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Landslide susceptibility assessment (LSA) is a significant part of landslide research, which plays an important role in preventing landslide disasters. It has gained an increasing attention in both the academic and practice fields for the past two decades. However, there have been few bibliometric analyses on this topic, although bibliometric analysis can inspire future researchers by exploring the overall characteristics of the published literature. This article aims at collecting and analyzing the information of the abstracts, authors, institutions, countries, journals, funds, and keywords of the recent 4,732 papers published from 1999 to 2021 in Web of Science (www.webofscience.com). In particular, latent Dirichlet allocation (LDA), a machine learning and text analysis method, is utilized to analyze the abstract of each article to identify the hottest research topics related to LSA. The results revealed that: (1) The amount of annual publications related to LSA generally shows an increasing trend, which accounts for about 22% of the total landslide publications in 2021; (2) The author of Pradhan B, the institution of the Chinese Academy of Sciences, the country of China, the journal of Natural Hazards and the funding agency of the National Natural Science Foundation of China, are the productive performers in each aspect of LSA; and (3) Machine learning methods have gained a rapid increase in LSA in recent five years, which have become the most popular research topic. [ABSTRACT FROM AUTHOR]

China is a country with frequent geological disasters. The geological disasters in different regions are different, such as earthquakes, landslides, collapses, karst collapses, and ground subsidence. With the development of science and technology and the improvement of engineering technology, the combination of image technology and digital technology is constantly developing. People can improve the accuracy of prediction by studying the characteristics of rock formation under different geological conditions while collecting geological data and sorting various information. And reliability, can more accurately identify poor geological conditions, can increase construction safety and reduce economic losses. Based on the comprehensive CT geological survey data, this paper uses nanomaterials to construct software DEM to model the porous medium of the Yanziyan dangerous rock mass in Jinfo Mountain, Nanchuan District, Chongqing and to estimate the area and volume of the collapse potential area of the high-steep dangerous rock mass parameter. [ABSTRACT FROM AUTHOR]

Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90. [ABSTRACT FROM AUTHOR]

There hides a certain relationship among various monitoring data in a landslide, and the mining of this relationship is of significance to landslide research. In this paper, we first collect multiple monitoring data of riverside 1# slump-mass of Huangtupo landslide, the Three Gorges Reservoir Region, China, including Global Positioning System (GPS) monitoring data, inclinometer data, reservoir water level, rainfall, water content, crack width, groundwater level and temperature data, etc. By adopting the combination of quantitative statistics and qualitative simulation method for multi-sensor fusion monitoring data analysis, we overcome the one-sidedness of using a single method or single data type. The result of fusion analysis has indicated that in time periods with low rainfall or when the rainfall is not the major factor, main factors affecting landslide movement are crack development, water content of the landslide and water level of the Three Gorges Reservoir. Compared with the actual monitoring data, the fusion analysis results has a maximum error of 1.9%, which shows a good effect. [ABSTRACT FROM AUTHOR]

Risk assessment and management of an individual landslide is the focus and difficulty. This paper analyzes the landslide losses in China from 1995 to 2018, combined with the ALARP approach, to research the risk acceptable model of landslide in China. Meanwhile, the Shiyantan landslide in Mayang county of Hunan Province is exemplified to quantitatively calculate the landslide risks under different conditions, and based on the risk acceptance level to judge whether the risk value is acceptable. The results show that the individual life risk acceptable criteria for Chinese landslide is 5.36 × 10−6/a, the upper and lower limits of the societal life risk (F-N curve) are 10−7/a and 10−8/a, the societal economic risk (F-D curve) are 10−1/a and 10−3/a. In addition, for the risk assessment of Shiyantan landslide, it is shown that the landslide may be damaged under extreme rainfall conditions. Its economic risk is tolerable risk and life risk is unacceptable risk. It is urgent to formulate relevant measures to mitigate the risk. The results can provide a reference for risk assessment and management conducted in other regions and offer a theoretical basis for early warning and forecast of landslide. [ABSTRACT FROM AUTHOR]

The critical stage in the evaluation of rainfall-induced landslide failure is in formulating reasonable models to better simulate spatiotemporal changes of slopes in the hilly terrains. A physically based model can take into account the contribution of rainfall infiltration and shear strength of saturated soil layer, and therefore help revealing the landslide formation mechanisms. This paper presents a physically based approach to simulate the landslide process triggered by rainstorm. On the basis of previous solutions, we select the simplified infiltration model Slope-Infiltration-Distributed Equilibrium (SLIDE) to illustrate the dynamical relations between factor of safety (FS) and accumulation of rainfall over time. This model is tested with three representative landslide events in the southwest, southeast, and south central of China during rainstorm. Results show that the time of landslide failure predicted from the SLIDE model is consistent with the reality. Meanwhile, this paper illustrates the differences of FS among the different slope gradients in the vicinity of same soil texture and relationship between FS and rainfall accumulation. This work formulates a methodology of rainstorm-induced landslide evaluation and improves upon the existing landslide prediction methods. [ABSTRACT FROM AUTHOR]

The standards applied to reclassify landslide-conditioning factors differ among studies and may change the accuracy of identifying landslide-prone areas. Therefore, we identified two standards per factor (elevation, aspect, slope, proximity to roads and proximity to streams) from the existing literature and set them as predisposing criteria in this paper. In addition to the five factors, lithology represented by types and a landslide inventory map produced from field surveys were also used in mapping. Thirty-two landslide susceptibility maps were generated based on weights-of-evidence and evaluated using the relative operative characteristic method. The results show that the subdivision criteria of factors change the accuracy, with the success rate varying from 84.34% to 87.51%. The map with the highest value captures more landslides in relatively higher susceptibility classes and is therefore considered the optimal one. Ultimately, a simplified mode of combining subdivision criteria is proposed to simplify comparison. [ABSTRACT FROM AUTHOR]

The assessment of landslide hazards is crucial for disaster prevention and mitigation, but it has not considered the dynamic influencing factors that trigger landslides. The timeliness and practical value of the assessment results still need to be further improved. This study constructed a dynamic landslide hazard assessment system using information value model, dynamic precipitation data, and Backpropagation Neural Network (BPNN) model. Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. It is predicted that the percentage of very high and high areas in 2025 and 2030 will be 50.5% and 57.5% respectively. [ABSTRACT FROM AUTHOR]

The periodic storage and release of water in reservoirs have the potential to accelerate deformation or trigger ancient landslides. Interferometric synthetic aperture radar (InSAR) technology, renowned for its wide coverage and high precision, has emerged as an effective technique for the identification and monitoring of landslides. This study utilized spaceborne InSAR technology to identify and interpret landslides in the Liujiangxia reservoir area in China, leading to the detection of a total of 20 landslides. Moreover, among these 20 landslides, the landslide with substantial deformation (LJX02) was chosen, and the wavelet quantitative analysis technique was employed to investigate the correlation and time lag between various elevation positions of this landslide, the reservoir water level, and rainfall. The study successfully demonstrated that the deformation of the landslide lagged behind the reservoir water level by 152.10-182.50 days (from low to high elevation) and was delayed by 45.60-68.44 days compared to rainfall (from high to low elevation). [ABSTRACT FROM AUTHOR]

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area's disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation's findings mostly agreed with the survey's actual findings. The study's findings demonstrated that: (1) the FCNN model's AUC value was 0.910, while the I-AHP model's AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened. [ABSTRACT FROM AUTHOR]

Landslide is a frequent geological hazard in the Three Gorges reservoir area of China. Impulse waves could be created and cause substantial damage if the landslide collides with a water body. A nonlinear shallow water equation in combination with a progressive landslide model is proposed to simulate this catastrophic disaster. The proposed approach is distinguished by rapid calculation, less computational burden, and high practical value. By simulating the 2008 Three Gorges Reservoir Gongjiafang landslide and the subsequent impulse waves, we illustrated and validated this methodology. The initiation and motion processes of the landslide, as well as the generation, propagation, and run-up processes of impulse waves, were thoroughly performed. The simulation results were in good agreement with the field survey data, demonstrating the viability of the proposed approach for simulating the impulse waves triggered by landslide events. This numerical approach is beneficial for engineers to perform a preliminary estimation of landslide-generated waves and further facilitate the prevention and mitigation of geological hazards. [ABSTRACT FROM AUTHOR]

Two rock topples with the opposite dip direction, observed in the Ribangliangzi hill and facing the China Provincial Highway S211, are located on the upper stream of the planned dam of Jinchuan Hydropower Station in China. The new site for submerged part of Highway due to reservoir impoundment is designed to be a tunnel crossing the Ribangliangzi hill. However, at this hill, the stratum facing the Dadu River dips southwestward, whereas the stratum facing the Xinzha Gully dips northeastward. Therefore, it's regarded as an important slope structure that may lead to an occurrence of landslide directly threatening the future tunnel excavation stability and slope stability along the highway. This paper reports for the geologic structures of the overturned strata, formative processes and mechanism for toppling on cataclinal slopes. It was concluded that the toppling on cataclinal slopes was caused by a combination of toppling and sliding. Furthermore, to analyse the conditions for toppling on cataclinal slope, the cantilever beam model was established and analyzed the minimum strata dip angle required for toppling more prone to occur on cataclinal slope and the critical length of rock stratum. The results reveal that the toppling will be more prone to occur on cataclinal slope while the strata dip angle is greater than 60°even if under a very small external force or without the assistance of external forces. [ABSTRACT FROM AUTHOR]

Lateral berms are often constructed to protect highway bridges against debris flows in mountainous regions. Currently, different solutions for lateral berm design are debated. The lack of standardization results in the improper design of lateral berms, limiting the mitigating effect. In this paper, a monitoring case of the mitigating effect of a lateral berm is introduced. The lateral berm was constructed through a bridge culvert at the alluvial fan of a debris-flow gully. In September 2008, a debris flow in this gully completely buried the lateral berm. The proposed numerical integral method was used to back analyse the flowing velocity and mud depth in berm. Results supported the speculation that abrupt decreases in mud depth and flow velocity in the lateral berm caused deposits that compressed the effective berm depth and resulted in overtopping flow. Therefore, we suggested reducing the berm width in order to increase the flow velocity in the berm. In June 2011, another debris flow with a smaller magnitude occurred in the gully, and the reconstructed lateral berm reportedly performed well to protect the bridge of Yalu highway. The case studies highlighted that berm width should be one of the main considerations in the berm design. [ABSTRACT FROM AUTHOR]

Loess landslide is one of the most harmful and serious geological hazards in the Loess Plateau of China. Early identification of potential loess landslide is an urgent need for its prevention. Traditional methods, e.g. support vector machines and decision trees, often suffer complicated data pre-processing, multitudinous causative factors, or low accuracy. This study aims to develop a high-performance loess landslide early identification model based on convolutional neural networks (CNNs). A case study was carried out in northwest Lvliang, China, where loess landslide is a major concern. Two hundred and six loess landslide cases were interpreted by comparing remote sensing images of two time phases, and were randomly divided into a training set (80%; 165) and a validation set (20%; 41). Four algorithms were developed, including a CNN structure with skip connection using data with (S–C) or without (S–N) slope crest and plain CNN structure using data with (P–C) or without (P–N) slope crest. The results show that the S–C structure is the most suitable for early identification of potential loess landslides because it achieved the highest overall accuracy (OA = 0.902) and largest area under the receiver operating characteristic curve (AUC = 0.932) on the validation set. [ABSTRACT FROM AUTHOR]

The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. [ABSTRACT FROM AUTHOR]

Landslides in high-order position areas pose a serious threat to residents located below such areas. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides that have already occurred; however, the investigation of potential landslides is of higher value. In this study, a progressive potential landslide identification method is proposed with the application of SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) and subsequently combined with Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3) and UAV (Unmanned Aerial Vehicle) imagery, and DEM (Digital Elevation Model) for further validation. The proposed method is employed to determine the potential landslide in Tuanjiecun by integrating the multi-period data to simultaneously analyze the evolution and mechanism of the potential landslide. The long short-term memory method is then adopted to predict the evolution trend based on accumulative deformation from SBAS-InSAR. The results suggest that the deformation of Tuanjiecun potential landslide will increase to −238.57 mm. This article provides a new approach for disaster prevention and mitigation by determining a potential landslide using composite remote sensing and predicting the development of the potential landslide on indirect contact monitoring. [ABSTRACT FROM AUTHOR]

The past landslides from landslide inventory are essential for machine learning (ML)-based landslide susceptibility assessment (LSA). They determine not only the positive samples (characterized by the past landslides) but also the negative samples (randomly generated based on past landslides) for training and validating the ML models. However, the number of past landslides is often limited because of the constraints of time, budget, and resources available, etc. In other words, the available data for establishing landslide susceptibility ML models are limited, which indicates that the accuracy and reliability of the corresponding models are insufficient. This article, therefore, proposes using a new landslide susceptibility index-based sampling strategy to enhance the positive and negative samples for model training and validation to reach an improved ML-based LSA. To realize this idea, landslide susceptibility analysis based on initial datasets compiled from landslide inventory by using three ML models, i.e., random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM), are first conducted to obtain the initial landslide susceptibility indices at different space locations. Then, the landslide susceptibility indices are analyzed with the proposed sampling strategy which considers directly the grid units with very high and very low landslide susceptibility indices as potential positive and negative samples, respectively, to enrich the initial dataset; and the number of these positive/negative samples is determined by a Bayesian optimization algorithm. Thereafter, the ML models are updated with the enriched datasets. Finally, to verify the effectiveness of the proposed strategy, the improved models are applied to assess the landslide susceptibility of Taojiang County, China, and the results are compared with those from initial corresponding models without updating. The results show that compared with the initial RF, GBDT and SVM models, the corresponding improved models have a better performance in accuracy, precision, recall rate, and specificity. In particular, the AUC values of the three models are increased by 11.94%, 10.72% and 0.57%, respectively. [ABSTRACT FROM AUTHOR]

Quantifying the uncertainties in the prediction of landslide displacement is important for making reliable predictions and for managing landslide risk. This study develops a novel approach for the interval prediction (i.e. uncertainty) of landslide with step-like displacement pattern in the Three Gorges Reservoir (TGR) area using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTEENN) based Random Forest (RF) and bootstrap-Multilayer Perceptron (MLPs). DBSCAN was employed to carry out clustering analysis for different deformation states of the landslide with step-like displacement pattern. The SMOTEENN based RF classifier was trained to deal with imbalanced classification problems. A dynamic switching prediction scheme to construct high-quality Prediction Intervals (PIs) using bootstrap-MLPs was established. The concepts of Pareto front and Knee point were adopted to select the PIs that could provide the best compromise between reliability and accuracy. The proposed DBSCAN-RF-bootstrap-MLP method is illustrated and verified with one typical landslide with step-like displacement pattern, the Bazimen landslide from the TGR area in China. The method showed to perform well and provides the uncertainties associated with landslide displacement prediction for decision making. [ABSTRACT FROM AUTHOR]

Rainfall represents one of the most significant cause of landslide hazards. With runoff in the saturated zone of shallow soil considered, an improved Green–Ampt model was proposed to depict the rainfall infiltration process. Furthermore, a formula for calculating the depth of wetting front was derived, and the slope stability was evaluated accordingly. Finally, a formula for calculating the safety factor of multilayer slopes above different surfaces was obtained via the limit equilibrium method. An illustration is a multilayer colluvial slope in Guangdong Province of China, the results indicated that the downward speed of wetting front decreases gradually within the same layer with continuous rainfall but increases significantly when it extends to the interfaces between the soil layers. Safety factor of the slope decreases sharply at the initial stage of rainfall but then more slowly. Slope failure is more likely to occur along the first interface between the soil layers than along the surface of wetting front. The result from the proposed model is closer to the result of the physical model test than from the existed Green–Ampt model. The improved Green–Ampt model for multilayer slopes can accurately evaluate the infiltration process and related stability of multilayer slopes under rainfall. [ABSTRACT FROM AUTHOR]

China has a vast land area with complex topography and landforms, of which the mountain area accounts for more than 60% of the total area. China is located between the Pacific Rim seismic zone and the Mediterranean-Himalayan earthquake zone, causing frequent earthquake disasters. Earthquake-linked secondary disasters cause far greater damage than themselves, and often cause geological disasters such as landslides and slope collapses in mountainous areas. The secondary disaster caused by the earthquake is closely related to the dynamic response of the slope and the dynamic characteristics of the slope. Geological structure of different slopes and the conditions of ground motion loading have different influences on the pressure and stability of the steep layer between soft and hard phases. Therefore, the purpose of this article is to explore the main modes and processes of slope instability induced by strong earthquakes based on the dynamic response characteristics and laws of slopes, to explore the model deformation and failure phenomena during the test, and to analyze the different results of different lithological slope bodies. This article will use the research methods of specific problems and specific analysis to compare the data and draw conclusions. The research results show that under the common slope body, the acceleration of the falling object is different with different angle and height. Therefore, combined with the investigation of the deformation and failure of the slope in the earthquake zone, the dynamic response law of the rock slope is obtained, which provides a reasonable explanation for the collapse of many large landslides during the earthquake, and provides a theoretical basis for the future design of seismic structures and field slope monitoring and protection against disaster. Combined with physical simulation and numerical simulation methods, the instability and damage characteristics and mechanism of layered slopes under strong earthquake conditions are analyzed and demonstrated. [ABSTRACT FROM AUTHOR]

This study introduces four heterogeneous ensemble-learning techniques, that is, stacking, blending, simple averaging, and weighted averaging, to predict landslide susceptibility in Yanshan County, China. These techniques combine several state-of-the-art classifiers of convolutional neural network, recurrent neural network, support vector machine, and logistic regression in specific ways to produce reliable results and avoid problems with the model selection. The study consists of three main steps. The first step establishes a spatial database consisting of 16 landslide conditioning factors and 380 historical landslide locations. The second step randomly selects training (70% of the total) and test (30%) datasets out of grid cells corresponding to landslide and non-slide locations in the study area. The final step constructs the proposed heterogeneous ensemble-learning methods for landslide susceptibility mapping. The proposed ensemble-learning methods show higher prediction accuracy than the individual classifiers mentioned above based on statistical measures. The blending ensemble-learning method achieves the highest overall accuracy of 80.70% compared to the other ensemble-learning methods. [ABSTRACT FROM AUTHOR]

The Chinese Loess Plateau (CLP) is still undergoing development. Nearly one-third of the geological disasters take place in this region in China each year. Prior to assessing the spatial characteristics of the rainfall-induced landslides in the CLP, it is fundamental to establish a detailed landslide inventory map. This study utilizes GLDAS (Global Land Data Assimilations Systems) rainfall distribution data in the CLP based on yearly, monthly, and the daily precipitation data from 57 rain gauge stations. Geostatistics analysis and Geographic Information System technique combined with information on geological disasters within the region were used to study the spatial characteristics and trend variations of rainfall and geological disaster distribution in the region. The results indicate that a positive relationship exists between geological disaster and the average annual rainfall, correlation coefficient reaches 0.82, with zonality disasters (account for more than 90%) in the loess gullies, hills, and ridges. Seven zones of loess landside distribution were identified based on their landside densities. The spatial distribution of the geological disasters is characterized by a "linear and zone" shape in the CLP. These results provide a useful reference for the study of the mechanism of water-induced loess landslides. [ABSTRACT FROM AUTHOR]

The size and spatial distribution of loess slides are important for estimating the yield of eroded materials and determining the landslide risk. While previous studies have investigated landslide size distributions, the spatial distribution pattern of landslides at different spatial scales is poorly understood. The results indicate that the loess slide distribution exhibits a power-law scaling across a range of the size distribution. The mean landslide size and size distribution in the different geomorphic types are different. The double Pareto and inverse gamma functions can coincide well with the empirical probability distribution of the loess slide areas and can quantitatively reveal the rollover location, maximum probability, and scaling exponents. The frequency of loess slides increases with mean monthly precipitation. Moreover, point distance analysis showed that > 80% of landslides are located < 3 km from other loess slides. We found that the loess slides at the two study sites (Zhidan and Luochuan County) in northern Shaanxi Province, China show a significant clustered distribution. Furthermore, analysis results of the correlated fractal dimension show that the landslides exhibit a dispersed distribution at smaller spatial scales and a clustered distribution at larger spatial scales. [ABSTRACT FROM AUTHOR]

Multi-sensor observation is very important for monitoring landslide disasters. Since various surveying techniques are currently available for detecting variational slope activities from different perspectives, studies have focused on integration of multi-source information for the analysing landslide displacements. In this study, a general multi-source data fusion scheme for landslide monitoring based on three-dimensional variation (3DVar) data assimilation was developed. The scheme was used to fuse different observations of Xishancun Landslide in Li County, Sichuan Province, China. First, the displacement observations obtained by a Global Positioning System (GPS) and Borehole Inclinometers (BIs) were assimilated for accurate evaluation of slope activities. Then, slope Stability Index (SI) was introduced to validate the assimilation results within a time interval. SIAssi values calculated using the integration model developed in the present study were compared with SIFS simulated by a physically based landslide model. The correlation coefficient between them ss 0.75, which is larger than those with SIGPS (0.45) or SIBIs (0.41) values determined by the GPS and BIs respectively. The assimilation results are thus confirmed to be more credible for slope stability simulation. [ABSTRACT FROM AUTHOR]

This study evaluates and compares landslide susceptibility maps of the Baxie River basin, Gansu Province, China, using three models: evidential belief function (EBF), certainty factor (CF) and frequency ratio (FR). First, a landslide inventory map is constructed from satellite image interpretation and extensive field data. Second, the study area is partitioned into 17,142 slope units, and modelled using nine landslide influence parameters: elevation, slope angle, slope aspect, relief amplitude, cutting depth, gully density, lithology, normalized difference vegetation index and distance to roads. Finally, landslide susceptibility maps are presented based on EBF, CF and FR models and validated using area under curve (AUC) analysis. The success rates of the EBF, CF and FR models are 0.8038, 0.7924 and 0.8088, respectively, while the prediction rates of the three models are 0.8056, 0.7922 and 0.7989, respectively. The result of this study can be reliably used in land use management and planning. [ABSTRACT FROM AUTHOR]

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Landslides (generally including debris flows) seriously threaten life and property. Studies on landslide risk are of great significance for regional disaster prevention and mitigation. In this study, the grid of 1 km ×1 km was selected as the assessment unit to exhibit the spatial distribution of landslide risk in China. With the support of Geographic Information System (GIS) technique, the combined model of certainty factor model (CF) and logistic regression model (LR) was applied to assess the landslide hazard, Liu's method was used to evaluate the landslide vulnerability, and the product of hazard multiplied by vulnerability was used to represent the landslide risk. The spatial patterns of the hazard, vulnerability and risk of landslides were displayed on the national scale of China. The results indicated that the high hazard zones are commonly distributed in the south of Yangtze River, especially the very high hazard zones are concentrated in the southwest of China. The high vulnerability zones are mainly distributed in the eastern coastal cities with developed economy and dense population. The distributional pattern of landslide risk is approximately divided by Heihe-Tengchong population density line. The west of the line is mainly distributed low risk areas; whereas the east of the line contains moderate and high risk areas. The classes of very low, low, moderate, high and very high risk areas account for 40.51%, 18.34%, 33.86%, 7.28% and 0.01% of the total area respectively. The macro-scale regional assessment is needed in China, which may benefit the top design of landslide risk reduction and territorial functional zoning. [ABSTRACT FROM AUTHOR]

Reservoir impoundment and water fluctuations have endangered the stability of the reservoir bank slopes, which have experienced several small-scale shallow slides/collapses and few large-scale slopes with failures/deformations. Field investigations, unmanned aerial vehicle technology and site displacement monitoring dates are used to study the spatiotemporal distribution and mechanisms of the reservoir landslides within the Dagangshan reservoir. The reservoir landslides can be divided into two main types: shallow small-scale slides and unstable deformation slopes, which are clearly associated with reservoir impoundment and fluctuations of the water level. Small-scale slides/collapses are continually distributed along the river banks nearest to the water level, but deep-seated landslides have usually experienced a long history, and some obvious precursor phenomena may exist in the slopes. According to the recognition of deformational and failure evolutionary processes of large-scale slopes, and based on hydrology and geomechanics, two types of potential failure models (creep-shear-tension failure and toppling-tensile-shear model) are proposed to interpret the mechanisms of the deformation slopes in the Dagangshan reservoir. Furthermore, some preliminary discussion is presented concerning the non-significant development of landslides in the Dagangshan reservoir that are also associated with smaller water fluctuations due to the daily regulation type of the reservoir. [ABSTRACT FROM AUTHOR]

On 13 November 2015, a disastrous rockslide-debris avalanche occurred in the Lidong village of Liandou District, Lishui City (south-western Zhejiang, China), which overwhelmed 27 houses and killed 38 people in the village. The geological survey on the slope revealed that prior to this incident, a rupture zone existed between the highly weathered volcanic rock and the underlying weathered volcanic rock in this slope. The rupture zone has dip direction coinciding with the slope aspect and some of the existing cracks in the surface are likely extended from the surface to the rupture zone. This study summarizes the past history and main characteristics of the landslide, simulates the correlation between the safety factor of the slope and the rainfall level using a physically based triggering model, and analyses the distribution of the effective antecedent rainfall that influences landslide occurrence, showing that the landslide on 13 November 2015 was triggered by the very low intraday rainfall and the 15-day antecedent rainfall. The results demonstrate that for a potential unstable slope with increasingly wet antecedent conditions, very low daily rainfall may be able to trigger landslides on a given day, which is important for improving landslide warning systems based on the rainfall-landslide relationship. [ABSTRACT FROM AUTHOR]

This study conducted terrain analysis and remote sensing image interpretation to determine the distributions of historical and potential landslides in Shang-an Village and elucidate the characteristics of landslide development within the area. Using the discrete element method, we constructed a digital model of the study area in order to explore the landslide movement processes and determine the scope of influence with regard to deposition. The scope of influence was then used to perform hazard zoning in the creation of a landslide hazard map for use as a reference in future land planning and disaster prevention. Our results revealed 13 potential landslide masses in the study area, 92% of which are on unstable slopes with limited landslide activity. These areas should be the focus of future monitoring and remediation projects. Direct impact from falling rock is the main concern in the area roughly 50 m around the sliding masses in the source areas of the study region and approximately 30 m around the creek beds upstream. In contrast, being buried by debris flow is the concern in the lowland areas roughly 30 m from creek channels in the middle and lower reaches. [ABSTRACT FROM AUTHOR]

Based on intensive landslide surveys and interpretation of remote sensing images, we established a loess slide inventory map of the Yan’an region in northern Shaanxi province, China. According to their slip surfaces, we grouped loess slides into three categories: landslides in loess, red clay contact landslides, and bedrock contact landslides. Results indicate an obvious power law relationship among loess slide length, area, and volume. Moreover, landslide size depends remarkably on the slip surface. The average area and length of red clay contact landslides are 5.52 and 2.45 times larger than those of landslides in loess. Intermediate-slope relative height and slope gradient have a prominent role in landslide formation. The size distribution of loess slides was examined with respect to the slope height and slope gradient. The analysis revealed that slope height and slope gradient were the most dominant controlling factors for loess slide size. The loess slide became larger with the increasing relative height of the slope, and there is an obvious linear or power law relationship between loess slide size and slope relative height. On the contrary, landslide size gradually decreases as the slope gradient increases at a certain level. [ABSTRACT FROM AUTHOR]

Slow moving landslide is a major disaster in the Three Gorges Reservoir area. It is difficult to compare the deformation among different parts of this kind of landslide through GPS measurements when the displacement of different monitoring points is similar in values. So far, studies have been seldom carried out to find out the information hidden behind those GPS monitoring data to solve this problem. Therefore, in this study, three landslides were chosen to perform landslide displacement analysis based on fractal theory. The major advantage of this study is that it has not only considered the values of the displacement of those GPS monitoring points, but also considered the moving traces of them. This allows to reveal more information from GPS measurements and to obtain a broader understanding of the deformation history on different parts of a unique landslide, especially for slow moving landslides. The results proved that using the fractal dimension as an indicator is reliable to estimate the deformation of each landslide and to represent landslide deformation on both spatial and temporal scales. The results of this study could make sense to those working on landslide hazard and risk assessment and land use planning. [ABSTRACT FROM AUTHOR]

Landslides are one of the major natural disasters that are frequently occurring in southwestern China. The main objective of this study is to investigate the relationship between failure of landslide and fractal dimension using displacement time-series. Taking two slopes in southwestern China as examples, we estimate the fractal parameters of displacement time-series and analyze the relationship between fractal dimension and the stability of the slope during the evolution of landslides. The result shows that the fractal dimension increases when a landslide undergoes a transition from stability to failure, but decreases during the evolution from active to stable. Fractal dimension has a critical value of 1.10 in the transition between active and stable, which indicates that fractal dimension is an important nonlinear parameter in the evaluation of the stability of landslides, although more studies and in situ data are required for validation. [ABSTRACT FROM AUTHOR]

On 3 August 2014, an earthquake of Mw 6.5 happened in Ludian County, Yunnan Province, China. This earthquake triggered hundreds of landslides of various types, dominated by shallow slides, deep-seated slides, rock falls, debris flow and unstable slopes. Using field investigations and remote sensing images, 413 landslides triggered by the Ludian earthquake were statistically analyzed. Statistical analyses show that most of the landslides are shallow slides with a small volume. Most of these landslides are concentrated near the epicentre with distances ranging from 6–12 km, especially at the upper slope along the river valley. The number of landslides increased with increasing distance from the epicentre (0–9 km) and then decreased with increasing distance from the epicentre (>9 km). The landslides decreased in density with increasing distance from the fault rupture. More than 70% of the landslides occurred on the right side of the Xiyuhe-Zhaotong fault, when viewed from Southwest (SW) to Northeast (NE). Slope aspect and gradient had a substantial influence on the landslide distribution and landslide density increased with increasing slope gradient. Approximately, 65% of the landslides happened at the back slope with respect to the earthquake epicentre. [ABSTRACT FROM PUBLISHER]

On 12 May 2008 the magnitude 8.0 Wenchuan Earthquake erupted on the eastern fringe of the Tibetan Plateau in China, triggering many landslides onto towns and villages, leading to substantial loss of life and damage to property and the infrastructure. Local characteristic cloudy weather posed serious difficulties upon regional remote observation for disaster rescue in the mountainous region. To overcome weather limits to remote sensing, the study advanced one quick methodology to extract primary regional landslides with 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and terrain slope information. The threshold of temporal NDVI jump (value of 0.4) was introduced to discriminate landslide induced sharp NDVI decline from seasonal variations of vegetation cover, while slope information can be used to further confirm land cover change caused by a landslide. The study indicated that the Wenchuan Earthquake induced about 170 km2 of landslides in the southern half of the Longmenshan Fault Zone, most of which was formerly covered by forest or shrub. An ecological restoration of the areas affected by landslides will therefore be quite difficult. In addition, the result emphasizes that regions with slopes over 15° are exposed to high landslide risk, which should be carefully taken into account for settlement and transportation planning. [ABSTRACT FROM AUTHOR]