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Strong earthquake geological hazards cause significant social and economic losses. The assessment of post-earthquake socioeconomic development levels is one of the important bases from which to measure the recovery capacity of hazard areas. However, the long-term impact of geological hazards is rarely considered in the assessment of the socioeconomic development level of a mountainous earthquake-stricken area. The purpose of this paper is to study the complex relationship between earthquake geological hazard effects and socioeconomic development in the long-term post-earthquake development and reconstruction of mountainous extremely earthquake-stricken areas, to provide a reference for the study area to achieve regional sustainable development goals and high-quality development. On this basis, using the economic, social, ecological environment and other relevant data from 2008 to 2016, and using unsupervised machine learning algorithms, a socioeconomic development level evaluation model based on spectral clustering was established, and the effects of different kernel function scale parameters on the model were analyzed. The optimal parameters were determined, and the spatiotemporal analysis of the socioeconomic development level of the study area was carried out. The research results show that the performance of the evaluation model is optimal when the output category is 4 and the scale parameter is 0.26, and the scale parameter of the kernel function is an important indicator that affects the accuracy of the model. From 2008 to 2016, the socioeconomic development level of Qingchuan has always been at a very low level, Wenchuan and Beichuan have always been at a low level, Shifang and Mianzhu have always been at a high level, and Pengzhou and Dujiangyan have always been at a very high level. The socioeconomic development level of Anxian, Maoxian and Pingwu fluctuates greatly over time. Another interesting finding is that the socioeconomic development level of the mountainous earthquake-stricken area has a strong correlation with its own industrial structure and landslide hazard effects. The work done in this paper is of great significance for understanding the temporal and spatial effects of hazards and understanding the complexities of regional disaster systems. [ABSTRACT FROM AUTHOR]

This paper gives an account of the diverse dimensions of research on disaster risk reduction in mountain regions derived from an open call of the Journal of Mountain Science that brought 21 contributions. This special issue includes topics as diverse as landslide dynamics and mechanisms, landslide inventories and landslide susceptibility models, insights to landslide hazards and disasters and mitigation measures, disaster response and disaster risk reduction. The overall structure of the paper takes the form of three sections. The first part begins by laying out the significance of disaster risk reduction in mountain areas, whereas the second one looks at the research insights on disaster risk reduction in mountains provided by the contributions comprised in the special volume. The final section identifies areas for further research. [ABSTRACT FROM AUTHOR]

During the last decades, natural hazards, such as earthquakes, volcanic eruptions, landslides, floods, storms, droughts, and cyclones, have increased in frequency and severity, influenced by climate change and population growth. These natural hazards can become sudden-onset disasters, causing human losses and property damages that affect economic stability and growth. Although there is a pressing need for risk decrease and adaptation strategies to these unexpected events, targeted natural hazard insurance would be a valuable tool to counteract governmental and individual consequences. This paper summarizes an overview of risk assessment and mitigation strategies based on geological, geomorphological, and meteorological factors. The study includes an examination of monitoring systems for movement and gas emissions, risk and emergency maps, and highlighting the vulnerability of different areas at national and regional levels. Furthermore, the paper addresses the importance of promoting comprehensive geological and geotechnical knowledge among citizens of every socio-economical group and proposing the tools to effectively deliver the message, aiming at increasing the willingness for natural hazard insurance at both individual and governmental scales for human and property protection. [ABSTRACT FROM AUTHOR]

Landslide is one of the most common and severe geological disasters, which significantly endangers people's lives and properties. Therefore, adequate evaluation of landslide susceptibility is an important for disaster control and mitigation. We selected Pengyang County as the study area, and divided the indicators into different state levels by graded area ratio and landslide occurrence frequency distribution. Meanwhile, the levels of each indicator were assigned the unique scores according to the corresponding information values. Given that the issue of subjectivity in single AHP model significantly influence the model performance in landslide susceptibility prediction, a hybrid model, namely variable-weight based weighted information value (VW-AHP-IV) model, was applied in this paper for landslide susceptibility evaluation. The study area was classified into five classes by the natural breakpoint method: very high susceptibility area (8.26%), high susceptibility area (19.78%), medium susceptibility area (29.93%), low susceptibility area (31.57%) and very low susceptibility area (10.46%). In addition, this paper also discussed the influences of precipitation and human activities on landslide occurrence. According to the evaluation results and discussion, the study area was classified into three prevention and control areas: focus prevention and control area, sub-focus prevention and control area, and general prevention and control area. For each area, the corresponding prevention and control suggestions were proposed in order to reduce the occurrence of landslide disasters. [ABSTRACT FROM AUTHOR]

Slow-moving landslide-prone areas (SLAs) are unstable objects on the terrestrial surface that can collapse rapidly when provoked by earthquakes, leading to infrastructure damage. It is critical to identify SLAs prior to earthquake events and assess their hazard-causing probabilities when triggered. An assessment approach of earthquake-triggered geohazards is proposed in this paper by combining interferometric synthetic aperture radar (InSAR) derived SLAs and geological and geomorphological factors. Taking the Ms6.8 Luding earthquake, which occurred in the Sichuan Province of southwestern China on September 5, 2022, as an example, 1320 scenes of Sentinel-1 SAR data in western Sichuan were processed using the small baseline subset (SBAS) InSAR technique before the earthquake. After the earthquake, hazard probability assessment was performed in real-time by filtering the SLAs using a spatial analysis technique with geological and geomorphological factors, e.g., real-time peak ground acceleration (PGA), slope, distance to fault (DTF), and distance to the river (DTR) data. The results show that 11 SLAs were classified into high-risk zones. As verified by the Luding co-seismic landslide dataset from visual interpretation of optical remote sensing images, 142 coseismic landslides were triggered by the earthquake in these high-risk regions. In these areas, an ancient landslide, with high pre-earthquake displacement rates (−50 mm/year) on the scarp was reactivated under the Luding earthquake forces. This method can provide a scientific tool for disaster mitigation and rapid response emergency management. [ABSTRACT FROM AUTHOR]

Landslides are one of the most destructive geological disasters and have been caused many casualties and economic losses every year in the world. The reservoir area formed by the world's largest hydropower project, Three Gorges Hydropower project of China, has become a natural testing ground for landslide prediction in the hope of reducing losses. In this paper, a new algorithm with strong optimization ability, the water cycle algorithm (WCA), is combined with the extreme learning machine (ELM) to improve the prediction accuracy of step-wise landslide. The gray relational grade analysis method was adopted to determine the main influencing factors of the landslide's periodic displacement. Then, the determined factors were used as the input items of the proposed WCA-ELM model, and the corresponding periodic displacement was used as the model output item. Taking the Liujiabao landslide in the Three Gorges Reservoir area as a case history, the proposed model was verified through a comparison with the measurements. The results showed that the model has a faster convergence rate and higher prediction accuracy than the traditional back-propagation neural network model and ELM-model. The water cycle algorithm is suitable for optimizing the accuracy of the extreme learning machine model in landslide prediction. [ABSTRACT FROM AUTHOR]

Landslides, widespread and highly dangerous geological disasters, pose significant risks to humankind and the ecological environment. Consequently, predicting landslides is vital for disaster prevention and mitigation strategies. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. Prior studies have shown that in the context of landslide susceptibility, these models frequently underperform relative to tree-based machine learning algorithms. This shortcoming has restricted the application of deep learning in this domain. To overcome this challenge, this study presents the TabNet algorithm, which combines the interpretability and selective feature extraction of tree models with the representation learning and comprehensive training capabilities of neural network models. This paper explores the potential of employing the TabNet algorithm for landslide susceptibility analysis in China's WanZhou region and evaluates its performance against traditional machine learning techniques. The experimental data indicate that the TabNet algorithm achieves a recall score of 0.898 and an AUC of 0.915, demonstrating a generalization capability that is comparable to that of classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

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

In the last 10 years, also due to climate change, extreme rain events have affected the Basilicata region (southern Italy) causing landslides and floods that have damaged urban fabric, commercial activities and transport infrastructures. In many of these cases, the civil protection system, involving national (DPC) and regional (DRPC) Civil Protection Departments, was activated to manage the emergency phase in cooperation with local administrations and scientific institutions, which in this context are referred to as competence centres (CdCs). Among the latter, the Institute of Methodologies for Environmental Analysis (IMAA) of the Italian National Research Council (CNR) has been frequently involved in carrying out geophysical investigations in landslide areas, especially during the post-event phase. This paper reports the main results of the in-field geophysical surveys carried out in two areas of the Basilicata region affected by significant landslides in the last 10 years. The aim of the surveys was to provide the DRPC technicians with a useful subsoil geophysical model to improve the knowledge of the geological setting of the slope, to reconstruct the geometry of landslide body and to highlight high water content areas, in order to support the decision-making process. At the end of the paper, a discussion follows with the lessons learned from each case study along with recommendations on how to possibly improve the application of geophysical techniques in landslide investigations in order to further increase their impact. [ABSTRACT FROM AUTHOR]

As one of the most destructive geological disasters, a myriad of landslides has revived and developed in the Three Gorges Reservoir area under the combined action of various detrimental factors. Therefore, the pertinently regional landslide susceptibility mapping (LSM) is of great significance for disaster prevention and mitigation. In this study, LSM is prepared by using a boosting-C5.0 decision tree model. Under the landslide verification of on-site investigations, the study area is divided into accumulation and rock areas, and a total of 12 impact factors are selected. TOL and VIF are employed to determine the multicollinearity among the impact factors. The independent training (80%) and validation (20%) datasets are constructed by random sampling for LSM. ANN, C5.0, and SVM are selected for comparative analysis. The results show that there is no rigorous multicollinearity among the impact factors proposed in this paper. The landslide susceptibility in the study area is divided into low, moderate, high, and very high. The highest susceptibility area distributes along the riverside where the landslide ratio is 37.05% in boosting-C5.0 model. Then the ROCs are expropriated to infer the accuracy of each model. The boosting-C5.0 performs the best with the largest area under the curve in both accumulation and rock areas, reaching at 0.991 and 0.990 in the validation sets, respectively. Finally, the composite modification of the 5 validation sets shows that the uncertainty of boosting-C5.0 is concentrated in the intermediate probability areas of susceptibility. This study reveals the feasibility of machine learning in landslide susceptibility assessment, which could provide a basis for the risk management and control of geological disasters. [ABSTRACT FROM AUTHOR]

Landslide is one of the most highly frequent natural hazards that can bring serious casualties. One of the most susceptible landslide regions in Indonesia is Bogor area (the Regency and City of Bogor), which records the highest landslide events in the Province of West Java, Indonesia. An assessment of landslide susceptibility is one of the mitigation measures that can spatially model the zone of landslide hazard. Recently, the Landslide Susceptibility Mapping (LSM) model has been developed using Machine Learning (ML) algorithms. However, there is still no agreement yet on which ML technique is the most appropriate for LSM. Accordingly, this paper aims to explore and compare the 7 ML algorithms for generating the most promising LSM. The LSM uses the available 13 landslide causal factors and a dataset consisting of 822 authorized landslide records and 822 prepared non-landslide points. The resulting LSMs are classified into 5 susceptibility levels, and evaluated through the Area Under Curve (AUC) of the Receiver-Operating Curve (ROC) and statistical indices (sensitivity, specificity, precision, F1-score, and accuracy). The resulting LSMs present that: (1) the very high (VH) class has the largest area percentage in all LSM models, (2) generally, the 7 MLs perform excellent for achieving > 90% AUC value, except for the Decision Tree (DT) (87.68%) in model classification, and (3) moreover, the overall accuracy (ACC) reflects that Random Forest (RF) outperforms the other MLs in model prediction. With this promising result, ML-based LSM models can be promoted as one of the mitigation measures for landslide disaster management. [ABSTRACT FROM AUTHOR]

Accurate assessment of seismic landslides hazard is a prerequisite and foundation for post-disaster relief of earthquakes. An Ms 5.7 earthquake occurring on September 7, 2012, in Yiliang County, Yunnan Province, China, triggered hundreds of landslides. To explore the characteristics of coseismic landslides caused by this moderate-strong earthquake and their significance in predicting seismic landslides regionally, this study uses an artificial visual interpretation method based on a planet image with 5-m resolution to obtain the information of the coseismic landslides and establishes a coseismic landslide database containing data on 232 landslides. Nine influencing factors of landslides were selected for this study: elevation, relative elevation, slope angle, aspect, slope position, distance to river system, distance to faults, strata, and peak ground acceleration. The real probability of coseismic landslide occurrence is calculated by combining the Bayesian probability and logistic regression model. Based on the coseismic landslides, the probabilities of landslide occurrence under different peak ground acceleration are predicted using a logistic regression model. Finally, the model established in this paper is used to calculate the landslide probability of the Ludian Ms 6.5 earthquake that occurred in August 2014, 78.9 km away from the macro-epicenter of the Yiliang earthquake. The probability is verified by the real coseismic landslides of this earthquake, which confirms the reliability of the method presented in this paper. This study proves that the model established according to the seismic landslides triggered by one earthquake has a good effect on the seismic landslides hazard assessment of similar magnitude, and can provide a reference for seismic landslides prediction of moderate-strong earthquakes in this region. [ABSTRACT FROM AUTHOR]

Landslide is a type of natural hazards causing many casualties in mountainous and rainy areas. Therefore, recognizing areas those that have potentials for happening such type of hazards is an important task. For this, methods of landslide susceptibility mapping, categorized mainly into two general data- and knowledge-driven approaches, have been widely developed and applied. In this regard, stochastic and systemic errors, respectively, associated with adequacy in the number of known landslide locations and subjectivity of expert judgment applied to assign weights of landslide conditioning factors are two main issues affecting the data- and knowledge-driven approaches. These issues are, in fact, types of bias and uncertainties that adversely affect landslide susceptibility mapping practices. This paper aims to adapt continuous fuzzification and geometric average multi-criteria decision-making approaches to overcome the aforementioned disadvantages of the existing landslide susceptibility mapping methods. In the method proposed weights of landslide conditioning factors are continuously assigned without using known landslide locations as training points, and without using expert opinion in categorization of values of landslide conditioning factors into arbitrary classes and assigning subjective weights. We applied the procedure proposed on a dataset of Oshvand watershed, Hamadan Province, Iran, to demonstrate its effectiveness. The results demonstrated that the continuous weighting method applied is more reliable than the existing methods those which apply classified values of landslide conditioning factors. [ABSTRACT FROM AUTHOR]

This study determines the runout behavior (Attabad landslide, Hunza, Pakistan) of one of the biggest landslides in Pakistan's history, along the highest and strategically most important highway of the world. On January 4, 2010, at 08:36 local time, 45 Mm3 of rock mass flowed down the hill slope for 1060 m and fell in Hunza River, thus blocking the river's flow and making an artificial dam. This paper examines the landslide's failure process based on seismic data obtained from a published paper. The average velocity of the slide was found to be 14.32 m/s. To better understand the landslide dynamics and failure phenomena, a numerical simulation was conducted using DAN3D to simulate displaced materials' runout behavior. Simulation results indicate that the slope's failure lasted for 70 s, which is in good agreement with seismic wave recordings of 70 s. The combined frictional–Voellmy model obtained the most accurate results for simulation. Further, to verify the results, another simulation was run using RAMMS debris flow software; it was found that the results of both the software's are in good agreement. It is expected that the selected model and its parameters will help understand similar kind of rock avalanches in the area, which will help concerned agencies improve landslide prediction along Karakoram Highway. [ABSTRACT FROM AUTHOR]

On October 2017, due to continuous rainfall, there was a massive river blockage induced by a landslide in Guang'an Village, Chongqing, China. Long-term monitoring analysis has shown that there remain four strong deformation areas on the slope, which seriously threaten the life and property of nearby residents. In this paper, a granular flow model and an elasto-visco-plasticity model were applied to reproduce and predict the landslide event that hit Guang'an Village. The results showed that the landslide gradually moved along the sliding surface, pushing loose deposits and blocking the Xixi River. The numerical reproduction results of the 2017 event are consistent with the actual slope deformation and failure process and deposit morphology. The simulated maximum depth-averaged velocity of this landslide was approximately 1.89 m/s, and the height of the landslide dam was approximately 10 m. After the landslide occurred in 2017, several large deformation areas appeared in the vicinity of the sliding area, and the right rear side of the sliding mass in area III has the largest deformation volume, accompanied by the most developed surface crack and the most intense deformation. There is a risk that the Xixi river will be blocked again. Therefore, with the same parameter and numerical model, a sliding–pushing–blocking dynamic prediction analysis of the strong deformation area III was conducted. The pushing motion of the mass in this area will reactivate the landslide mass observed in 2017. The maximum depth-averaged velocity of deformation area III was 0.5 m/s, and the maximum depth-averaged velocity of landslide deposition was 0.45 m/s. The length of the blocking dam formed by the mass of deformation area III along river was approximately 780 m, 30 m longer than that in 2017. The predicted height of the landslide dam was 14.5 m, approximately 4.5 m higher than that in 2017. The length of the landslide dam reservoir was predicted to be 2.55 km along the Xixi River, which may submerge the Waping Village. This study supports the landslide hazard prevention, reveals the whole movement process of sliding-pushing-blocking, and provides a new research idea and method for the landslide movement prediction. Hence, this study can serve as a reference for the hazard prevention and mitigation of such chain disasters. [ABSTRACT FROM AUTHOR]

The primary aim of this paper is to highlight the importance of scientific collaboration for landslide disaster risk reduction in Mexico. Drawing upon specific undertakings into applied research, this article attempts to outline the emerging role of the ICL World Centre of Excellence (WCoE) based in the Institute of Geography (IGg) of the National Autonomous University of Mexico (UNAM), Mexico City. This paper begins by offering a brief introduction of the significance of WCoEs in the international landslide disaster research sphere. In the second section, general information of the institutional framework and the IGg-UNAM WCoE is provided. The third part contextualises the landslide research engagement with reference to the activities carried out under the umbrella of the International Consortium on Landslides (ICL). Overall insights are offered in the final section. [ABSTRACT FROM AUTHOR]

The great M ~ 8 1920 Haiyuan earthquake (HYEQ) was one of the largest and most deadly earthquakes in China in the last century, with ~ 234,000 deaths. The earthquake occurred within the Loess Plateau of northern China, where Quaternary loess deposits form a distinctive blanket across the landscape. Large regions of this loess cover experienced co-seismic landslides. Based on an analysis of the original disaster reports, field surveys, and satellite image interpretation, we have compiled the shaking effects of the earthquake, including the distribution of landslides, fatalities, and structural damage. Landslides triggered by the HYEQ (n > 7,000) are concentrated south of the Haiyuan fault, in a region that has both thick loess cover and long-term relief generated by the drainage network. This distribution is spatially separated from landslides triggered by other earthquakes. We find that in contrast to previous studies, the most important factor in the severe death toll of the HYEQ was the collapse of housing by ground shaking, including collapse of loess house-caves. Landslides were a secondary factor; although up to 32,000 deaths occurred in areas with intense landsliding. Based on the revised distribution pattern of landslides and damage (e.g., house collapses), we suggest that the isoseismal intensity IX line extends south of previous locations. We have also identified 126 dammed lakes created by co-seismic landslides, which form major modifications of this semi-arid landscape. The research methods in this paper, combining historical review, satellite image interpretation, and field validation of landslides, can be used as a reference for studies of other areas affected by historical earthquakes and co-seismic landslides, elsewhere in the Loess Plateau and beyond. [ABSTRACT FROM AUTHOR]

Landslide is one of the most serious and widespread disasters in natural disasters, which seriously endangers the lives and property of residents. Based on the occurrence mechanism of landslide, this paper uses the analytic hierarchy process-comprehensive index (AHP-CI) model and takes Yuan'an County in China as an example to study and evaluate the susceptibility characteristics of landslides in this area. The geological conditions in Yuan' an County are complex, and the landslide is extremely serious. This paper will discuss separately from the inherent factors and the inducing factors that lead to the occurrence of landslides. Combined with the geological environment conditions of Yuan' an County, 5 inherent factors, including the slope, slope structure, rock and soil characteristics, geological structure, and the lithology were selected as evaluation indexes in this evaluation. Based on the spatial superposition function of GIS, the landslide susceptibility in Yuan' an county was evaluated and zoned. And in the chapter of analysis and discussion, the influence mechanism of two induced factors of rainfall and human activities on the occurrence of landslide was discussed. Since the inherent factors could not be easily changed, but the induced factors could be controlled artificially, so according to the evaluation results, scientific prevention and control measures could be put forward for different grades of landslide prone areas in the study area. This paper studied the evaluation of landslide susceptibility based on the occurrence mechanism of landslide, which has good practicability and can provide important reference for the evaluation and prevention of landslide susceptibility in other areas. [ABSTRACT FROM AUTHOR]

The strong spatial nonuniformity of dam soil is a key factor for studying the safety and stability of landslide dams. In this paper, a 500 × 500 × 500 mm cubic model box was made, and the dam soil was deposited in it by layered deposition and different sliding angles to investigate the nonuniformity of the deposition. The depositional characteristics and particle size distribution (PSD) variations in different zones of different depositions were analyzed. Furthermore, based on the basic principle of grading entropy, the particle distributions of different depositions using the entropy parameter A−B coordinates were discussed. Finally, an index of nonuniformity Nd was proposed to quantitatively assess the nonuniformity degree of the deposition. We yielded that as the sliding angle increased, the deposition showed prominent sorting characteristics in the sliding direction. The coarser and finer particles were mainly concentrated in the front and back parts of the deposition, respectively. Compared with the traditional characteristic parameters, the grading entropy is more meticulous for characterizing the PSD curve. In the entropy parameter A−B coordinates, the points for the expected uniform deposition are more concentrated, which indicates that the nonuniformity of this deposition is smaller. The points of different zones for the sliding deposition are arch-shaped, and their distribution is more dispersed and directional in these coordinates, which indicates a greater nonuniformity of this deposition. For the sliding deposition, the index of nonuniformity Nd of different depositions tends to increase and then decrease with increasing sliding angle. The Nd of the deposition made by the sliding angle of 60° is the largest at 0.173. However, that of the expected uniform deposition is only 0.057. This study improves the understanding of spatial nonuniformity and aids the disaster prevention and mitigation of landslide dams. [ABSTRACT FROM AUTHOR]

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

Landslide susceptibility assessment is highly valuable for disaster prevention and mitigation. This study utilized the aspects of data and content to comprehensively examine the research status of landslide susceptibility. First, we used CiteSpace to visually analyze papers with "landslide susceptibility" as their theme word in the Web of Science database from 1991 to 2020. Next, we summarized the characteristics of quantitative trends, journals, authors, organization types, and keywords, and created a map of authors, institutions, and keywords. Then we presented the common methods and their advantages and disadvantages of landslide inventory, evaluation index, evaluation unit, evaluation model and verification method, combs the shortcomings of each part at the present stage, and looks forward to the possible research direction in the future. Finally, the research difficulties of landslide susceptibility in spatial scale, qualitative and quantitative problems, and spatial representation of landslide information are discussed. We find that the development of 3S and new technology in computer field promotes the development of landslide susceptibility research, makes landslide inventory more efficient and accurate, makes the assessment factor system considering factor contribution more reasonable, and makes more intelligent models applied to landslide susceptibility research. The results are beneficial for researchers to understand the current landslide susceptibility condition and can provide a reference for subsequent landslide susceptibility studies. [ABSTRACT FROM AUTHOR]

Landslide early warning systems (LEWS) can be categorized into two groups: territorial and local systems. Territorial landslide early warning systems (Te-LEWS) deal with the occurrence of several landslides in wide areas: at municipal/regional/national scale. The aim of such systems is to forecast the increased probability of landslide occurrence in a given warning zone. The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. This paper describes a new Excel user-friendly tool for the application of the EDuMaP method, originally proposed by (Calvello and Piciullo 2016). A description of indicators used for the performance evaluation of different Te-LEWS is provided, and the most useful ones have been selected and implemented into the tool. The EDuMaP tool has been used for the performance evaluation of the "SMART" warning model operating in Piemonte region, Italy. The analysis highlights the warning zones with the highest performance and the ones that need threshold refinement. A comparison of the performance of the SMART model with other models operating in different Te-LEWS has also been carried out, highlighting critical issues and positive aspects. Lastly, the SMART performance has been evaluated with both the EDuMaP and a standard 2 × 2 contingency table for comparison purposes. The result highlights that the latter approach can lead to an imprecise and not detailed assessment of the warning model, because it cannot differentiate among the levels of warning and the variable number of landslides that may occur in a time interval. [ABSTRACT FROM AUTHOR]

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

The assessment of earthquake-induced landslide hazards is an important prerequisite for disaster prevention and reduction in tectonic active areas. However, few studies have considered the amplification effect of site and topography on ground motion parameters and made the peak ground acceleration (PGA) correction. Based on Newmark's method, taking Litang County, Sichuan Province, China, as the study area, considering the site amplification effect and topographic amplification effect, this paper carried out the assessment of landslide hazards under the action of occasional earthquakes with an exceedance probability of 10% and rare earthquakes with an exceedance probability of 2%. The results show that the hazard of earthquake-induced landslides is higher in the high slopes of loose rock in the middle of Litang County and the steep rock slopes with large topographic relief in the northeast, and low in the southern plateau and central basins. The site and topographic conditions have a significant effect on the nonlinear amplification of PGA, and the corrected PGA is even magnified by 2–3 times in steep mountains. Compared with the occasional earthquakes, the influence of rare earthquakes on the initiation and movement distance of landslides is remarkably improved. This study can provide a valuable reference for potential earthquake-induced landslide hazard assessment and seismic landslide emergency response in Litang County. [ABSTRACT FROM AUTHOR]

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

The paper describes the mesoscale seismic hazard zonation study carried out in the Central Seismic Gap of the Himalaya by integration of ground motion, site and earthquake-induced effects using GIS technique. Three indices, namely (i) earthquake hazard index, (ii) liquefaction susceptibility index and (iii) landslide susceptibility index were estimated for comprehensive seismic hazard zonation mapping in this area. The earthquake hazard index was estimated by integration of data such as lithology, faults, soil, landforms, Gutenberg–Richter parameter (b value), predominant frequency, amplification factor and peak horizontal acceleration using the analytical hierarchy process (AHP) method. Similarly, geology and topographic parameters were integrated using the AHP method to estimate the Liquefaction Susceptibility Index. A multivariate statistical technique was used to estimate the Landslide Susceptibility Index. The three indices were combined to prepare mesoscale seismic hazard zonation map of the area which was classified into low, moderate, and high seismic hazard zones. Validation of the mesoscale seismic hazard zonation map was done using the isoseismal data of past earthquakes. High seismic hazard zones are found in the foothills and Siwalik region of the Himalaya, which consist of alluvial sand and unconsolidated sediments, whereas moderate zones are identified partially in the foothills, Siwalik and the Lesser Himalaya. This map will be useful for earthquake hazard mitigation in the NW Himalaya. [ABSTRACT FROM AUTHOR]

The landslide caused a huge disaster to the living environment and seriously threatened the lives and property safety of nearby residents. Assess or predict landslide-susceptible the landslide displacement through monitoring are great beneficial to guide landslide control and mitigate these hazards by taking appropriate preparatory measures. In this paper, a new water cycle algorithm optimization BP neural network (BPNN) dynamic prediction model (WCA-BPNN) was established to make up for the shortcoming of BPNN convergence speed. A typical step-wise landslide——Langshuwan Landslide happened in the Three Gorges Reservoir area of China is taken as a case, and the displacement monitoring data of 4 years was used for time series analysis and modeling. The long-term creep effect of the landslide and the short-term acceleration effect of the climate are considered in the model, and the accumulative displacement is divided into two kinds of trend displacement and periodic displacement. The key influencing factors of landslide periodic displacement were screened by gray relational grade analysis method, and then used as learning data. In addition to comparing the predicted value of the model with the measured value, it also compares the accuracy of the three models of BPNN, support vector machine, extreme learning machine under the training conditions of the same learning data set. The results show that the WAC-BPNN model has faster convergence speed and higher prediction accuracy than the traditional BPNN model, and it is also the most accurate of the four models. [ABSTRACT FROM AUTHOR]

India ranks first in the world in terms of fatal landslides. Large vulnerable area (0.42 million km2), high population density and monsoon rainfall make India's landslide management and mitigation task difficult. Therefore, an understanding of landslide occurrences and exposure of socio-economic parameters on a national scale is essential to prioritise landslide mitigation efforts. In this paper, a database of 45,334 landslides (polygons) in India mapped by the National Remote Sensing Centre (NRSC) during the 1998–2018 period was compiled and catalogued in a WebGIS platform. High-resolution satellite data such as IRS PAN+LISS-III, Resourcesat LISS-IV Mx, Cartosat, WorldView, Pleiades and GeoEye were used to map landslides as small as 12 m2 to as big as 1,390,350 m2. GIS analysis using the landslide inventory revealed interesting results about control, exposure and pattern of landslide occurrences in India. The Northwest Himalayas contribute 66.5% of landslides in India, followed by the Northeast Himalayas (18.8%) and the Western Ghats (14.7%). The Greater Himalayan sequence consisting of high-grade metamorphic rocks has a considerable control (32%), and the Main Central Thrust is the major regional structure controlling (12%) landslides in India. In the Northeast Himalayas, the size of landslides and the slope gradient controlling landslide occurrence are less in comparison to the Northwest Himalayas and the Western Ghats. Landslides in the Western Ghats are triggered with a lesser amount of rainfall than the Himalayan regions. Exposure analysis using four key socio-economic parameters in the 145 hilly districts shows that Rudraprayag district is most affected by landslides in India. The understanding derived using the landslide database on a national scale will help to prioritise and strengthen landslide disaster risk reduction strategies in India. [ABSTRACT FROM AUTHOR]

Landslide phenomenon in accumulated erodible bed sediments in a reservoir is one of the issues in hydraulic and sedimentation sciences that has received little attention. We intend to model two-dimensional changes of the water surface in a reservoir and of an erodible bed caused by a non-rigid landslide using a particle-based meshless approach. In this study, a fully explicit three-step algorithm is used. In this method, approximate numerical solution to the equations of the fluid dynamics is obtained by replacing the fluid with a set of particles. The governing equations for water flow and sand mass movement are solved for each particle. The movement of each particle, which is in interaction with other particles, is tracked. Experiments of a dam break on a dry bed, and submarine rigid and non-rigid landslides have been used to validate the method. Results indicate that the model was successfully calibrated against the measured data. Moreover, good agreement with the measured data demonstrates high capabilities of this method in simulating free-surface flows and wave-related phenomena. After the model validation, changes of erodible bed in a reservoir due to a non-rigid landslide were modelled. In this study, non-rigid landslide masses and sediment materials were modelled by non-Newtonian Carreau-Yasuda fluid, which is the novelty in the analysis of this type of natural hazard. Two possible scenarios were analyzed—one with the sliding material lighter, and the other with the sliding material heavier than the deposited sediments. The model was run until the landslide completely collapsed and its full impact was applied to the reservoir bed sediments. Additionally, we waited until the water level reached a steady state. These examples demonstrate that the model presented in this paper can be used as a reliable tool for modelling these phenomena. [ABSTRACT FROM AUTHOR]

Critical acceleration is an inherent property of a slope and determines the slope stability under seismic action. The critical acceleration model is a core element of regional seismic landslide hazard assessment. Therefore, the purpose of this paper is to reveal the influence of different critical acceleration models on assessments of potential earthquake–induced landslide hazards. Traditionally, the Newmark critical acceleration model has commonly been used to evaluate the potential earthquake–induced landslide hazard. This method needs to assume the failure depth of the slope, which leads to an underestimation of the predicted displacement of the seismic landslide. Recently, the prediction equations of critical acceleration based on a parametric study of the limit equilibrium method overcomes the limitation of Newmark critical acceleration model and has been applied to assessments of co-seismic landslide hazards. In this study, we use Newmark critical acceleration model and prediction equations of critical acceleration to obtain the distribution maps of potential earthquake–induced landslide hazard in Shimian County, with peak ground acceleration of 10% and 2% exceeding the probability in 50 years. In addition, the nonlinear effect of site and topographic effects on peak ground acceleration were considered. The results show that Newmark critical acceleration model underestimates the area and value of the predicted displacement, while prediction equations of critical acceleration produces seismic landslides in a wider range of mountainous areas. This indicates that the critical acceleration model has a significant influence on assessments of potential earthquake–induced landslide hazards. In addition, the study not only provides valuable reference for assessment of potential earthquake–induced landslide hazard, emergency response of seismic landslides, and land planning in the study area, it also provides a useful demonstration for the selection of a critical acceleration model in seismic landslide hazard assessments for future researches. [ABSTRACT FROM AUTHOR]

The Loess Plateau can be considered as a landslide-prone area in northwest China. The genera consensus about the interaction between landslide deposit and terrace sediments is not well studied; this paper summarised 40 loess landslides in the South Jingyang Platform, Shaanxi Province, China to help understand of this issue. Four of the loess landslides with high mobility have been analysed in detail. Three trenches T1, T2 and T3 dug after the loess landslides LD37, LD11 and LD38 highlighted the landslide-induced changes in geomorphology and internal geometry of geology, respectively. Furthermore, observation of upwards seepage flow on the profile of trench T3 is believed to be the trigger of the high speed, and long runout flowslides in the study area. A newly developed sandbox apparatus is used to reproduce the landslide kinematics due to a mass travelling over an inclined plane. The sandbox experiments show that the sediments are sheared and pushed upwards after the collision with the deposits. The deposits are then wrapped in a space between sediments, which tends to form the 'sandwich' structure. The distal sediments are thrust when the loess deposits' kinetic energy consistently dissipates, developing the accumulated folded strata. These results reveal the deposits' interactions with the sediments in the study area and provide key guideposts regarding prevention and mitigation of loess landslide hazards. [ABSTRACT FROM AUTHOR]

The landslide hazards occurring in the complex geological genesis accumulation body are usually controlled by the coupling action of many internal and external factors. Therefore, this paper takes the dam-front Danbo accumulation body landslide of Yangfanggou hydropower station on the Yalong River as the geological prototype, and discusses the process and mechanism of slope stability degradation under the combined action of rainfall and slope construction. Based on the detailed understanding of the basic characteristics of the accumulation body, the development characteristics of the landslide and the construction situation of the slope engineering, the study conducted correlation analysis between rainfall and landslide displacement, the physical and mechanical tests of all types of rock-soil masses, and the numerical simulation testing of seepage field variation of the landslide section. It is found that the special slope structure and material composition of the old landslide accumulation layer on the upper part of the Danbo accumulation body are the internal factors for the occurrence of thrust load-induced landslide, and the construction of the slope engineering not only creates free space conditions for sliding, but also provides channels for the infiltration of rainfall into the slope after confluence, which is an external factor that caused the mechanical properties of the sliding zone soil to gradually weaken from the trailing edge to the leading edge. The geomechanical model of such landslide is that the active section of the trailing edge produces the "source of force", the transition section of the middle section affects the occurrence of sliding, and the anti-sliding section of the leading edge controls the occurrence of landslide hazards. The results of this research provide not only a useful supplement to the theory of landslide formation mechanisms but also a scientific basis for guiding the prevention and control of similar hazards. [ABSTRACT FROM AUTHOR]

Giant landslides can cause significant damage to their dynamic runout processes. Understanding the mechanics of the runout process is essential for landslide risk assessment and mitigation design. This paper investigates the runout process of the 1.16 × 109 m3 giant Daguangbao landslide triggered by the 2008 Ms 8.0 Wenchuan earthquake. The Daguangbao landslide is a typical bedding-plane slide and the properties of basal sliding surface play a key role in the runout behaviors of the landslide. The material point method (MPM) is adopted as a numerical tool of the simulations. A thermal-based velocity-weakening friction law is used to simulate the contact between the sliding body and the basal sliding surface. A strain-softening constitutive model is used to evaluate the damages inside the landslide body and their effects on the runout behaviors of the landslide. Numerical results reveal that landslide mass slides along the bedding-plane as a whole body initially and then disintegrates during the runout process. The calculated duration of sliding is 64 s and the maximum velocity reaches 60 m/s. The friction coefficient of the slip surface decreases sharply as the landslide body starts to move, and a steady-state friction coefficient μ ≈ 0.06 is reached when the velocity exceeds approximately 20 m/s. Friction degradation of the slip surface shows a sensible influence on the final runout distance and the depth of the deposit zone. The dynamic fragmentation of the landslide body, the final runout distance, and deposition topography are also significantly affected by material softening. [ABSTRACT FROM AUTHOR]

This paper reports a feasible alternative to compile a landslide inventory map (LIM) from remote sensing datasets using the application of an artificial intelligence–driven methodology. A deep convolutional neural network model, called LanDCNN, was developed to generate segmentation maps of landslides, and its performance was compared with the benchmark model, named U-Net, and other conventional object-based methods. The landslides that occurred in Lantau Island, Hong Kong, were taken as the case study, in which the pre- and post-landslide aerial images, and a rasterized digital terrain model (DTM) were used. The assessment reveals that LanDCNN trained with bitemporal images and DTM yields the smoothest and most semantically meaningfully LIM, compared to other methods. This LIM is the most balanced segmentation results, represented by the highest F1 measure among all analyzed cases. With the encoding capability of LanDCNN, the application of DTM as the input renders better LIM production, especially when the landslide signatures are relatively subtle. With the computational setup used in this study, LanDCNN requires ~ 3 min to map landslides from the datasets of approximately 25 km2 in area and with a resolution of 0.5 m. In short, the proposed landslide mapping framework, featured LanDCNN, is scalable to handle the vast amount of remote sensing data from different types of measurements within a short processing period. [ABSTRACT FROM AUTHOR]

Loess landslides have complicated deformation mechanisms. Accurately describing the internal failure deformation of loess landslides and establishing a theoretical method of landslide instability evaluation for the prevention of subsequent landslides have become important topics in western development project construction in China. This paper presents a case study of the Zhonglou Mountain landslide in Shaanxi Province, China. Based on field investigation results, a two-dimensional stability analysis model was constructed using the finite element method. Taking the deformation characteristics of the landslide as the research basis, the distribution laws of the displacement, stress, and shear strain of this landslide were identified with the strength reduction finite element numerical simulation method. Additionally, the safety factor was evaluated under normal and storm conditions. The numerical simulation results show that the horizontal tensile stress of the landslide was mainly distributed in the middle and upper parts of the landslide under normal conditions, while the vertical tensile stress was distributed near the sliding surface. Under heavy rainfall, the sliding force increased, and the anti-sliding force and anti-sliding section decreased; the location of the maximum shear strain shifted down from the middle and upper parts of the landslide body to the area with a shear crack, and the plastic shear strain area expanded along nearly the entire the sliding surface, leading to the occurrence of a landslide. Thus, the use of anti-slide piles to stabilize the landslide was proposed and tested. Monitoring points were arranged along the sliding surface to evaluate the displacement, stress, and strain responses. The on-site observation results agreed with the modeling results. The use of anti-slide piles was demonstrated to be an effective stabilization method for the Zhonglou Mountain landslide. [ABSTRACT FROM AUTHOR]

Fatal landslides cause severe disasters to human lives and socioeconomic costs in China. In this study, the data non-seismically fatal landslides were collected between 2004 and 2016 in China. The hazard and life risk criteria of these fatal landslides were assessed, and the government's investment and ecological influencing factors for landslide prevention and mitigation were analyzed. There were more fatal landslides in Sichuan, Guizhou, Yunnan, Hunan, and Guangdong provinces. High landslide density value focused on Guizhou, Hunan, and Guangdong province had very high landslide density value, while Gansu held the biggest fatality density value as a result of a few huge fatal landslide events. The Chinese life risk evaluation criterion was higher than those in other countries because there was a greater population density in landslide-prone areas. Nevertheless, the government has invested a great deal of human and financial resources for landslide mitigation over the past 13 years. In total, 244,559 engineering projects were carried out and $15,920.89 million was spent. Thus, a total of 13,603 landslides were successfully predicted and 641,333 persons and $1,372.94 million has been saved. Additionally, the types of land use, afforestation area, and soil erosion management have a positive effect on landslides. However, a trend of reverse increase was presented in fatal landslides. This paper gives a detailed examination of the non-seismically fatal landslide hazard and proves an evaluation of the Chinese government's contribution to landslide mitigation by integrating engineering and ecological measures. [ABSTRACT FROM AUTHOR]

The use of an inventory map of past landslide events in the derivation of susceptibility models is considered common practice. However, evidence of landslide activity may be lost due to various degrees of modification by subsequent landslides, erosional processes, vegetation growth and anthropic influences. The timely detection of active landslides can form an effective supplement to landslide records for improving the accuracy of landslide susceptibility maps. In this paper, we present a landslide susceptibility assessment carried out in a southwestern region of Cyprus using a synergy of differential interferometry and evidential statistics. A measurement of the vertical and horizontal displacements for the period 2016–2018 was done using the Small BAseline Subset multi-pass Differential Interferometric Synthetic Aperture Radar technique. Based on the results, a total of 8859 raster cells/pixels were classified as active landslides. The weight-of-evidence technique was applied to determine the weights of seven geomorphological and hydrological factors to landslide occurrence and compile a susceptibility model. The success and prediction rates of the derived model were calculated as 79.6% and 78.9%, respectively. The validation of the results against the existing landslide inventory indicates an 84% agreement with respect to the moderate and high landslide susceptibility zones. The proposed methodology can complement existing conventional landslide inventories as a means of providing updated landslide activity at frequent intervals and can provide valuable information regarding the distribution of landslides to support a detailed landslide assessment. [ABSTRACT FROM AUTHOR]

Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments. [ABSTRACT FROM AUTHOR]

The reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period. [ABSTRACT FROM AUTHOR]

The study of landslide initiation mechanisms and dynamic process analysis is important for monitoring, predicting, and controlling landslide occurrence. A giant landslide that occurred on July 19, 2018, in Yanyuan County, Southwest China, was investigated, providing a research example. In this paper, the failure mechanism and dynamic process of the Boli landslide were analyzed using field investigations, laboratory experiments, unmanned aerial vehicle (UAV) photogrammetry, remote sensing images, hydrological calculations, and numerical simulations. The results indicated that the antecedent rainfall was not the direct factor to saturate the sliding mass. The runoff supply provided by the comb-like channel group upstream of the landslide reached 709.5 mm, which was an important hydrodynamic triggering point for accelerating the sliding mass to saturation and instability. Numerical simulation results showed that the evolution of the landslide included three stages: low-speed initiation, accelerating sliding, and flow deposition. The maximum sliding speed of the landslide occurred during the second stage and reached 39 m/s. From the source zone to the Taozi Gully, the silt content decreased from 67.9 to 40%, which was an internal particle–triggering factor of the alteration of the sliding mass from the liquefaction-resistant state to the liquefaction-prone state. The study has revealed the failure mechanism of the landslide and presented its dynamic process, which could be used to provide a reference for the study of similar landslides mainly induced by runoff supplied by such channel groups. [ABSTRACT FROM AUTHOR]

In high mountain canyon regions, SAR geometric distortion in imaging side may have an inevitable impact on InSAR deformation information, so the effective deformation information acquisition is critical for landslide identification and deformation mechanisms analysis. The landslide deformation around the reservoir of Gushui Hydropower Station located in upstream of the Lancang River has been focused on in the study. Using SAR satellite parameters and topographic information, the visibility analysis of deformation in radar line-of-sight (LOS) direction has been carried out, and a method to obtain LOS effective deformation information based on the visibility analysis has been proposed. The small baseline subsets (SBAS) technique is used to process the L-band and C-band SAR data, and the area affected by the geometric distortion in the InSAR result is masked to obtain the deformation information of the effective deformation region. The landslide identification analysis in the reservoir area has been carried out based on the effective deformation information in LOS direction. Thirteen landslides have been identified, and ten of them are new ones. A new large unstable area (New Zhenggang landslide) has been found near the Zhenggang landslide. The geological survey and displacement time series of the Zhenggang landslide reveals that it is in pull-type landslide mode, that is, due to the local instability of the leading edge of a landslide, the support of the trailing edge may be weakened, which may result in the landslide gradually developing backwards and upwards, and finally becoming a large landslide. The impact of peak rainfall and cumulative rainfall during the rainy season on landslide deformation has been verified in this paper. It indicates that the cumulative precipitation is the dominant factor causing the deformation of the landslide, and it shows that the landslide begins the deformation acceleration period about 12 days after the peak precipitation. The results have shown that the proposed visibility analysis method for extracting the effective deformation information of InSAR results can significantly improve landslide identification and analysis in complex terrain. [ABSTRACT FROM AUTHOR]

This paper aims to compare and develop the influence on different sample sizes and sample ratios when using machine learning (ML) models, i.e., support vector machine (SVM) and artificial neural network (ANN), to produce landslide susceptibility maps (LSMs) in Penang Island, Malaysia. At the same time, traditional statistical (TS) models are also considered to produce LSMs in this comparative research. The receiver operating characteristic (ROC) curve and recall metric are applied to evaluate the model's performance. Based on the evaluation criteria, the ML model outperforms the TS models and the ML models trained using the datasets with larger sample size give a better performance. ML models, especially SVM models, have better performance when training with balanced datasets as well as the datasets of more landslide sample data. Kruskal-Wallis test and Mann-Whitney U test are applied to test the significance. The results indicate that sample size and sample ratio are essential factors when considering ML models to produce LSMs. The LSMs produced in this research can provide valid and useful information to the local authorities for landslide mitigation and prediction. [ABSTRACT FROM AUTHOR]

Landslide displacement evolution is important for predicting landslide geological disasters. Because landslide displacement monitoring data are limited, in this paper we propose a novel model for predicting landslide displacement, namely the kernel grey model with fractional operators (FKGM). By combining the advantages of fractional modeling, kernel function methods and grey models, we derived the theoretical framework of FKGM. The parameters of FKGM were obtained using particle swarm optimization algorithm. Then, FKGM was applied in a case study of a landslide in Hubei, China. The engineering geological characteristics of the landslide were analyzed, and seven factors including rainfall and the rate of the reservoir water-level change were selected as inputs. The results show that the mean absolute percentage error and mean square error of FKGM are smaller than those of the least square support vector machine (LSSVM) and the classical grey prediction model—GM(1,1). The influence of the FKGM parameters was investigated. Our results indicate that FKGM can be applied to reliably predict large deformation of landslides. [ABSTRACT FROM AUTHOR]

A year after the establishment of the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR), the science and technology community (STC) endorsed in Geneva the UNISDR Science and Technology Roadmap to Support the Implementation of the SFDRR 2015–2030 (STR-SFDRR). Conducted actions by the International Consortium on Landslides (ICL) reflect priorities and challenges at different scales with regard to the progress of multi-sectoral partnerships, recognising the key role of the STC for the implementation of the SFDRR. Central to such endeavour are the Sendai Landslide Partnerships 2015–2025 and the new-fangled Kyoto Landslide Commitment 2020. While the former was conceived as a strategy for global promotion of understanding and reducing landslide disaster risk, the latter is directed to advocate for harmonic cohesiveness between the Sendai Landslide Partnerships 2015–2025, and the SFDRR, the 2030 Agenda Sustainable Development Goals, the New Urban Agenda and the Paris Climate Agreement. By encompassing the linkages of the contributions of the ICL community to the expected outcomes of the STR-SFDRR, this paper provides valuable input to foster the SFDRR, and provides concrete information on the ongoing ICL initiatives, actions and deliverables for strengthening partnerships and science-informed public policies to reduce landslide disaster risk and to advance Integrated Landslide Disaster Risk Management at different scales. [ABSTRACT FROM AUTHOR]

This paper presents details of the recently reactivated landslide in Wushan Town, Chongqing, China. The landslide was reactivated on July 17, 2019, by slope cutting, and thereafter, entered a state of imminent sliding. The landslide involved 4 million m3 of rock and soil masses, thereby threatening National Road G348 and the safety of 588 residents in 136 households in Xiping Village and over 1000 residents in the Jinke Community. Field investigations, drilling, and in situ monitoring were performed to determine the landslide deformation characteristics and reactivation mechanism. The results show that the regional abundant rainfall, formation lithology, and tectonic effects were responsible for the formation of the ancient Baiyangwan landslide. Moreover, the building load on the rear and middle parts increased the sliding force. Open excavation at the toe decreased the anti-sliding force and directly promoted landslide reactivation. In particular, the groundwater table rise caused by gully filling in recent years also played a key role in the reactivation of the ancient landslide. [ABSTRACT FROM AUTHOR]

This paper shows the results of a study aimed at quantitatively estimating—in terms of direct (repair) costs, at large scale (1:5000)—the slow-moving landslide risk to a road network assumed as undamaged as well as the consequences to the same network in damaged conditions. The newly conceived methodological approaches address some challenging tasks concerning (i) the hazard analysis, which is expressed in terms of probability of occurrence of slow-moving landslides with a given intensity level that, in turn, is established based on empirical fragility curves, and (ii) the consequence analysis, which brings to the generation of time-dependent vulnerability curves. Their applicability is successfully tested in a case study in the Campania region (southern Italy) for which both very high-resolution DInSAR data and information gathered from in situ surveys on the severity of damage sustained by the selected road sections are available. Benefits associated with the use of the obtained results in informed decision-making processes are finally discussed. [ABSTRACT FROM AUTHOR]

Landslide susceptibility map (LSM) provide useful tool for decision makers in hazard mitigation concerns. In the present paper, a novel hybrid block-based neural network model (HBNN) for the purpose of producing high-resolution LSM was developed. This hybrid approach was found through the mixture of expert modular structures and divide-and-conquer strategy incorporated with genetic algorithm (GA). The introduced HBNN then was applied on southern part of Guilan province (north of Iran) using 14 causative factors covering topographic and geomorphologic features, and geological and tectonical factors as well as hydrology, land data, and climate conditions. The landslide inventory map was provided using a synergy work from monitored events, interpretation of aerial photographs, and carried out geotechnical investigations in the area as well as field surveys. To insight, the predictability of proposed HBNN was compared with two developed models using multilayer perceptrons (MLPs) and generalized feed forward neural network (GFFN). The generated LSM was validated using receiver operating characteristic (ROC) curves, statistical error indices, and sensitivity and weight analyses as well as monitored landslides. Based on the compared metrics, HBNN with 86.52% and 90.15% in prediction and success rate as well as 89.36% for precision-recall curve demonstrated more consistent tool for future landslide susceptibility zonation. This implies on capability of developed HBNN in producing higher resolution and more reliable LSM for urban and land-use planners. [ABSTRACT FROM AUTHOR]

Landslides, collapses and cracks are the main types of geological hazards, which threaten the safety of human life and property at all times. In emergency surveying and mapping, it is time-consuming and laborious to use the method of field artificial investigation and recognition and using satellite image to identify ground hazards, there are some problems, such as time lag, low resolution, and difficult to select the map on demand. In this paper, a 10 cm per pixel resolution photogrammetry of a geological hazard -prone area of Taohuagou, Shanxi Province, China is carried out by DJ 4 UAV. The digital orthophoto model (DOM), digital surface model (DSM) and three-dimensional point cloud model (3 DPCM) are generated in this region. The method of visual interpretation of cracks based on DOM (as main) -3DPCM (as auxiliary) and landslide and collapse based on 3DPCM (as main) — DOM and DSM (as auxiliary) are proposed. Based on the low altitude remote sensing image of UAV, the shape characteristics, geological characteristics and distribution of the identified hazards are analyzed. The results show that using UAV low altitude remote sensing image, the method of combination of main and auxiliary data can quickly and accurately identify landslide, collapse and crack, the accuracy of crack identification is 93%, and the accuracy of landslide and collapse identification is 100%. It mainly occurs in silty clay and mudstone geology and is greatly affected by slope foot excavation. This study can play a great role in the recognition of sudden hazards by low altitude remote sensing images of UAV. [ABSTRACT FROM AUTHOR]

This paper provides newly found and profound insight into the initiation mechanism of the first Baige landslide occurred on the upper reaches of the Jinsha River in October 10, 2018. The detailed geological characteristics are interpreted by comprehensive field surveys in the past year, which indicate that the Baige landslide developed in the Jinsha River tectonic suture zone, and the active tectogenesis significantly contributes to broken stratigraphic structures and complex spatial distribution of lithologies. The initiation is considered to be long-term creep under the exogenic and endogenic integration, while the active tectogenesis and the influence of serpentinite and foliation, respectively, are the primary exogenic and endogenic factors. In addition, this creep process can be analyzed within three stages: evolution and formation of failure-prone geological structures, progressive deformation and fracturing, and shear failure of the locking section. The distribution and easily degraded behavior of the serpentinite are the fundamental, enabling the formation of failure-prone structures and also responsible for the subsequent deformation evolution. The foliation controls the toppling deformation-failure mode and direction. Furthermore, this catastrophic landslide further reminds us to pay attention to the landslides along the upper reaches of the Jinsha River, and our preliminary results indicate that the distribution characteristics of landslides significantly depend on the Jinsha River tectonic suture zone and the serpentinite mélange belt and thus are always associated with tectonically induced damage. [ABSTRACT FROM AUTHOR]