Landslide Investigation Research Articles

Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam.

Recurrent landslide events triggered by typhoons and tropical storms over Vietnam pose a longstanding threat to the nation's population and infrastructure. Changes in hydroclimatic conditions, especially the growing intensity and frequency of storms, have elevated landslide susceptibility in many parts of the country. This research examines the spatio-temporal variations in landslide susceptibility across central Vietnam over several years, using multi-temporal landslide inventories from Typhoon Ketsana (2009), Tropical Storm Podul (2013), and Typhoon Molave (2020). Additionally, the research explores the impact of individual landslide causative factors on the probabilistic occurrences of landslides. The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. These changes may be early indicators of increasing landslide susceptibility in response to changing hydro-climatic conditions. The research found that annual average rainfall and topographic elevation are the two most important variables influencing landslide prediction, showing a nonlinear relationship with landslide probability. The landslide susceptibility models achieved high Area Under the Receiver Operating Characteristic Curve (AUC) (>95%), accuracy (>89%), and sensitivity (>90%) scores, signifying the robustness of the models. Additionally, the uncertainty of the models was quantified and spatially mapped. This multi-temporal analysis of landslide susceptibility is crucial for understanding the regional susceptibility trends and identifying areas with increasing, decreasing, and consistently high susceptibility to landslides. These insights are invaluable for prioritizing mitigation and risk reduction strategies in landslide-prone regions and guiding appropriate land use planning.

Investigation of landslide triggers on Mount Oku, Cameroon, using Newmark displacement and cluster analysis

BackgroundThe 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.MethodologyIn 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.ResultsThe 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.

Hazard zonation for potential earthquake-induced landslide in the eastern East Kunlun fault zone

Abstract Based on probabilistic seismic hazard analysis, the seismic landslide hazard research considers the spatial and temporal distribution characteristics of seismic peak ground acceleration, which integrates the factors such as seismic intensity, location, and recurrence time. The occurrence of future earthquakes has certain randomness. This article presents the landslide hazard zoning of the eastern Kunlun fault zone and its surrounding faults, which is carried out under the action of horizontal ground motion with certain exceeding probability. According to the geological structure and seismicity characteristics of the study area, the potential source is divided. Based on the seismic hazard analysis and Newmark cumulative displacement evaluation model, the seismic landslide hazard in the study area is analyzed. The landslide probability is taken as the risk index. The seismic landslide hazard can be divided into five grades: extremely low-prone area, low-prone area, medium-prone area, high-prone area, and extremely high-prone area. In the results of seismic landslide risk zoning given in this article, the surrounding areas of Tazang fault and Minjiang fault are high-risk areas, which should be paid attention to.

Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras

Quantitative disaster risk studies for slow-moving rotational and translational landslides in small regions (e.g., cities and watersheds) are very scarce. The limitations of risk modeling associated with these hazards include (i) the lack of data for physical modeling, (ii) methodological restrictions on estimating landslide intensity with statistical models and determining the temporal probability of landslides, and (iii) the absence of characterizations of the physical vulnerability of exposed assets. The present study combines and updates different methodologies to overcome these limitations for quantitative landslide disaster risk estimation, creating a novel methodological approach that was applied in a pilot study in Tegucigalpa city, Honduras. Tegucigalpa, the capital city of Honduras, has the highest number of recorded landslides in the country. In a previous study, landslides were found to be mainly concentrated in areas with colluvium and residual soils. As an input for the disaster risk assessment, this study generated landslide risk vulnerability functions based on empirical data. The application of the proposed methodology allowed us to estimate the average annual loss (AAL) caused by landslides in the study area—a key disaster risk metric that is lacking in other landslide disaster risk studies—enabling comparisons with disaster risk estimates associated with other hazards. In particular, the AAL value obtained for the study region was USD 7.26 million.

A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide

A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide

Synthetic libraries of urban landslide simulations to identify slope failure hotspots and drivers across spatial scales and landscapes

AbstractRainfall-triggered landslides are most deadly in developing countries, and future urban sprawl and climate change could intensify existing risks. In these regions, enhancing efforts in urban landslide risk mitigation and climate change adaptation is crucial. Current landslide probability assessment methodologies struggle to support effective mitigation because they fail to represent local anthropogenic factors (e.g. informal housing) across space and time scales. To meet this challenge, we demonstrated in previous work that hillslope-scale mechanistic models representing such localised changes can be used to create synthetic libraries of urban landslides that account for both data and future scenario uncertainty. Here, we show how these libraries can become an explorative tool for researchers and stakeholders, allowing them to investigate slope stability variations across spatial scales and landscapes. Results highlight, for example, how the main slope instability drivers change according to the location (e.g., upper vs lower catchment), the landcover (e.g. forest vs urban) and the spatial scale analysed (e.g. at hillslope scale slope stability was mostly controlled by water table height, whereas at regional scale by slope geometry). Ultimately, we demonstrate that stochastic analyses can lead to a greater understanding of the system interactions and they can support the identification of mitigation strategies that perform well across spatial scales and uncertain scenarios. These strategies should be prioritised even if future conditions are unknown. This reasoning is shown on a data-scarce region with expanding informal housing. However, the same methodology can be applied to any urban context and with any mechanistic-based model.

Prediction method of loess landslides based on faster R-CNN and WACM

Due to the complexity of the environment and geological conditions in which the loess slope is located, there are many challenges in the accuracy and prediction of loess landslide detection. Therefore, this study introduces a fast convolutional neural network model to solve the problems of traditional detection methods in terms of technology, cost, and detection accuracy, and to achieve real-time detection of the morphology of loess landslides. A weight absorption coupling model is constructed to address the uniform moisture content in loess with hidden dangers. Combined with instability probability, the probability of shallow loess landslides is predicted. The results showed that the mAP value of the Faster R-CNN algorithm using the ResNet125 network exceeded 90%, which was 46.23% and 32.01% higher than the algorithm models using ResNet50 and VGG16, respectively. The proposed model performed fractal analysis on four different loess particle samples, with correlation coefficients R2 above 0.9. The difference between the predicted and actual moisture content of upper and surface loess was within 11%. Compared with existing methods, the research and construction of a loess landslide detection and probability prediction model has greatly improved reliability and accuracy, which is of great significance for predicting the probability of different loess landslides.

InSAR-Driven Dynamic Landslide Hazard Mapping in Highly Vegetated Area

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management.

Quasi-3D Geoelectrical Imaging as A New Application for Landslide Investigations: A Tunnel Case Induced by Blasting Activity

Landslides are a significant hazard in mountainous regions, especially when influenced by construction activities such as tunnel excavation. In this paper, we aim to conduct a slope stability analysis as a result of tunnel blasts using quasi-3D subsurface models based on resistivity values. The study site is a construction area for the Jakarta-Bandung High-Speed Train tunnel, located in a mountainous region undergoing drill-and-blast excavation. This excavation method makes the area susceptible to landslides, which pose a threat to settlements in the Padalarang subdistrict, West Bandung Regency, Indonesia. Data was collected along four lines in 2D, and the dipole-dipole array was used to enhance resolution. Data modeling was carried out using ResIPy v3.2.3 software to create 2D and quasi-3D subsurface models based on resistivity values. The study findings indicate that the study area exhibits three resistivity ranges: low resistivity (0-30 Ωm), medium resistivity (31-49 Ωm), and high resistivity (>50 Ωm). Utilizing quasi-3D imaging, we were able to identify the dimensions and presence of slip surfaces, which can be categorized as shallow (1.5-5 m) and deep (5-20 m) criteria. This study successfully applied the quasi-3D geoelectrical approach in a susceptible environment to detect potential landslide zones.

Investigation and monitoring of rotational landslides in El Mokkattam plateau Egypt, using integrated geological and geophysical techniques

Landslides are a rare but hazardous geological phenomenon in Egypt, with the El Mokattam plateau situated in the eastern part of Cairo covering approximately 64 km2 and ranging in elevation from 50 to 205 m. This study aims to identify and monitor landslides in the area using various geophysical methods. Twelve Electrical resistivity tomography (ERT) profiles,twenty-two P-wave Seismic Refraction profiles, twenty-two Refraction microtremors profiles, three ground penetrating radar (GPR) profiles and borehole data were utilized to analyze the occurrence of landslides in the El Mokattam Plateau. Additionally, we employed a relatively new geophysical method, studying high-frequency microtremor sounds emitted from landslide collapses at 22 stations. Our analysis identified steep slopes, jointed or fractured rocks, and irrigation water as primary factors contributing to landslides, with irrigation water acting as a lubricant for clays and promoting ground sliding. Examination of high-frequency microtremor sounds revealed a potential correlation between vertical high-frequency spectra at 100 Hz and landslide collapses, which aids in the identification of landslide-prone zones. Therefore, we conclude that seismological studies, particularly spectral analysis of high-frequency and low-amplitude sounds (microtremors) emitted from soil, offer a promising approach for investigating landslides.

Geotechnical Evaluation of Landslide in Nanggerang Village

Landslides are significant geological events that can cause extensive damage to infrastructure, disrupt communities, and pose serious safety hazards. Understanding the mechanisms behind slope failures is crucial for effective risk mitigation and the development of engineering solutions to improve slope stability. According to data from the National Disaster Management Agency (BNPB), Indonesia experienced 83 landslide events from January to February 2024. A notable landslide occurred in Nanggerang Village, Sukasari Sub-district, Sumedang Regency, West Java Province, on February 3, 2024. This landslide happened in a terraced rice field area following heavy rainfall earlier in the day. This study focuses on evaluating the failed slope to understand its condition just before failure and the material properties that influence the landslide event. The research methodology includes field data collection, soil testing, and slope stability analysis using the Limit Equilibrium Method (LEM) with a probabilistic approach via Slide 2 software. The analysis revealed that the failed slope had an average safety factor (FS) of 0.968 and a landslide probability of 58.897%. Sensitivity analysis showed that the cohesion parameter in the soil layer (CWZ) significantly impacts the safety factor of the slope. The study concludes that the reduction in soil cohesion and internal friction angle due to excessive moisture was the primary cause of the landslide, and the cohesion parameter of the soil layer is the most sensitive factor affecting slope stability.

Landslide susceptibility mapping using modified frequancy ratio method in Correb area, South Wollo, North-Western Ethiopia

Correb area in Mekdela Woreda, South-Wollo Zone, North-Western Ethiopia is one of the most landslide affected region. The primary aim of this study was to assess the landslide susceptibility of the area and create a landslide susceptibility map. A bivariate statistical frequency ratio model was used to accomplish this goal. Using fieldwork and Google Earth picture interpretation, a map of landslides was made. A total of 524 landslides were identified and then classified in to training landslide (75%) for model development and validation landslide (25%) for model validation. Eight landslide causative factors slope, aspect, elevation, distance to drainage, distance to spring, slope material, distance to lineament, and vegetation cover were integrated with training landslides in order to determine frequency ratio value(weight) for each class of landslide causative factors. Furthermore, each causative factor’s prediction rate or weight was established. Relative frequency values were allocated to the appropriate factor classes and prediction rate values were allocated to the appropriate causative factors and summed using a raster calculator algorithm in order to produce the landslide susceptibility map. The final landslide susceptibility map revealed that 27.54% (31.52 km2) of the study area falls under very low susceptible, 30.35% (34.74 km2) low susceptible, 21.36% (24.44 km2) moderate susceptible, 14.74% (16.88 km2) high susceptible, and the rest 6.01% (6.88 km2) as very high susceptible. This shows that more than 20% of the area has probability and risk of landslide while about 80% has relatively low probability and risk of this natural hazard. The performance of modified frequency ratio model for landslide susceptibility mapping was validated using simply overlay, relative landslide density index, and receiver operating characteristics curve. Both training and validation landslides were rare in the very low and low susceptibility classes and strongly concentrated in the high and very high susceptibility classes, according to validation using simple overlay and the relative landslide density index. The implemented model performed exceptionally well, as seen by the receiver operating characteristics curve, which showed 82.6% success rate and an 83.1% prediction rate. In general, the model produced reasonable accuracy. The resultant map would be useful for general land use planning, site selection, and landslide prevention and mitigation programs.

Optimized Landslide Susceptibility Mapping and Modelling Using the SBAS-InSAR Coupling Model

Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely associated with geo-environmental conditions. However, landslide hazards are often characterized by significant surface deformation. The Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology plays a pivotal role in detecting and characterizing surface deformation. This work endeavors to assess the accuracy of SBAS-InSAR coupled with ensemble learning for LSM. Within this research, the study area was Shiyan City, and 12 static evaluation factors were selected as input variables for the ensemble learning models to compute landslide susceptibility. The Random Forest (RF) model demonstrates superior accuracy compared to other ensemble learning models, including eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting Decision Tree, and K-Nearest Neighbor. Furthermore, SBAS-InSAR was utilized to obtain surface deformation rates both in the vertical direction and along the line of sight of the satellite. The former is used as a dynamic characteristic factor, while the latter is combined with the evaluation results of the RF model to create a landslide susceptibility optimization matrix. Comparing the precision of two methods for refining LSM results, it was found that the method integrating static and dynamic factors produced a more rational and accurate landslide susceptibility map.

Constraining Mean Landslide Occurrence Rates for Non‐Temporal Landslide Inventories Using High‐Resolution Elevation Data

AbstractConstraining landslide occurrence rates can help to generate landslide hazard models that predict the spatial and temporal occurrence of landslides. However, most landslide inventories do not include any temporal data due to the difficulties of dating landslide deposits. Here we introduce a method for estimating the mean landslide occurrence rate of deep‐seated rotational and translational slides derived solely from high‐resolution (≤3 m) elevation data and globally available estimates of the diffusion coefficient for sediment flux. The method applies a linear diffusion model to the roughest landslide deposits until they reach a representative non‐landslide roughness distribution. This estimates the time for a landslide deposit to be unrecognizable in high‐resolution digital elevation data, which we term the mean lifetime of the landslide. Using the mean lifetime and number of landslides within an area of interest, we can estimate the mean occurrence rate of landslides over that domain. We validate this approach using a comprehensive temporal inventory of landslides in western Oregon created using age‐roughness curves that are calibrated with high‐resolution elevation data and radiocarbon data. We find good agreement between our diffusion method and the existing age‐roughness‐derived estimates, producing mean lifetimes of 4500 and 5200 years (4% difference), respectively. Hazard maps produced using the two methodologies generally agree, with the maximum differences in landslide probability reaching 0.1. Due to the relative abundance of high‐resolution elevation data compared with age‐dated landslides, our method could help constrain landslide occurrence rates in areas previously considered unfeasible.

A Unique Conditions Model for Landslide Susceptibility Mapping

Several methods and approaches have been proposed to assess landslide susceptibility. The likelihood of landslides occurring can be determined by applying statistical models to historical landslides, taking into account controlling factors. Popular methods for predicting the probability of landslides are weights-of-evidence and logistic regression. We discuss the assumptions and interpretations of these methods, the relationships between them, and their strengths and weaknesses in case of categorical factors. Of particular interest is the conditional independence of the controlling factors and its effect on model bias. To avoid lack of conditional independence of factors and model bias, we present a unique conditions model that is always unbiased. To illustrate the theoretical developments, a practical application is given using observed landslides and geo-environmental factors from a previous study. The unique conditions model appears superior to the other models.

Computational modeling of near-fault earthquake-induced landslides considering stochastic ground motions and spatially varying soil

Landslides represent a large-deformation process influenced by various factors of uncertainty, such as types of ground motion (GM), randomness of GMs, and spatial variability of soils. The behavior of landslides is profoundly affected, making their assessment and measurement challenging. This paper proposes a computational approach for simulating the large-deformation process of landslides based on three-dimensional (3D) non-ordinary state-based peridynamics (NOSBPD). The analysis results of NOSBPD indicate that the mean runout distance triggered by pulse-like ground motions (PLGMs) is 16% greater than that induced by non-pulse ground motions (NPGMs), suggesting that PLGMs exhibit higher destructiveness. Besides, this paper establishes a runout distance assessment framework by considering the stochastic nature of PLGMs. This framework allows for the evaluation of the specific risk probability of landslides caused by stochastic PLGMs that match the target spectrum specified by codes. By introducing the theory of random fields and implementing a coupled procedure in peridynamics, we conducted a detailed analysis of the impact of spatial heterogeneity on the evolution process and consequences of landslides. Additionally, compared to two-dimensional (2D) analysis, the mean runout distance obtained from 3D analysis increased by 27.5%. This suggests that 2D analysis may underestimate the consequences of landslides. The findings of this study can serve as a scientific foundation for predicting the extent and scope of landslides triggered by near-fault earthquakes.

GIS-Based Integrated Multi-Hazard Vulnerability Assessment in Makedonska Kamenica Municipality, North Macedonia

This study presents a comprehensive analysis of natural hazard susceptibility in the Makedonska Kamenica municipality of North Macedonia, encompassing erosion assessment, landslides, flash floods, and forest fire vulnerability. Employing advanced GIS and remote sensing (RS) methodologies, hazard models were meticulously developed and integrated to discern areas facing concurrent vulnerabilities. Findings unveil substantial vulnerabilities prevalent across the area, notably along steep terrain gradients, river valleys, and deforested landscapes. Erosion assessment reveals elevated rates, with a mean erosion coefficient (Z) of 0.61 and an annual erosion production of 182,712.9 m3, equivalent to a specific erosion rate of 961.6 m3/km2/year. Landslide susceptibility analysis identifies 31.8% of the municipality exhibiting a very high probability of landslides, while flash flood susceptibility models depict 3.3% of the area prone to very high flash flood potential. Forest fire susceptibility mapping emphasizes slightly less than one-third of the municipality’s forested area is highly or very highly susceptible to fires. Integration of these hazard models elucidates multi-hazard zones, revealing that 11.0% of the municipality’s territory faces concurrent vulnerabilities from excessive erosion, landslides, flash floods, and forest fires. These zones are predominantly located in upstream areas, valleys of river tributaries, and the estuary region. The identification of multi-hazard zones underscores the critical need for targeted preventive measures and robust land management strategies to mitigate potential disasters and safeguard both human infrastructure and natural ecosystems. Recommendations include the implementation of enhanced monitoring systems, validation methodologies, and community engagement initiatives to bolster hazard preparedness and response capabilities effectively.

Centrifuge modeling of loess slope failure induced by rising water level utilizing intact sample

Understanding the failure process of bottom-saturated loess slopes is of great significance in loess areas where flood irrigation is commonly utilized to alleviate the scarcity of precipitation. In this study, the failure process of a loess slope with an increased groundwater level was recreated and the multiple failure modes of loess landslides were revealed. A centrifugal model test was conducted using a groundwater-recharge device. An intact loess sample retrieved from the Heifangtai Loess Terrace was employed in the test. The model test was monitored using a video recorder, high-speed camera, and soil/pore water pressure sensors, and the results were validated by utilizing an intermittently investigated field landslide. Based on the monitored data and acceleration, the test was divided into three periods: the initial acceleration period with no water inlet (0–40 g), bottom saturation period (40–60 g), and failure occurrence period (60–80 g). The soil/pore water pressure and degree of deformation were relatively low, with a steadily increasing trend during the first two periods. With the enrichment of the soil water content, retrogressive sliding, deep subsidence, and surface sinkhole failures occurred successively up to areas with relatively high pore water pressure during the last period. The results of the field landslide investigation showed multiple failure modes, as observed in the model test. The results suggest that the coexistence of multiple failure modes could gradually evolve into croplands and promote water infiltration into the deep loess, increasing the groundwater level and accelerating the failure process of the slope. Despite the overall effects of multiple failure patterns on the evolution of the slope, each failure pattern had a relatively independent evolutionary process within a certain area, which could be further analyzed for the early recognition of loess landslides. This study also indicates that the challenges of centrifuge modeling for water-related materials with intact soil samples are the boundary conditions and data monitoring within the model.

Comprehensive investigation of a dangerous counter-tilt landslide in the upper reaches of Jinsha River, China

Comprehensive investigation of a dangerous counter-tilt landslide in the upper reaches of Jinsha River, China

A review of submarine landslides associated with rapid sedimentation

Submarine landslides are a critical geological process that significantly influences seafloor stability and geological evolution, making them a focal point of geological disaster research. Particularly, rapid sedimentation as a crucial preconditioning factor for submarine landslide initiation is increasingly recognized in contemporary research. However, no comprehensive review currently examines the correlation between rapid sedimentation and submarine landslides. To elucidate the interaction mechanisms between submarine landslides and rapid sedimentation and to provide a new theoretical foundation for the prediction and prevention of geological hazards, the study categorizes three primary sedimentation patterns in marine environments and establishes a detailed historical database of sedimentation-related submarine landslide events. Moreover, the paper systematically reviews and critiques the existing research methods and advancements in addressing rapid sedimentation-related submarine landslides, and a bibliometric analysis of recent research progress is conducted. Finally, the study highlights several challenges in the study of rapid sedimentation and proposes future recommendations, aiming to provide robust support for the in-depth investigation of submarine landslides.