interval does not need to be uniform. (4) Arrange continuous, dense, and close points on the number line in the same side in batches, and represent a state continuously, dividing it into one suitable interval or batches. Using this method, an improved RMSE of 0.28mm and MAPE of 0.87% were obtained for engineering examples, outperforming other models such as GM(1,1), Verhulst, DGM(2,1) with corresponding RMSE values of 0.86 mm, 0.69 mm, and 1.38 mm, and MAPE values of 2.75%, 2.53%, and 5.99%. The combined prediction results for five sets of data yielded an RMSE of 0.14 mm and MAPE of 0.56%, which are quite close to the results obtained using Markov selection correction with an RMSE of 0.37 mm and MAPE of 1.01%. Furthermore, comparing the four sets of case, the average reduction in RMSE and MAPE is 3.56mm and 1.72%, respectively, demonstrating that this method can further improve the performance of Markov chain prediction. [ABSTRACT FROM AUTHOR]

There are a vast number of large-scale ancient landslides in the east Tibetan plateau. However, these landslides have experienced reactivation in recent years and resulted in increasingly serious casualties and economic losses. To study the reactivation mechanism and early identification of ancient landslides on the eastern margin of the Tibetan Plateau, high-resolution remote-sensing interpretation, field survey, interferometric synthetic aperture radar (InSAR) monitoring, laboratory and in situ geotechnical tests, physical modeling tests, and numerical simulations were used, and the main results obtained are as follows. The development and distribution of ancient landslides on the eastern margin of the Tibetan Plateau were clarified, and an efficient identification method was proposed. Reactivation characteristics, triggering factors, and typical genesis patterns were analyzed. Second, the macroscopic mechanical properties of gravelly slip-zone soil and their strength evolution mechanisms at the mesoscale were revealed, and then the strength criterion of gravelly slip-zone soil is improved. Third, combined with typical cases, the reactivation mechanism of ancient landslides under different conditions is simulated and analyzed, and a multistage dynamic evolution model for the reactivation of ancient landslides is established by considering key factors such as geomorphic evolution, coupled endogenic and exogenic geological processes. Finally, an early identification method for ancient landslide reactivation was proposed, enabling rapid determination of the evolutionary stage of ancient landslide reactivation. These findings provide new theoretical and technical support for effectively preventing the risk of reactivation disasters of ancient landslides on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Landslide disasters have inflicted incalculable losses on China's national economy, as well as on lives and property. Notably, 90% of landslide disasters are directly induced by rainfall or have indirect associations with it. In Bazhong City, Sichuan Province, China, the proportion of rainfall-induced landslides accounts for more than 70% of all geological disasters in the region. Our research undertook a susceptibility analysis of multimodal landslide data in Bazhou District of Bazhong City, employing four distinct machine learning methods: decision trees (DTs), random forests (RFs), support vector machines (SVMs), and back-propagation neural networks (BPNNs). Additionally, data from the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation product were utilized to develop a rainfall intensity-duration (I-D) model for the Bazhou District. The experimental results indicated that the BPNN achieved the highest overall classification accuracy, reaching 92.00%, which was 3.00% to 6.00% higher than those achieved by other algorithms. The kappa coefficient for BPNN was 0.84, surpassing other algorithms by 0.06 to 0.10. Furthermore, our results demonstrated that the rainfall I-D model had a prediction accuracy of 90.91% for rainfall-induced landslides. Finally, a probability quantification model for landslide triggering factors was established based on the previous two research results, aimed at meteorological warning. Comparisons with five recorded landslide events in 2009 revealed that the experimental outcomes of the meteorological early warning model aligned with the actual inspection results. Therefore, this model can serve as a reliable reference for issuing warnings about rainfall-induced landslides in Bazhou District. [ABSTRACT FROM AUTHOR]

The slope failure and the debris flow at Chandmari and Sichey within the city of Gangtok in Sikkim, India, have been numerically analyzed using the distinct element method. The published data on the failure surface and the extent of the debris flow, which followed a 5-h, 210-mm rainfall event in 1997 at Chandmari, have been used to verify the adopted methodology. The numerical simulations have revealed that the runout distances predicted for the landslide are in agreement with the actual ground observations. After a satisfactory simulation of the Chandmari landslide, the instability and the debris flow at Sichey, located on the backside of the same mountain, were studied. The results indicated that a collapsed building noticed during a 2019 field visit to Sichey is located within the predicted failure surface. The debris flow after the ground saturation originates from two distinct parts of the Sichey slope, located near the toe and the crown of the potential failure surface. The debris flow from the lower slope failure, near the toe of the critical slip surface, has a longer runout distance and higher velocity than the debris flow from the upper slope failure near the crown of the critical slip surface. Due to a broad and relatively flat road bench located at the middle portion of the failure surface, these two distinct debris flows might not get a chance to merge to form a major destructive force entirely, and thus, the locality in the down slope might escape a total inundation. [ABSTRACT FROM AUTHOR]

Bioinspired slope improvements can achieve outcomes similar to traditional slope improvements for shallow slope failures, while incorporating plant material as a structural component and using a minimum of heavy equipment. Vetiver grass can mitigate the rain-induced slope instability of earthen infrastructure, such as levees, constructed using loess and clay soils. Vetiver grassroots can extend to depths greater than 3 m (10 ft), creating a new composite material with the grassroots and soil, thereby increasing shear strength to combat shallow slope failures. The objective of this study is to determine the feasibility of vetiver as a climate-resilient bioinspired slope stability improvement on a test levee constructed of loess in Vicksburg, Mississippi (MS). Vetiver was planted at 1 ft center-to-center intervals on a 9.1 m wide (30 ft) section of an approximately 12.2 m long (40 ft) downstream slope of a test levee and observed for 2.5 years. To consider the effect of extreme precipitation events, a finite element analysis was completed for a comparable clay slope using 500 year precipitation intensity–duration–frequency curves of Jackson, MS. Precipitation negatively impacts the collapsible and expansive nature of the local loess and clay, respectively. The results demonstrate that vetiver grass is a viable method to increase slope stability for earthen levees constructed with loess and clay, which are prevalent in Vicksburg and Jackson, respectively. Vetiver also holds promise as a climate resilient solution to combat rain-induced shallow slope failures. Practical Applications: As society advances toward a more sustainable approach to managing infrastructure, traditional methods of using heavy machinery, concrete, and steel to repair landslides are being replaced with using minimal equipment, earthen and geosynthetic materials, and vegetation. Earthen infrastructure, such as dams and levees, provide protection against the flooding of basins, rivers, lakes, and other bodies of water; however, they are not immune from landslides. One common landslide that can occur in earthen infrastructure is a shallow slope failure, particularly in soil that destabilizes greatly with changes in climate, such as collapsible and expansive soils. The researcher is proposing to use vetiver grass to combat shallow slope failures in earthen infrastructure in MS. Vetiver grassroots can grow to depths greater than 3 m (10 ft), which exceeds the depths of shallow landslides. Vetiver was transplanted on a test levee that was no longer being used in Vicksburg to determine if vetiver could grow in loess. Further, computer modeling was done to predict the performance of vetiver on a levee constructed in clay following storms with an estimated return period of 500 years for Jackson. The use of vetiver was determined to reduce the possibility of shallow slope failures under the conditions specified. [ABSTRACT FROM AUTHOR]

ABSTRACT The socioeconomic prosperity of a region is intricately tied to its infrastructure development, particularly the connectivity provided by highways. Preserving the economic well‐being of an area demands meticulous planning and the execution of construction projects with minimal risk. Landslide hazard zonation (LHZ) is a crucial tool for assessing the likelihood of landslide occurrences in specific regions. This study delves into the LHZ of a 171.2 km2 area, focusing on critical causative factors such as slope inclination and aspect, slope mass material, Land Use/Land Cover (LULC), distance from fault lines, roads, and drainage systems. The classification resulted in four distinct zones along the National Highway (NH‐109) stretch from Bhowali to Almora. Notably, the moderate‐hazard zone encompasses 60.4% of the area, followed by the high‐hazard zone at 24.4%, the low‐hazard zone at 11.9%, and the very high‐hazard zone at 3.3%. To enhance the reliability of the study, 63 previously identified landslide locations were marked based on diverse sources, including field studies and landslide inventory data from the Geological Survey of India (GSI). In the validation process, 12 past landslide occurrences were situated within the very high‐hazard zone, 32 in the high‐hazard zone, 18 in the moderate‐hazard zone, and only one in the low‐hazard zone. The receiver operating characteristic (ROC) curve yielded a commendable quality with a % area under the curve (AUC) of 71.2%. In summary, this research underscores the importance of LHZ in assessing and mitigating landslide risks along a stretch of the NH‐109 corridor. The findings provide valuable insights for informed decision‐making in infrastructure development and risk management, contributing to the sustainable growth and protection of the region's livelihood. [ABSTRACT FROM AUTHOR]

In August 2022, Pakistan experienced unprecedented monsoon rains, leading to devastating floods and landslides affecting millions. While previous research has mainly focused on the contributions of seasonal and synoptic anomalies, this study elucidates the dominant influences of tropical and extratropical intraseasonal oscillations on both the occurrence and subseasonal prediction of this extreme rainfall event. Our scale-decomposed moisture budget analysis revealed that intense rainfall in Pakistan was triggered and sustained by enhanced vertical moisture transport anomalies, primarily driven by interactions between intraseasonal circulation anomalies and the prevailing background moisture field when tropical and mid-latitude systems coincided over Pakistan. Evaluation of subseasonal-to-seasonal prediction models further highlighted the critical role of tropical intraseasonal modes in causing this extreme rainfall event in Pakistan. Models that accurately predicted northward-propagating intraseasonal convection with a forecast lead time of 8–22 days demonstrated good skill in predicting the extreme event over Pakistan. [ABSTRACT FROM AUTHOR]

Background: La Palma, like many regions of the world, may be affected by various hazards, with different levels of susceptibility, which have a greater or lesser impact on society. In this context, a multi-risk approached model capable of estimating the total risk faced by the territory is required, considering the different hazards to which it is exposed (floods, earthquakes, volcanic eruptions and landslides) and the different conditions of vulnerability of both the population and the exposed assets. Methods: The methodology employed in this study has been referred to as "multilayer single hazard", which consists of the aggregation or superposition of hazards located in the same space. As usual, the identification of the individual hazard of each one of them was carried out using heterogeneous methods, which is why an index is proposed to homogenize the hazards and make them comparable with each other. On the other hand, the model also integrates a prototype to evaluate vulnerability from a multiple perspective (vulnerability by hazard) and shows the aggregated information in maps at a medium representation scale (definition of 250 m). Results: The risk integration results show that 5% of the island is at high risk levels. Simultaneously, the south of the island is presented as the area that shows the highest risk by combining a high hazard, especially related to volcanic hazards, and a high exposure, as it is home to a significant volume of population. Conclusions: The proposed methodology is also interesting because it can be replicated in other areas of study. [ABSTRACT FROM AUTHOR]

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety. [ABSTRACT FROM AUTHOR]

Whether Earth materials exhibit frictional creep or catastrophic failure is a crucial but unresolved problem in predicting landslide and earthquake hazards. Here, we show that field-scale observations of sliding velocity and pore water pressure at two creeping landslides are explained by velocity-strengthening friction, in close agreement with laboratory measurements on similar materials. This suggests that the rate-strengthening friction commonly measured in clay-rich materials may govern episodic slow slip in landslides, in addition to tectonic faults. Further, our results show more generally that transient slow slip can arise in velocity-strengthening materials from modulation of effective normal stress through pore pressure fluctuations. This challenges the idea that episodic slow slip requires a narrow range of transitional frictional properties near the stability threshold, or pore pressure feedbacks operating on initially unstable frictional slip. [ABSTRACT FROM AUTHOR]

Background: The landslide inventory of the western flank of Mount Oku, Cameroon, includes spreads or complex landslides, indicating sudden soil weakening, possibly due to seismic activity or heavy rainfall causing groundwater rise. These landslides were likely triggered between 2009 and 2018 based on the dates of the aerial imagery. Identifying triggers for past landslides remains a major unresolved issue in landslide science. However, understanding these triggers is crucial for accurately assessing future landslide hazards. Methodology: In this paper, we investigate the possibility of earthquakes to precondition landslide development or reactivation during climatic events. By assuming a magnitude 5.2 earthquake, an epicenter of 10 km from this area, and different wetness conditions, the factor of safety (FS) and Newmark displacement (ND) models were calculated for shallow and deep-seated landslides with sliding depths of 3 and 7.5 m. Afterward, the relationship between FS, assumed ND, and observed landslides was analyzed in a cluster analysis, to derive patterns of climatically and seismically triggered landslides. Results: The comparison of FS maps and FS values of the observed landslides revealed that especially for landslides at 7.5 m depth, most sites that are stable during dry conditions become instable under saturated conditions, indicating a climatic trigger. At 3 m depth, however, some landslide sites that are still marginally stable under saturated conditions, display relatively high ND values for the investigated hypothetical earthquake, indicating a possible seismic influence. In the cluster analysis, we clustered the observed landslides according to their distances to rivers and topographic ridges and obtained three clusters. Landslides from cluster 3 with 31% of the landslides display medium to high ND for the assumed earthquake, and were found near ridges and farther away from rivers, suggesting seismic triggering. Cluster 2, with 12% of landslides closer to rivers, suggested climatic origins. Thus, while climate is a critical landslide contributing factor, seismic events may also contribute, either by predisposing to landslides or by reactivating them alongside climatic factors. These results enable the establishment of more precise and effective landslide mitigating measures considering mostly rainfall but also earthquakes as possible triggers. [ABSTRACT FROM AUTHOR]

AbstractChanges in reservoir water level often trigger landslides along the reservoir banks, which are common geological hazards that significantly affect the construction and operation of hydropower projects. In this paper, the stability of the RS reservoir landslide in the southwestern region of China is investigated. A discrete element seepage analysis model is constructed based on the theory of changes in the infiltration surface of the deposit caused by water level drawdown. The model is then embedded into a continuous-discontinuous three-dimensional numerical simulation method to systematically investigate the influence of reservoir water level decrease on the stability of the RS deposit. The results indicate that, under the condition of a sudden drop in reservoir water level decrease, the deposit experiences destabilization failure. Its sliding velocities are mainly influenced by seepage forces and gravity, while sliding displacements are influenced by seepage forces and topographic conditions. The landslide process shows distinct stratification characteristics. The portion of the deposit that slides into the river channel is mainly distributed between an altitude of 2655 m and 2870 m. The research results provide a scientific basis for studying the mechanism of slope failure and landslide disaster chain under the condition of reservoir water level lowering. [ABSTRACT FROM AUTHOR]

• Thoroughly compare XGBoost, RF, LR, and SVM models optimized by TPE algorithm. • Introduce SHAP algorithm to CLHA; global interpretation reveals factor importance. • Combine local unit interpretations and field surveys to reveal correlations. In the coseismic landslide hazard assessment (CLHA), advanced machine learning (ML) models have garnered significant attention due to their effectiveness in handling the complex relationships between landslides and various influencing factors. However, explaining the decision-making mechanisms of machine learning models that predict landslide spatial distribution based on these influencing factors remains challenging. This study compares the predictive performance of four models—XGBoost, RF, LR, and SVM—optimized using the TPE algorithm, and introduces the SHAP algorithm into the XGBoost model to achieve both global and local interpretations of CLHA. In various tests, the optimized XGBoost model demonstrated the best predictive performance, achieving an accuracy of 0.864 and an AUC value of 0.886. Global interpretations indicate that the occurrence of coseismic landslides is primarily influenced by triggering factors such as hypocentral distance and distance from the seismogenic fault. Terrain roughness and elevation, on the other hand, make significant contributions among the conditioning factors. Single-factor dependence plots indicate that the contribution of individual factors to landslides varies across different ranges of their feature values. Analysis of two-factor dependence plots reveals that interactions between factors are also crucial in influencing the occurrence of landslides. Combining field surveys with local interpretations confirms significant variations in the contributions of influencing factors within local ranges. The main innovation of this study lies in the integration of the SHAP algorithm into the CLHA model, revealing the decision-making mechanisms of the model for spatial prediction of coseismic landslides. [ABSTRACT FROM AUTHOR]

• Local and global XAI models explain produced landslide susceptibility maps. • Black-box models outperform white-box models by about 17 %. • Pairwise effects of factors have greater impacts on landslide susceptibility. • Local and global explanations identified slope as a major contributing factor. Artificial intelligence and machine learning have attracted significant attention in the preparation of landslide susceptibility maps (LSMs) over the years. Achieving considerable success, they frequently face criticism for their opaque nature and the limited capacity to explain and interpret the resulting LSMs. This study uncovers the inherent characteristics of conditioning factors by investigating both local and global driving forces influencing landslide events through the lens of explainable artificial intelligence. To accomplish this, black-box algorithms, including random forest, gradient boosting machines, and extreme gradient boosting, as well as white-box algorithms including logistic regression and decision trees, were employed to generate LSMs. Their internal structures were later illuminated with three global and one local explainable artificial intelligence (XAI) techniques. The results unveiled a significant superiority of black-box algorithms over white-box algorithms, demonstrating an improvement of up to 17 % in overall accuracy and 19 % in the area under the curve (AUC) score. Among them, the gradient boosting machines exhibited the highest performance, achieving an overall accuracy of 87.88 % and an AUC of 0.9382. Global explanation analyses revealed that landslide susceptibility was predominantly influenced by slope, elevation, distance to roads, and lithological units. On the other hand, local interpretations, conducted for three specific landslide cases, disclosed relative variations in the importance of causative factors such as slope and distance to rivers on landslide occurrences. Overall, this study illustrates the potential utility of XAI tools in enhancing the transparency of generated maps and elucidating the underlying causes of specific landslide occurrences. [ABSTRACT FROM AUTHOR]

• Semi-automatic technique has been utilized using VHR imagery to develop landslides inventory. • Most prone transportation infrastructure has been identified using susceptibility analysis. • Temporal recovery of landslides based on the vegetation change has been evaluated. • Rainfall induced landslides has been identified using timeseries data. In mountainous terrains, the frequent landslides and their associated impacts on human lives and the economy is increasing globally. Development of landslide inventory and afterward landslide susceptibility mapping are the main prerequisites for implementing landslide mitigation measures and protection in mountainous regions. The 2005 Kashmir earthquake induced different small and large landslides and some were active for the long term. So far many studies have used medium and high-resolution data to develop landslide inventory. This study aims to develop a 1st detailed, comprehensive and accurate landslide inventory using a very high-resolution image using a semi-automatic technique. The precise landslide inventory is employed to develop an accurate and comprehensive landslide susceptibility map considering the landslide inventory data using a logistic regression training model. Furthermore, the landslide's temporal recovery from the earthquake and its reactivation due to rainfall in spare vegetation areas have been evaluated. Fine-resolution satellite images of Worldview-2 are applied to develop a detailed landslide inventory using a Support Vector Machine (SVM) classifier. A total of 63,630 landslides were identified using a semi-automatic technique within a study area of 265 km2. From regression modeling, the results show that geology, topography, and road networks have a significant impact on the spatial distribution of landslides. Model performance was evaluated based on the testing data, the model gives an AUC of 0.93 and the kappa value of 0.9353. The spatiotemporal NDVI has been assessed to identify the landslide recovery and its reactivation due to extreme rainfall. The results show that 72.1 % of the landslides occurred in Muzaffarabad formation in the study area. The developed landslide susceptibility map can be further used for land-use planning and implementing mitigation measures for the safety of roads and other infrastructure in the area. [ABSTRACT FROM AUTHOR]

The stability analysis of rainfall-induced slope failures considers a number of factors including the characteristics of the rainfall, vegetation, geometry of the slope, unsaturated soil characteristics, infiltration capacity, and saturation degree variations. Amongst all these factors, this study aims to investigate the effects of the antecedent rainfall and saturation conditions. A numerical modeling study was conducted using finite difference code software on a representative slope geometry with two different soil types. Two scenarios were followed: The first involved the application of three different rainfall intensities for varying initial saturation levels between 40% and 60%, representing the antecedent saturation conditions. The second scenario involved modeling successive rainfalls for a typical initial saturation degree of 50%. The impact of antecedent rainfall was assessed by determining the time required for failure during the application of a main extreme rainfall after a preceding rainfall of varying durations. Consequently, a zone of susceptible time for failure was suggested for use as a criterion in hazard management, allowing for the tracking of rainfall and its duration through the proposed chart for potential failures. Once the anticipated critical rainfall intensities have been determined through a meteorological analysis, a risk assessment for a specific slope can be conducted using the proposed practical procedure. Accordingly, a control mechanism may be established to detect the potential for a natural hazard. Furthermore, the proposed procedure was applied to a case study, whose modeling insights were in harmony with the real conditions of the slope failure. Thus, this demonstrated the significance of the antecedent conditions in modeling landslides triggered by rainfalls. [ABSTRACT FROM AUTHOR]

Landslide hazards affect the security of human life and property. Landslide hazard maps are essential for landslide prevention and mitigation. In this study, the success of machine learning and statistical methods in predicting landslide hazards in and around the district center of Maden, Elazığ province, within the borders of Turkey, was analyzed, and their performances were compared. The Random Forest method correctly predicted 1.398 of the 1.425 landslide points in the training dataset, but was incorrect on 27 points. The same method predicted 1942 of the 2075 landslide-free points in the training dataset, but incorrectly predicted 133 points as landslide-exposed. As a result of the study, it is evident that the Random Forest and M5P Rule Tree methods yield more successful results than the Frequency Ratio method. In the study area, the landslide hazard is concentrated in areas close to the East Anatolian Fault and in areas with steep slopes. Lithology, slope, and seismicity have been identified as important triggering factors for landslides in the region. It is expected that machine learning methods, which operate with high levels of accuracy, will make a significant contribution to the prediction of landslide hazards. [ABSTRACT FROM AUTHOR]

In order to reveal the destructive mechanism of loess landslide induced by stagnant water on the combined surface, and to clarify the influence of the main control factors, this paper takes a typical loess landslide in northern Shaanxi as the research object, analyzes the structure of the rock and soil body, and the excavation and filling construction through the geohazard survey, and analyzes the process of traction sliding caused by the stagnant water on the combined surface at the different stages of the project by combining with the calculation of the stability of the slope body. Further the article analyses the process of traction sliding caused by water on the combined area due to construction by means of a discrete element model, and delves into the mechanism of strength reduction of saturated loess. The results show that: 1) the combined surface stagnant water type loess landslide has the characteristics of sudden sliding and rapid evolution, which is highly hazardous and difficult to prevent and control; 2) the slope destabilization is controlled by the engineering geological conditions, and the slope excavation changes the original mechanical equilibrium conditions of the slope, which provides the dynamic conditions for the traction sliding of the slope; 3) the change of the hydrogeological environment results in the obstruction of the natural drainage channel, which leads to the formation of continuous sliding surface due to stagnant water on the combined surface, and the formation of a continuous sliding surface due to stagnant water on the combined surface. Surface stagnant water to form a continuous slippery surface, inducing the overall destabilization of the slope damage; 4) loess strength index with the increase of saturation and the exponential function form of reduction, and when the saturation degree reaches more than 80%, the strength index of the soil body to reach the basic stability. The article expanding the ideas of landslide control and analysis, and the research results will provide a theoretical basis for the design of junction landslide management in the loess areas of northern Shaanxi. [ABSTRACT FROM AUTHOR]

Landslide inventories are crucial for evaluating susceptibility, hazards, and risks, and for devising resilience strategies in mountainous regions. This importance is amplified in the context of climate change, as existing inventories might not adequately reflect changing stability conditions. In May 2023, the Emilia-Romagna region of Italy was hit by two major rainfall events, leading to widespread flooding and the triggering of thousands of landslides. Predominantly, these were shallow debris slides and debris flows, occurring on slopes previously deemed stable based on historical data with no prior landslides recorded. Our team supported the Civil Protection Agency through field surveys and mapping efforts to pinpoint and record these landslides, prioritizing areas critical to immediate public safety and focusing on thorough mapping for future recovery planning. The outcome is a detailed map of all landslides induced by these events, manually identified using high-resolution aerial photography (0.2 m pixel resolution, RGB+NIR four bands) and categorized with the help of a 3D viewer. This comprehensive landslide inventory, comprising 80997 polygons, has been made openly accessible to the scientific community. [ABSTRACT FROM AUTHOR]

Chefchaouen province, located in northern Morocco, is prone to landslides because of its geological features, climate change, and human activities. We investigated landslide dynamics in this mountainous province using a multi-criteria spatial approach and GIS, applying the Analytical Hierarchy Process (AHP). Our findings identified 1267.877 km2 as low-risk, 1695.334 km2 as moderate-risk, and 904.2071 km2 as high-risk, mainly in the northern and northwestern regions. Forests were the most susceptible at 13.83%, followed by agricultural and bare lands, with water bodies least affected. Contributing factors include deforestation, wildfires, and agricultural practices. Our results highlight the need for targeted risk mitigation and continuous land use monitoring. Strategies like slope stabilisation and reforestation can improve landslide prediction and prevention, enhancing community resilience in Chefchaouen. This study’s innovative approach and insights into landslide prediction and prevention provide valuable knowledge to the field. [ABSTRACT FROM AUTHOR]

This article, published in Frontiers in Earth Science, discusses the fault geometry and kinematics at the intersection of the Zemuhe, Daliangshan, and Xiaojiang Faults in China. The study aims to understand the relationships between these fault intersections and earthquake rupture behavior in the region. Through aerial photographs, field surveys, and analysis of seismic data, the researchers identified the geometric patterns and fault kinematics in the intersection area. The findings suggest that the segmented boundary between the Zemuhe Fault and the Xiaojiang Fault may prevent the propagation of large earthquake ruptures, while the lack of geometric complexity between the Daliangshan Fault and Xiaojiang Fault may hinder earthquake rupture propagation. The study emphasizes the need for different earthquake prevention and disaster reduction measures in different cities in the region. [Extracted from the article]

The persistent trend of rising temperatures and shifting weather patterns caused by climate change has prompted significant concern around the world. This research aims to evaluate the instability of slopes in Almaty, Kazakhstan, under various rainfall patterns, groundwater tables, and slope geometries by incorporating the principles of unsaturated soil mechanics. However, there have been a limited number of studies incorporating the principle of unsaturated soil mechanics with constant rainfall patterns in Central Asia, particularly in Kazakhstan, on the impact of rainfall-causing landslides. Hence, in this research, GeoStudio software (SEEP/W and SLOPE/W) was used to simulate the factor of safety (FoS) and pore water pressure for the investigated slopes under different rainfall patterns. Results from Hyprop and statistical method show that the saturated volumetric water content is 0.502, whereas the residual one is 0.147 and for the permeability function the conductivity coefficient started to sharply decrease at the suction value of 2 kPa when the air-entry value was 24 kPa. Findings from numerical analysis show the change in FoS for the slope of 10mheight and 27-degree slope angle was 6%, 7%, 7%, and 8% for cyclic, delayed, advanced, and normal distributions, respectively. For the slope with 20 m height and the same 27-degree angle, the change in FoS was 8%, 10%, 8%, and 11% for the cyclic, delayed, advanced, and normal distributions, respectively. These same patterns were shown in slopes with 35-degree and 45-degree angles, having the same 10 m and 20 m heights. Comparatively, this shows that slopes under cyclic rainfall patterns (240mmof rain within 12 days) are less prone to failure compared to slopes under continuous, delayed, or regularly distributed rainfall patterns. Moreover, an increase in slope height and angle also affect the FoS negatively. It should be noted that the results obtained are only applicable to clayey-loam soil. [ABSTRACT FROM AUTHOR]

ABSTRACT Landslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar Interferometry (InSAR), which can map subtle ground displacement over large areas, has been widely utilized to recognize active landslides. Nevertheless, due to various origins of subtle ground displacement, their presence on slopes may not always reflect the occurrence of active landslides. Therefore, interpretation of exact landslide-correlated deformation from InSAR results can be very challenging, especially in mountainous areas, where natural phenomenon like soil creep, anthropogenic activities and erroneous deformational signals accumulated during InSAR processing can easily lead to misinterpretation. In this paper, a two-phase interpretation method applicable to regional-scale active landslide recognition utilizing InSAR results is presented. The first phase utilizes statistical threshold and clustering analysis to detect unstable regions mapped by InSAR. The second phase introduces landslide susceptibility combined with empirical rainfall threshold, which are considered as causative factors for active landslides triggered by rainfall, to screen unstable regions indicative of active landslides. A case study validated by field survey indicates that the proposed interpretation method, when compared to a baseline model reported in the literature, can achieve better interpretation accuracy and miss rate. [ABSTRACT FROM AUTHOR]

Introduction: The thickness of ground substrate in shallow mountainous areas is a crucial indicator for substrate investigations and a key factor in evaluating substrate quality and function. Reliable data acquisition methods are essential for effective investigation. Methods: This study utilizes six machine learning algorithms -Gradient Boosting Machine (GB), Random Forest (RF), AdaBoost Regressor (AB), Neural Network (NN), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN) -to predict ground substrate thickness. Grid search optimization was employed to finetune model parameters. The models' performances were evaluated using four metrics: mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). The optimal parameter combinations for each model were then used to calculate the spatial distribution of ground substrate thickness in the study area. Results: The results indicate that after parameter optimization, all models showed significant reductions in the MSE, RMSE, and MAE, while R2 values increased substantially. Under optimal parameters, the RF model achieved an MSE of 1,589, RMSE of 39.8, MAE of 26.5, and an R2 of 0.63, with a Pearson correlation coefficient of 0.80, outperforming the other models. Therefore, parameter tuning is a necessary step in using machine learning models to predict ground substrate thickness, and the performance of all six models improved significantly after tuning. Overall, ensemble learning models provided better predictive performance than other machine learning models, with the RF model demonstrating the best accuracy and robustness. Discussion: Moreover, further attention is required on the characteristics of sample data and environmental variables in machine learning-based predictions. [ABSTRACT FROM AUTHOR]

When designing anti-landslide measures, slope stability assessment is an important factor and is determined by conducting comprehensive engineering and geological studies on a slope with potential landslide risks. The results of correct engineering and geological justification for the design of anti-landslide measures lead to the preservation of the structure and safe operation of infrastructure facilities. Our experience shows that to eliminate the negative effects of landslide processes, it is necessary to accurately determine the engineering and geological conditions of the slope, including the stability of the slope, taking into account natural dynamic phenomena (seismicity of the territory and tectonic processes) and, if possible, to see the complete elimination of the causes that lead to landslide processes when developing anti-landslide measures. To achieve these goals, the geotechnical justification of the design is the only correct solution. Geotechnical justification of protective measures intended to protect the structural elements of the Baku-Russian Federation State Border highway, which is part of the north-south transport corridor and passes through the foot of a potentially landslide-hazardous slope west of the city of Shabran, is one of the most important conditions that increase the effectiveness of these measures. On a slope located 1500 meters southwest of the current research area, the results of our scientific research were not properly utilized and many of our warnings were not heeded, resulting in a landslide during construction, leading to serious damage to the road's structural elements (Fig. 1) and additional costs. This event once again confirms that landslides are the most common natural and man-made processes that pose a threat to the safe operation of infrastructure facilities, civil and industrial construction in mountainous and foothill areas (Shiraliyev et al., 2024). The purpose of this study is to determine the geotechnical conditions of the slope that is the object of research, analyze and assess the stability of the slope and provide geotechnical data for geotechnical justification for the design of landslide protection structures. Thanks to the application of the project solutions developed based on the results of the studies presented in the article, the danger of landslides on the mentioned slope has been completely eliminated and the safe operation of infrastructure facilities has been ensured. [ABSTRACT FROM AUTHOR]