Search

Landslides are among the major environmental hazards with large-scale socio-economic and environmental impacts that jeopardize socio-economic wellbeing in mountainous regions. Landslides are due to the interaction of several complex factors such as local or regional geology, geomorphology, topography, and seismic motions. The goal of this study is to review published articles on causes and effects of landslides in Central Asia throughout 2000–2020. In line with this goal, we have collected (using Scopus database), reviewed, and analyzed 79 papers published during 2000–2020. Our results revealed an increasing number of landslide studies in Central Asia during the period under investigation, with authors from Belgium dominating in the published outcomes (28% of total), followed by authors from Central-Asian countries. After then, the paper analyses the mostly applied models and frequently identified driving conditions and triggers of landsliding, such as aspect, altitude, soil types, precipitation, earthquakes and human interventions. Geographic information system (GIS) and remote sensing (RS) had not commonly been used in the papers between 2000 and 2010, and they have progressively been applied in landslide studies in Central Asia in the last decade. According to our analysis, geotechnical, geophysical and statistical methods were preferably used for the landslide studies in Central Asia. [ABSTRACT FROM AUTHOR]

In this paper, the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology is adopted to monitor the Line of Sight (LOS) displacement of Fushun West Opencast Coal Mine (FWOCM) and its surrounding areas in northeast China using Sentinel-1 Synthetic Aperture Radar (SAR) images acquired from 2018 to 2022. The spatial-temporal evolution of urban subsidence and the south-slope landslide are both analyzed in detail. Comparison with ground measurements and cross-correlation analysis via cross wavelet transform with monthly precipitation data are also conducted, to analyze the influence factors of displacements in FWOCM. The monitoring results show that a subsidence basin appeared in the urban area near the eastern part of the north slope in 2018, with settlement center located at the intersection of E3000 and fault F1. The Qian Tai Shan (QTS) landslide on the south slope, which experienced rapid sliding during 2014 to 2016, presents seasonal deceleration and acceleration with precipitation, with the maximum displacement in vicinity of the Liushan paleochannel. The results of this paper have fully taken in account for the complications of large topographic relief, geological conditions, spatial distribution and temporal evolution characteristics of surface displacements in opencast mining area. The wide range and long time series dynamic monitoring of opencast mine are of great significance to ensure mine safety production and geological disaster prevention in the investigated mining area. [ABSTRACT FROM AUTHOR]

As the total mileage of the pipeline and the years of operation are increasing, the threat of landslide and corrosion defects to the safety of pipeline operation is also increasing. In this paper, based on the nonlinear elastoplastic theory, a pipe-soil coupling model is established to determine the maximum stress location of the pipeline containing corrosion defects under the action of landslide, on the basis of this location, the internal pressure of pipeline operation and the factors such as landslide width and landslide displacement are analyzed. Through multivariate linear regression, the equation of the maximum stress of the pipeline with the change of landslide width and displacement is fitted. The results show that in the landslide area, the operating pressure has less influence on the safety of the pipeline and the existence of corrosion defects will lead to sudden stress changes here, which will lead to the perforation of the pipeline; with the increase of the landslide width and landslide displacement, the plastic deformation area will appear in the middle of the pipeline, which means that the pipeline in this area is prone to bending, rupture and flattening and other failures, and the safety of the pipeline has a greater risk. In the non-slippery area, the maximum stress of the pipeline will increase with the increase of operating pressure, landslide width, and landslide displacement. Through multiple linear regression, the correlation coefficients of fitting the maximum stress of the pipeline with the expressions of landslide width and displacement are 0.959 and 0.996 respectively to prove the reliability of the formula. [ABSTRACT FROM AUTHOR]

The field stress path experienced by a soil element during rain-induced slope failures is reproduced by injecting water in an initially unsaturated specimen under constant total stress conditions. The accumulation of injected moisture near the base of triaxial specimens causes non-uniformity in the specimen leading to progressive failure. A new idea of using a helix shaped filter paper wrapped around the specimen’s periphery to uniformly distribute the injected water is presented. Water injection experiments were conducted and the behavior of specimens with and without the presence of filter paper was compared under constant shear stress. The use of helical filter paper ensured more uniform distribution of injected moisture, and reduced the discrepancy of moisture along specimen’s height by around 50%. Improvement in moisture uniformity of the specimen also reduced the possibilities of progressive failure during water injection experiments. The helical filter paper technique was found to hold a strong potential for the reproduction of rain-induced landslide conditions in laboratory triaxial experiments. [ABSTRACT FROM AUTHOR]

Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved. [ABSTRACT FROM AUTHOR]

Spatial planning decisions form the basis of territorial progress by enhancing the resilience and opportunities for local and regional development. Therefore, decisions made as a result of multidisciplinary studies based on GIS assessment of all involved factors can provide a real and up-to-date image of the analyzed territory. In this context, geomorphic processes are among the factors restricting development, affecting built-up areas, transport infrastructure, and economic activities. This paper assesses geomorphic processes at the level of Cluj County, Romania, which mainly consist of active landslides that directly impact the accessibility of communities and their degree of safety, while accelerated soil erosion severely affects the output of land used for agriculture. GIS technology and a semi-quantitative model for determining the landslide hazard were used to classify landslides across occurrence probability classes. This methodology was implemented in Romania through Government Decision no. 447/2003. The USLE model was used to determine the soil erosion. As a result, the territory of Cluj County, which is the study area of this paper, was entirely included in one of the classes of risk concerning active geomorphic processes. Another important aspect consisted of categorizing transport infrastructure according to risk classes. A population risk assessment was also performed, taking into account the degree of accessibility of the territorial emergency department in the event that such hazards and processes occur. These results form the basis of proposals to efficiently plan the county territory, adapting decisions to the present trends in the evolution. [ABSTRACT FROM AUTHOR]

This paper systematically reviews remote sensing technology and learning algorithms in exploring landslides. The work is categorized into four key components: (1) literature search characteristics, (2) geographical distribution and research publication trends, (3) progress of remote sensing and learning algorithms, and (4) application of remote sensing techniques and learning models for landslide susceptibility mapping, detections, prediction, inventory and deformation monitoring, assessment, and extraction and management. The literature selections were based on keyword searches using title/abstract and keywords from Web of Science and Scopus. A total of 186 research articles published between 2011 and 2024 were critically reviewed to provide answers to research questions related to the recent advances in the use of remote sensing technologies combined with artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms. The review revealed that these methods have high efficiency in landslide detection, prediction, monitoring, and hazard mapping. A few current issues were also identified and discussed. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using a time series short-duration memory network (LSTM). Initially, ICEEMDAN decomposes the landslide displacement time series into trend and periodic terms. SSA is then employed to denoise these components before fitting the trend term with LSTM. Pearson correlation analysis is utilized to identify characteristic factors within the LSTM model, followed by predictions using a multivariate LSTM model. The empirical results from the Baijiabao landslide in the Three Gorges Reservoir area demonstrate that the joint ICEEMDAN-SSA approach, when combined with LSTM modeling, outperforms the separate applications of SSA and ICEEMDAN, as well as other models such as RNN and SVM. Specifically, the ICEEMDAN-SSA-LSTM model achieves an RMSE of 6.472 mm and an MAE of 4.992 mm, which are considerably lower than those of the RNN model (19.945 mm and 15.343 mm, respectively) and the SVM model (16.584 mm and 11.748 mm, respectively). Additionally, the R2 value for the ICEEMDAN-SSA-LSTM model is 97.5%, significantly higher than the RNN model's 72.3% and the SVM model's 92.8%. By summing the predictions of the trend and periodic terms, the cumulative displacement prediction is obtained, indicating the superior accuracy of the ICEEMDAN-SSA-LSTM model. This model provides a new benchmark for precise landslide displacement prediction and contributes valuable insights to related research. [ABSTRACT FROM AUTHOR]

Due to the uncertainty in soil landslide failure mechanisms, lack of early warning systems for soil landslides and adoption of improper excavation configurations, soil landslides accidents triggered by highway excavation in Chinese mountainous areas generally require expensive remedial measures. This paper describes a soil landslide associated with excavation through integrating field reconnaissance and finite element method simulation. According to the obtained results, the adoption of toe excavation and the presence of a silty clay layer are the two main factors contributing to the failure of the soil landslide, and a strong negative correction was observed between the toe excavation and surface displacement and the safety factor of the investigated cut slope; therefore, a four-level early warning system for this excavationinduced soil landslide was established by employing toe excavation and surface displacement thresholds as the warning indicators. Lastly, a preferable excavation configuration was proposed to facilitate excavation designs in similar landslide-prone areas. [ABSTRACT FROM AUTHOR]

Landslides triggered by rainfall kill people worldwide, and frequent extreme events that are expected to be an effect of climate change could exacerbate this problem. This review aims to identify recent research, highlighting both the dynamics of landslide accidents and the characteristics of victims. From SCOPUS and WOS databases, using the PRISMA (preferred reporting items for systematic reviews and meta-analysis) approach, 25 articles written in English, published in the January 2010–March 2022 period and focused on landslide fatalities, were mined. The selected articles recognized a worldwide underestimation of landslide fatalities and analyzed landslide mortality from three perspectives, indicating the importance of this topic for a multidisciplinary research community. The papers focused on (a) fatal landslides and their geographic distribution, seasonality, trends, and relationships with socioeconomic indicators; (b) landslide fatalities and their behaviors and the dynamics of accidents; and (c) clinical causes of death or injury types, aiming to improve emergency rescue procedures. The gaps that emerged include (a) the insufficient reuse of valuable fatality databases; (b) the absence of simple take-home messages for citizens, practitioners, schoolteachers, and policymakers, aiming to set educational campaigns and adaptation measures; and (c) the lack of joint research projects between researchers working on landslides and doctors treating victims to provide complete research results that would be able to actually reduce landslide mortality. [ABSTRACT FROM AUTHOR]

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

The North Mountain landslide in Lijie Town has been in a long-term creeping deformation state and has experienced multiple landslide and debris flow disasters. Monitoring the surface deformation of landslide to grasp the surface deformation pattern of disaster body is a reliable basis for realizing early warning prediction of geological disaster. In this paper, a machine learning model is introduced to predict the relevant data, and a long and short-term memory network is used to predict the landslide deformation by monitoring the displacement data of North Mountain in Lijie, and the prediction results are compared with the actual data and analyzed. In this paper, root mean square error, mean absolute error, coefficient of determination and explainable variance are used to evaluate the prediction results, among which the coefficient of determination and explainable variance reach 0.99. It shows that the long short-term memory network used in this paper achieves good prediction performance in the prediction of landslide deformation in the North Mountain of Lijie. [ABSTRACT FROM AUTHOR]

The use of deep learning approaches to predict landslide displacement based on monitored time-series data is an effective method for the early-warning of landslides. Currently, most prediction models focus on the temporal correlation of displacements from a single monitoring point, ignoring the spatial influence of other monitoring points. To fully consider the spatiotemporal features of the displacement data, this paper develops three deep learning models based on graph convolution networks to spatiotemporally predict the landslide displacements of the Huanglianshu landslide. Specifically, we first establish a fully connected graph to represent the spatial relationships of all the deployed monitoring points. Second, we develop a temporal graph convolutional network-long short term memory (TGCN-LSTM) model and an Attention-TGCN model based on the temporal graph convolutional network-gate recurrent unit (TGCN-GRU) deep learning model and employ the three models to spatiotemporally predict displacements of the Huanglianshu landslide. The proposed spatiotemporal prediction models accurately predict the displacements at seven monitoring points, with a maximum R2 of 0.85 at the individual monitoring points. The comparative results show that the proposed Attention-TGCN model achieves the highest spatiotemporal prediction accuracy, and the accuracy of the Attention-TGCN model can further improve after considering the movement of the monitoring points. [ABSTRACT FROM AUTHOR]

A steel-wire wound reinforced thermoplastic pipe (SWW-RTP) has been widely utilized in many industrial areas, and a soil landslide is an inevitable hazardous extreme condition for the SWW-RTP as it is usually buried underground. It is imperative to study the mechanical failure behavior and the failure criterion of the SWW-RTP under the combination of internal pressure and soil landslide conditions, and this paper is the first study to investigate the topic. In this paper, groups of stress–strain curves of high-density polyethylene (HDPE) and steel wires were obtained by uniaxial tensile tests at different strain rates, with the help of a Digital Image Correlation device (DIC). A rate-dependent constitutive model was employed to represent the mechanical behavior of the HDPE and to help deduce the stress–strain curve of the HDPE under the required strain rate, estimated from the static simplification of the dynamic soil landslide. Afterwards, a finite element model of the SWW-RTP, embedded in a cubic of soil, was established with the software ABAQUS. The SWW-RTP model was composed of HDPE solid elements, embedded with steel-wire truss elements, and the soil was characterized with the extended Drucker–Prager model. A quartic polynomial displacement distribution was applied to the soil model to represent the soil landslide. Then, the mechanical response of the SWW-RTP was analyzed. It was found that the failure criterion of the HDPE yield was more suitable for the pipe subjected to internal pressure and soil landslide conditions, instead of the steel-wire strength failure criterion always used in traditional research on the SWW-RTP. Further, the influence of landslide width, internal pressure and steel-wire number were discussed. The larger the width of the landslide area, the gentler the deformation of the pipeline; this resulted in an increase in the maximum landslide and a decrease in the maximum curvature with the width of the landslide area. The relatively high internal pressure was beneficial to the safety of the SWW-RTP under landslide, because the internal pressure could increase the stiffness of the pipeline. The number of steel wires had a limited influence on the maximum landslide required for the SWW-RTP's failure. This work can be useful for the design and safe assessment of the SWW-RTP under internal pressure and soil landslide conditions. [ABSTRACT FROM AUTHOR]

In recent years, RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research. In order to systematically understand their application situation, this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods. Then, this article focuses on sub domain analysis from four aspects: landslide detection and monitoring, prediction models, sensitivity mapping, and risk assessment. The study found that the number of literature in this field has steadily increased and is expected to continue to rise. This literature review has attracted widespread attention from the academic community, but it is challenging to meet research needs. Frequent and effective cooperation between countries, institutions, and authors is very beneficial for promoting progress in this field. The future development direction is a new intelligent hybrid model that integrates multiple research methods. This study can provide researchers in this field with the core research force, hot topic evolution, and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations'sustainable development goals. [ABSTRACT FROM AUTHOR]

Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 "Earth observation for seismic hazard assessment and landslide early warning system" are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a digital model to run an evacuation simulation that reflects the road network blockage caused by the landslide and river flooding damage in Marumori-machi, Miyagi Prefecture, which was severely damaged by Typhoon No. 19 in 2019. In particular, we propose an evacuation agent simulation model that can be extended in the future to scenarios related to disaster response decisions, education, and awareness on the part of residents and can reproduce the evacuation agent situation of a real disaster. The method adjusts a set of parameters of vehicles and pedestrian agents to reproduce the evacuation situation. Then, using the parameter set, we perform the agent simulations under different scenarios varying the time of disaster occurrence and evacuation. Finally, we evaluate the changes in the number of people who have completed their evacuation, the number of victims, etc. The results of the scenarios with different disaster occurrence times showed that the final evacuation rate situation improved by at least 1% (about 170 people who completed evacuation) during daylight time compared to nighttime. The relationship between sunset time and the start of evacuation was qualitatively and quantitatively demonstrated to be supported. It was also confirmed that the evacuation situation did not change much with the time of the evacuation announcement. These results show trends limited to the present study's disasters and scenarios and do not necessarily provide generalized findings for disaster response. However, the results indicate that applying the proposed methodology to a greater number of disasters and scenario conditions could lead to better analysis and optimization of disaster response. Interviews with government disaster management officials in the target areas suggest that confirming the effectiveness of disaster response while visualizing the distribution of disaster risk in the areas from a bird's eye view, as in this study, could enhance existing response plans. This approach may also present information comprehensibly for staff and residents who did not experience the disaster firsthand, simulating the experience for better understanding. [ABSTRACT FROM AUTHOR]

Land degradation and soil erosion, intensified by frequent intense hydro-meteorological events, pose significant threats to ecological processes. In response to the environmental challenges, there is a growing emphasis on employing Nature-Based Solutions (NBS), such as Soil and Water Bioengineering (SWBE) techniques, which promote a sustainable approach and materials for the restoration of natural areas damaged by climate events, unlike traditional "grey" engineering works. However, the effective implementation of SWBE interventions requires a multidisciplinary monitoring approach, considering engineering, geological, ecological, biological, and landscape aspects. The success of these interventions depends on evaluating both short-term stabilities provided by the non-living supporting structure and the long-term development of vegetation introduced during the work. Monitoring should regard structural integrity assessments, vegetation evolution studies, and analyses of root system efficiency (distribution, mechanical characteristics, etc.). This study wants to fill the research gap in SWBE management by proposing a comparison of two study techniques for a root system development evaluation, within a multi-approach methodology for the assessment of these interventions in terms of soil stability and natural evolution. The paper provides insights into geotechnical analysis within a shallow landslide, comparing two different methods for the evaluation of root system evolution. Direct methods (RAR) and indirect methods (ERT) were used for root development monitoring and then compared. Vegetation development was assessed by NDVI parameter by analysing Landsat satellite images. An overall analysis of the data obtained from monitoring the study area shows good plant development, thanks to the SWBE intervention, which in addition to the slope stability effect contributes to better water regulation and initiates a natural ecological succession. The findings contribute to advancing the understanding of the effectiveness of SWBE techniques, offering valuable information for future bioengineering projects and environmental conservation efforts, and promoting them as sustainable techniques for natural recovery. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)