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Abstract In mountain areas landslides many times endanger safety of transport infrastructures, and these must be stabilized with retaining wall structures. In this paper the validation of a new composite as a backfill material for landslide stabilization with a large scale demo retaining wall is presented. The new composite was made from residues of paper industry, which uses for its production deinking process. New composite was validated with the laboratory tests, construction of small demo sites and at the end with a large demo retaining wall structure with a length of 50 m. It was concluded that the paper sludge ash and the paper sludge are in proportion 70:30, compacted on the optimal water content and maximum dry density, reached sufficient uniaxial compressive and shear strength. However, the composite's hydration processes required the definition of an optimal time between the composite mixing and installation. In 2019, the retaining wall structure from the new composite was successfully built. The large demo structure is an example of the knowledge transfer from the laboratory to the construction site, in which composite and installing technology could be verified.

Landslide susceptibility zoning is necessary for landslide risk management. This study aims to conduct the landslide susceptibility evaluation based on a model coupled with information value (IV) and logistic regression (LR) for Badong County in Hubei Province, China. Through the screening of landslide predisposing factors based on correlation analysis, a spatial database including 11 landslide factors and 588 historical landslides was constructed in ArcGIS. The IV, LR and their coupled model were then developed. To validate the accuracy of the three models, the receiver operating characteristic curves (ROC) and the landslide density curves were correspondingly created. The results showed that the areas under the receiver operating characteristic curve (AUCs) of the three models were 0.758, 0.786 and 0.818, respectively. Moreover, the landslide density increased exponentially with the landslide susceptibility, but the coupled model exhibited a higher growth rate among the three models, indicating good performance of the proposed model in landslide susceptibility evaluation. The landslide susceptibility map generated by the coupled model demonstrated that the high and very high landslide susceptibility area mainly concentrated along rivers and roads. Furthermore, by counting the landslide numbers and analyzing the landslide susceptibility within each town in Badong County, it was discovered that Yanduhe, Xinling, Dongrangkou and Guandukou were the main landslide-prone areas. This research will contribute to landslide prevention and mitigation and serve as a reference for other areas., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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]

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied; nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016-2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Shape optimization of this hydrofoil maximized its lift-to-drag ratio and minimized its pressure coefficient, thereby increasing the turbines power output power and improving its cavitation characteristics. A waiting time is required to observe whether the applied supersaturation is sufficient to initiate bubble nucleation. When the supersaturation is not sufficient to cause bubble nucleation, some or all of the supersaturated gas may diffuse out from the liquid through the gas-liquid interface before further reducing the pressure in order to increase the supersaturation. The aim of this first I Eng i Special Issue is to collect experimental and theoretical research relating to engineering science and technology. [Extracted from the article]

The increased occurrence and severity of natural disasters, such as landslides, have impacted the stability of phyllite rock slopes in the complex geological regions of Western China. This situation presents significant challenges for infrastructure development in the area. This study investigates the upper span bridgehead slope of Guang-Gansu expressway K550 + 031 as a case study to analyze the sliding failure mechanism of thousand rock slopes in the seismic fault zone and the supporting structure failure through field investigation and exploration. The analysis shows that the slope's rock mass is extensively fractured, primarily influenced by the Qingchuan fault zone. This geological activity leads to slope instability, worsened by seasonal rainfall. The phyllite undergoes alternating dry and wet cycles, weakening its mechanical strength, forming cracks, and accelerating slope displacement, subsidence, and cracking. This results in front slope instability, followed by gradual backward and step-by-step traction sliding deformation on both sides. The geological structure and seasonal rainfall damage the original bolt-grid beam-supporting structure. To address this issue, an anti-slide pile combined with a grid beam treatment method is proposed, and its effectiveness is verified through deep displacement monitoring. This study emphasizes the significance of integrating geological structure and seasonal rainfall impacts into infrastructure design within complex geological areas, ensuring slope and supporting structure stability., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A World Natural Heritage Site, Jiuzhaigou, is the first nature reserve in China whose primary purpose is to protect natural scenery. On August 8, 2017, a M s 7.0 earthquake caused many unstable slopes in Jiuzhaigou, Sichuan Province, China. In the extreme storm conditions that follow, the unstable slopes tend to develop into potential landslides, which can cause many casualties and property losses in scenic areas. Sentinel-1A ascending orbit data were obtained in this paper to establish a SAR database. The large-scale deformation rate map of the study area was obtained using a small baseline set InSAR technology. The potential landslides in the deformation area are preliminarily confirmed with remote sensing interpretation. The field verification is further carried out by studying the deformation information of the characteristic points on the potential landslides. The results show that 13 deformation zones were preliminarily identified, and three typical deformation zones were selected for coupling verification and identified as potential landslides. At the same time, further analysis shows that the four potential landslides have been in continuous linear deformation for a long time since the earthquake, posing a severe threat to the safety of local people's lives and property. The research results provide a reference for the early identification and warning of potential landslides in earthquake-prone regions., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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The disastrous earthquake of 1693 AD caused over 60,000 causalities and the total destruction of several villages and towns in south-eastern Sicily. Immediately after the earthquake, a tsunami struck the Ionian coasts of Sicily and the Messina Strait and was probably recorded even in the Aeolian Islands and Malta. Over the last few decades, the event has been much debated regarding the location of the seismogenic source and the possible cause of the associated tsunami. The marine event has been related to both a submarine landslide and a coseismic displacement at the seafloor. To better define the most reliable sources and dynamics of the tsunami, we couple high-resolution marine seismic survey data with hydrodynamic modelling to simulate various scenarios of tsunami generation and propagation. Results from the simulations are compared with geomorphological evidence of past tsunami impacts, described in previous work along the coast of south-eastern Sicily, and within historical chronicles and reports. The most reliable scenario considers the 1693 event composed by two different tsunami waves: a first wave generated by the coseismic fault displacement at the seafloor and a second wave generated by a submarine landslide, triggered by the earthquake shaking. Tsunami modelling shows that a simultaneous movement between fault displacement and submarine mass movement could determine a destructive interference on the tsunami waves, resulting in a reduction in wave height. For this reason, the second tsunami wave probably occurred with a maximum delay of few minutes after the one generated by the earthquake and induced a greater flooding. The double-source model could explain the observation because in the course of other destructive earthquakes in south-eastern Sicily, such as that of 1169 AD, the associated tsunami caused less damages. This implies the need to better map, define and assess the hazard responsible for this type of tsunami events., (© 2022. The Author(s).)

Landslides are a kind of geological hazard with great destructive potential. When a landslide event occurs, a reliable landslide segmentation method is important for assessing the extent of the disaster and preventing secondary disasters. Although deep learning methods have been applied to improve the efficiency of landslide segmentation, there are still some problems that need to be solved, such as the poor segmentation due to the similarity between old landslide areas and the background features and missed detections of small-scale landslides. To tackle these challenges, a proposed high-resolution semantic segmentation algorithm for landslide scenes enhances the accuracy of landslide segmentation and addresses the challenge of missed detections in small-scale landslides. The network is based on the high-resolution network (HR-Net), which effectively integrates the efficient channel attention mechanism (efficient channel attention, ECA) into the network to enhance the representation quality of the feature maps. Moreover, the primary backbone of the high-resolution network is further enhanced to extract more profound semantic information. To improve the network's ability to perceive small-scale landslides, atrous spatial pyramid pooling (ASPP) with ECA modules is introduced. Furthermore, to address the issues arising from inadequate training and reduced accuracy due to the unequal distribution of positive and negative samples, the network employs a combined loss function. This combined loss function effectively supervises the training of the network. Finally, the paper enhances the Loess Plateau landslide dataset using a fractional-order-based image enhancement approach and conducts experimental comparisons on this enriched dataset to evaluate the enhanced network's performance. The experimental findings show that the proposed methodology achieves higher accuracy in segmentation performance compared to other networks. [ABSTRACT FROM AUTHOR]

Among the natural disasters on the planet, especially in the mountainous and foothill regions, it is widespread erosive-debris flow events, which have the most significant environmental and economic damage to humanity. Georgia is no exception. This paper aims to develop a new methodology to calculate the predictive quantities of debris flow, essential for implementing anti-debris flow measures. Based on the available data and various calculations, a completely new empirical approach has been adopted to calculate predictive quantities of debris flow spent, predicting debris flow spent in the mountains and foothill regions of Georgia. The suggested methodology reflects the physics of debris-flow processes at a very high level and can be applied to calculate debris flow in various world regions., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

With the development of computer technology, landslide recognition based on machine learning methods has been widely applied in geological disaster management and research. However, in landslide identification, the problems of an insufficient number of samples and an imbalance of samples are often ignored; that is, landslide samples are much smaller than non-landslide samples. In order to solve this problem, taking the main urban area of Lanzhou City as an example, this paper proposes to construct a semi-supervised generated countermeasure network (SSGAN) model, which aims to achieve high performance with a limited number of labeled samples for precise landslide identification, and to help prevent and reduce the harm caused by disasters. In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, the study constructs three sets of samples to represent landslide features, including a landslide influencing factor sample set, a Sentinel-2A optical remote sensing sample set, a joint influencing factor and Sentinel-2A sample set. The three kinds of sample sets are transferred to SSGAN for training to form a comparative study. The results show that the joint sample set has excellent feature results in discriminator and generator. Through the experimental comparison, the model proposed in this paper is compared with the model without semi-supervised generated confrontation training. The experimental results show that the proposed method is better than the unsupervised adversarial learning model in terms of accuracy, F1 score, Kappa coefficient, and MIoU. A total of 160 landslides have been identified in the study area, with a total area of 10.328 km2, with an accuracy rate of 83%. Therefore, the generated results are accurate and reliable, and show that SSGAN can better distinguish landslides from non-landslides in an image, under the condition of obtaining a large number of unmarked environmental features; enhance the effect of landslide classification in complex geographical environment; and then put forward effective suggestions for the prevention and control of landslides and geological disasters in the main urban area of Lanzhou. [ABSTRACT FROM AUTHOR]

Accumulation landslides are prone to occur during the continuous infiltration of heavy rainfall, which seriously threatens the lives and property safety of local residents. In this paper, based on the Green-Ampt (GA) infiltration model, a new slope rainfall infiltration function is derived by combining the effect of air resistance and lateral seepage of saturated zone. Considering that when the soil layer continues to infiltrate after the saturation zone is formed, the air involvement cannot be discharged in time, which delays the infiltration process. Therefore, the influence of air resistance factor in soil pores is added. According to the infiltration characteristics of finite long slope, the lateral seepage of saturated zone is introduced, which makes up for the deficiency that GA model is only applicable to infinite long slope. Finally, based on the seepage characteristics of the previous analysis, the overall shear strength criterion is used to evaluate the stability of the slope. The results show that the safety factor decreases slowly with the increase of size and is inversely correlated with the slope angle and initial moisture content. The time of infiltration at the same depth increases with the increase of size and slope angle, and is inversely correlated with the initial moisture content, but is less affected by rainfall intensity. By comparing with the results of experimental data and other methods, the results of the proposed method are more consistent with the experimental results than other methods. [ABSTRACT FROM AUTHOR]

To address the landslide recognition problem in remote sensing images, this paper designs a visual transformer network model based on DEM (digital elevation model) feature enhancement, which is experimentally validated on the Bijie landslide dataset and Landslide4Sense2022 dataset. The lion optimizer is used during training. The results show that 98.49% accuracy and 97.24% F1 score are achieved on Bijie dataset, and 88.22% accuracy and 90.16% F1 score on Landslide4Sense2022 dataset, which is a significant improvement in landslide recognition compared with other mainstream network models. Therefore, it can be found that this paper's method is effective in the recognition of landslide from remote sensing images. Firstly, the swin transformer network model was successfully applied to remote sensing landslide image classification by means of transfer learning. Secondly, an information enhancement approach based on DEM features was designed. [ABSTRACT FROM AUTHOR]

Mr. Peternel, the municipal engineer, I did not i mention the prehistoric landslide in his 1973 memorandum because he had no knowledge of this landslide. As presented in considerable detail in the paper, the municipality I did not cause i the subject landslide and the homeowner was unable to prove in court that the municipality caused this landslide. 2 was plotted in 2006 by the writer from the map of Davies ([1]) along with observations on 1937-1986 aerial photographs and in the field; the 1973 landslide described by then municipal engineer Arthur Peternel and observed by the writer on 1973 aerial photographs corresponded to the portion of the recent landslide behind Houses 4347, 4353, and 4359; and this 1973 landslide grew from 1973 to 2005 to extend behind House 4363, as shown in the outline of the recent landslide in Fig. As noted previously and stated in the paper, the 1973 landslide described by Mr. Peternel and observed by the writer on the 1973 aerial photographs corresponded to the portion of the recent landslide behind Houses 4347, 4353, and 4359, and this 1973 landslide grew from 1973 to 2005 to extend behind House 4363 as shown in the outline of the recent landslide in Fig. [Extracted from the article]

This document is an editorial from the journal Frontiers in Earth Science titled "Prevention, Mitigation, and Relief of Compound and Chained Natural Hazards." It discusses the increasing frequency of extreme natural disasters due to global climate warming and frequent earthquakes, which pose significant threats to human life and property. The editorial highlights the importance of preventing, mitigating, and relieving compound and chained natural hazards, and the role of technological advancements in addressing these hazards. The document provides an overview of nine published papers that focus on earthquakes, geological hazards, earthquake-triggered landslides, and landslide susceptibility. It concludes by emphasizing the need for continued research on comprehensive natural hazards and disaster chains, beyond earthquakes and geological disasters, such as meteorological events, floods, droughts, wildfires, and tsunamis. [Extracted from the article]

Le'an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town's terrain is complex, and its geological environment is fragile. Multiple phases of tectonic movements have resulted in numerous cracks and faults, making the area prone to landslides, debris flows, and other disasters. Additionally, heavy rainfall and fluctuating groundwater levels further exacerbate the instability of the mountains. Human activities, such as overdevelopment and deforestation, have significantly increased the risk of geological disasters. Currently, the methods for landslide prediction in Le'an Town are limited; traditional techniques cannot provide precise forecasts, and the study area is largely covered by tall vegetation. Therefore, this paper proposes a method that combines SBAS-InSAR technology with dynamic changes in land use and hydrological conditions. SBAS-InSAR technology is used to obtain surface deformation information, while land-use changes and hydrological condition data are incorporated to analyze the dynamic characteristics and potential influencing factors of landslide areas. The innovation of this method lies in its high-precision surface deformation monitoring capability and the integration of multi-source data, which can more comprehensively reveal the geological environmental characteristics of the study area, thereby achieving accurate predictions of landslide development. The study results indicate that the annual subsidence rate in most deformation areas of Le'an Town ranges from −10 to 0 mm, indicating slow subsidence. In some areas, the subsidence rate exceeds −50 mm per year, showing significant slope aspect differences, reflecting the combined effects of geological structures, climatic conditions, and human activities. It is evident that land-use changes and hydrological conditions have a significant impact on the occurrence and development of landslides. Therefore, by utilizing SBAS-InSAR technology and cross-verifying it with other techniques, the consistency of identified landslide deformation areas can be enhanced, thereby improving results. This method provides a scientific basis for the monitoring and early warning of landslide disasters and has important practical application value. [ABSTRACT FROM AUTHOR]

A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

In order to scientifically and rationally evaluate the susceptibility to landslide hazards in Tekes County, Yili State. This paper takes Tekes County in Xinjiang as an example, on the basis of a comprehensive analysis of the regional geological environment conditions and the distribution pattern and formation conditions of geological disasters, using the data of geological disaster points (landslide center points), and through the correlation matrix calculation of the evaluation factors, the nine evaluation factors with larger absolute values of correlation coefficients were determined to construct the evaluation system of the susceptibility to landslide geological hazards in Tekesi County. Combining the information quantity method and the entropy value method, using the weights determined by the entropy value method, the information quantity method is used to calculate the information quantity value of each factor within the factor, calculate the susceptibility index of landslide geological disasters within the territory of Tekes County, and then carry out the landslide susceptibility evaluation. The susceptibility of landslide disasters was evaluated by ArcGIS. The results show that the landslide disaster susceptibility level in Tekes County can be divided into four levels: high susceptibility, medium susceptibility, low susceptibility, and not susceptible, with areas of 491.3276 km2, 1181.5171 km2, 1674.7609 km2 and 5295.2976 km2 accounting for 5.68%, 13.67%, 19.38% and 61.27% of the total area of Tex County, respectively. The AUC number obtained by the success curve method (ROC) is 0.8736, reflecting the evaluation accuracy of 87.36%, indicating that the model method used in this paper is effective. The results are expected to provide practical data support for landslide disaster control in Tekes County and provide a reference for geological disaster monitoring, early warning and engineering prevention and control deployment in Yili Valley. [ABSTRACT FROM AUTHOR]

To overcome the reliance on large samples and high-quality data in existing evaluation methods, while also improving evaluation efficiency and accuracy, this paper proposes a method for rapid landslide hazard assessment. This method utilizes existing research findings and specific analytical techniques for the study area to conduct rapid assessments. Taking the landslide in Yongxing Town, Mingshan Area, Ya'an City, Sichuan Province as an example, the Analytic Hierarchy Process (AHP) is combined with the Information Value (IV) method, Certainty Factor (CF) method, and Frequency Ratio (FR) method from previous studies, The AHP-IV and AHP-FR methods assess the study area as a moderately hazardous zone, while the AHP-CF method assesses it as a slightly hazardous zone. Affected by the strong 2013 Lushan earthquake, the landslide in the study area caused permanent damage. Field investigation results show that the landslide hazard in the study area is moderate, and the AHP-IV and AHP-FR methods are more consistent with the actual field results. The AHP-CF method, due to not considering the water system factor and having certain errors in its discrimination method, leans towards a safer assessment, The results of the three evaluation methods are somewhat consistent. [ABSTRACT FROM AUTHOR]

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage. [ABSTRACT FROM AUTHOR]

The suddenness of landslide disasters often causes significant loss of life and property. Accurate assessment of landslide disaster susceptibility is of great significance in enhancing the ability of accurate disaster prevention. To address the problems of strong subjectivity in the selection of assessment indicators and low efficiency of the assessment process caused by the insufficient application of a priori knowledge in landslide susceptibility assessment, in this paper, we propose a novel landslide susceptibility assessment framework by combing domain knowledge graph and machine learning algorithms. Firstly, we combine unstructured data, extract priori knowledge based on the Unified Structure Generation for Universal Information Extraction Pre-trained model (UIE) fine-tuned with a small amount of labeled data to construct a landslide susceptibility knowledge graph. We use Paired Relation Vectors (PairRE) to characterize the knowledge graph, then construct a target area characterization factor recommendation model by calculating spatial correlation, attribute similarity, Term Frequency–Inverse Document Frequency (TF-IDF) metrics. We select the optimal model and optimal feature combination among six typical machine learning (ML) models to construct interpretable landslide disaster susceptibility assessment mapping. Experimental validation and analysis are carried out on the three gorges area (TGA), and the results show the effectiveness of the feature factors recommended by the knowledge graph characterization learning, with the overall accuracy of the model after adding associated disaster factors reaching 87.2%. The methodology proposed in this research is a better contribution to the knowledge and data-driven assessment of landslide disaster susceptibility. [ABSTRACT FROM AUTHOR]

This paper describes a simple methodology to calculate the two-dimensional seepage across an infinite unsaturated slope using models of one-dimensional infiltration through horizontal ground. The methodology decomposes the seepage across the infinite slope into antisymmetric and symmetric parts, whose respective solutions are combined to calculate the actual flow regime. The antisymmetric solution is trivial and does not even require integration of the governing continuity equation, while the symmetric solution, albeit non-trivial, reduces to the case of one-dimensional flow through horizontal ground, for which solutions already exist. The methodology is generally applicable to the calculation of distinct seepage regimes across unsaturated slopes with different hydraulic properties under both stationary and transient conditions. The paper also defines the gradient of the piezometric head parallel to the slope, which is the Neumann boundary condition to be imposed on slope sections perpendicular to the ground surface. The rigorous definition of this gradient overcomes the need of imposing arbitrary boundary conditions in finite-element models. Finally, the paper demonstrates that all infiltrated water crosses the slope along the shortest path – namely, the path normal to the surface – while the flow parallel to the slope is entirely fed by an upstream source at infinite distance. [ABSTRACT FROM AUTHOR]

Accurately predicting landslide displacement is essential for reducing and managing associated risks. To address the challenges of both underdecomposition and over-decomposition in landslide displacement analysis, as well as the low predictive accuracy of individual models, this paper proposes a novel prediction model based on time series theory. This model integrates a Convolutional Neural Network (CNN) with a Bidirectional Long-Short Term Memory network (BiLSTM) and an attention mechanism to form a comprehensive CNN-BiLSTM-Attention model. It harnesses the feature extraction capabilities of CNN, the bidirectional data mining strength of BiLSTM, and the focus-enhancing properties of the attention mechanism to enhance landslide displacement predictions. Furthermore, this paper proposes utilizing the Variational Mode Decomposition (VMD) method to decompose both landslide displacement and its influencing factors. The VMD algorithm's parameters are optimized through the Sparrow Search Algorithm (SSA), which effectively minimizes the influence of subjective bias while maintaining the integrity of the decomposition. A fusion of the Maximal Information Coefficient (MIC) and Grey Relational Analysis (GRA) is then employed to identify the critical influencing factors. The selected sequence of factors that conforms to the criteria is used as the input variable for displacement prediction via the CNN-BiLSTM-Attention model. The cumulative displacement prediction is derived by aggregating the results from each sequence. The study reveals that the SSA-VMD-CNN-BiLSTM-Attention model introduced herein achieves superior predictive accuracy for both periodic and random term displacements than individual models. This advancement provides a dependable benchmark for forecasting displacement in similar landslide scenarios. [ABSTRACT FROM AUTHOR]

Global warming is causing unprecedented changes to permafrost regions with amplified effects in the Arctic through a phenomenon known as Arctic amplification. This intensified climate warming thaws both the discontinuous and continuous permafrost resulting in changes in the mechanical properties of the soils found in these regions. Since permafrost regions constitute nearly 24% of the Northern Hemisphere, understanding the strength of soils in thawed conditions is essential to analyze the stability of existing structures, and to design safer and more economical infrastructure in these regions. Specifically, thawing of the permafrost is causing considerable reductions in its strength of soils, which may lead to massive landslides, foundation failures, and so forth. Since frozen soil is a multiphase structure that consists of soil particles, unfrozen water, ice, and air, each constituent will influence the mechanical properties. This paper reviews the current state of knowledge of the impact of temperature, volumetric ice content, unfrozen water content, and frozen density on the compressive strength, peak shear strength, residual shear strength, undrained shear strength, and tensile strength of soils. The undrained shear strength of soil is said to have a linear correlation with temperature. In addition, the undrained cohesion of soil was found to depend on the temperature, whereas the undrained friction angle of soil was significantly influenced by volumetric ice content. An increase in the volumetric ice content up to 80% to 90% will cause a reduction in the peak and residual deviatoric stresses. In addition, an increase in volumetric ice content resulted in an increase in the compressive strength of the soil. The tensile and compressive strengths were found to be functions of the unfrozen water content. Global warming is causing the temperature of the permafrost, which is permanently frozen ground, to rise. This paper provides valuable insights into the impact of the changes in this ambient temperature on the strength of frozen soils in permafrost regions for a wide range of applications. Such insights are crucial for the design of resilient and stable infrastructure, such as foundations, embankments, and retaining walls, in which consideration of the reduced strength of thawed soils due to climate change will be necessary. In addition, the knowledge will allow for better management of vulnerable areas prone to landslides and erosion caused by the weakened soil strength permitting the implementation of mitigation measures before lives are lost and costly economic damages are incurred. Finally, this information will aid in early warning systems, emergency planning, and decision making to minimize the impact of hazards on human settlements and infrastructure. In this paper, a review of the current state of knowledge regarding the strength of frozen soils and the associated fluctuations in these strengths because of a rise in temperature are presented. Guidelines on the best practices for sample preparation and testing along with correlations to estimate various strength parameters are also provided. [ABSTRACT FROM AUTHOR]

Introduction: Reservoir landslides undergo large deformations during the early stages of impoundment and maintain long-term persistent deformations during the operational period of the reservoir. The management of reservoir landslides mostly focuses on the early identification, risk assessment during the large deformations, and long-sequence monitoring during long-term persistent deformations, which requires sufficient continuity and integrity of the landslide monitoring data. Methods: Taking the Wulipo (WLP) landslide in Baihetan Reservoir as example, this paper proposes a reservoir landslide monitoring method that integrates field survey, unmanned aerial vehicle (UAV) photogrammetry and global navigation satellite system (GNSS) monitoring, which can effectively eliminate the practical monitoring gaps between multiple monitoring methods and improve the continuity and completeness of monitoring data. Results and discussion: First, this study determined the initiation time of the landslide through the field investigation and collected five period of UAV data to analyze the overall displacement vector of the WLP landslide using sub-pixel offset tracking (SPOT). On the basis of the above data, we compensated for the missing data in GNSS system due to the practical monitoring vacancies by combining the field survey and the landslide-water level relationship. Based on these monitoring data, this paper points out that the WLP landslide is a buoyancy-driven landslide, and whether or not accelerated deformation will occur is related to the maximum reservoir water level. Finally, this study analyzed and discussed the applicability of UAV photogrammetry for reservoir landslide monitoring in the absence of ground control points (GCPs), and concluded that this method can be quickly and flexibly applied to the stage of large deformation of reservoir landslides. [ABSTRACT FROM AUTHOR]

Sichuan-Tibet Railway spans several watersheds such as Jinsha River and Yalong River, and Potential landslides are frequent along the route, which poses serious hazards to the normal construction and operation of the railroad. The traditional time-series InSAR technology is limited by the number of images and other restrictions, and has a long solution time, making it difficult to obtain information on short-term occurrence of deformation and unable to perform wide-area potential landslides monitor quickly. In this paper, taking a geological hazard-prone area in the Jinsha River basin (or a section of the Sichuan-Tibet line) as an example, based on the Sentinel-1 satellite SAR data provided by the Copernicus program of ESA, the model corrects the interferometric superposition deformation results obtained from a small number of SAR images (less than 7), decodes the corrected rate results, and identifies a total of 13 areas where deformation obviously occurs A total of 13 typical areas with significant deformation were identified. The identified typical areas were time-series solved and their deformation was traced. The method provides a new idea for identification and monitor of Potential landslides in a wide area and further promotes the development of disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Outburst floods can affect the survival adaptability of fish. Although the survival adaptability of many fish species under low steady-flow conditions has been studied, research on the survival adaptability of fish species under large outburst flood conditions is lacking. This paper takes the 2018 Baige landslide dam as an example. A breach model was developed to calculate the outburst discharge of the landslide dam. The outburst flood hydrograph is simulated with the breach model, which shows that the difference between the peak discharge of the dam break simulation results and the measured data is 0.13x104 m³/s. In addition, the simulated hydrographs are the same as the measured hydrographs. Furthermore, a two-dimensional fish habitat model was used to analyse the adaptability of Schizothorax to survival during the breaching process. For the survival adaptability of Schizothorax, we observed that as the flow rate increased the weighted usable area (WUA) decreased, which indicated a decrease in the adaptability of Schizothorax survival. In contrast, as the flow rate decreased and the WUA increased, the survival adaptability of Schizothorax improved. In addition, the WUA of Schizothorax changed with the substrate of the riverbed; the smaller the channel suitability index (CSI) the greater the WUA. This study revealed the impact of outburst floods triggered by landslide dam failure on the survival adaptability of Schizothorax, and a method for assessing the impact of outburst floods on fish habitat adaptability is provided. [ABSTRACT FROM AUTHOR]

In the context of natural hazard risk quantification and modeling of hazard interactions, some literature separates "Level I" (or occurrence) interactions from "Level II" (or consequence) interactions. The Level I interactions occur inherently due to the nature of the hazards, independently of the presence of physical assets. In such cases, one hazard event triggers or modifies the occurrence of another (e.g., flooding due to heavy rain, liquefaction and landslides triggered by an earthquake), thus creating a dependency between the features characterizing such hazard events. They differ from Level II interactions, which instead occur through impacts/consequences on physical assets/components and systems (e.g., accumulation of physical damage or social impacts due to earthquake sequences, landslides due to the earthquake-induced collapse of a retaining structure). Multi-hazard life cycle consequence (LCCon) analysis aims to quantify the consequences (e.g., repair costs, downtime, casualty rates) throughout a system's service life and should account for both Level I and II interactions. The available literature generally considers Level I interactions – the focus of this study – mainly defining relevant taxonomies, often qualitatively, without providing a computational framework to simulate a sequence of hazard events incorporating the identified interrelations among them. This paper addresses this gap, proposing modeling approaches associated with different types of Level I interactions. It describes a simulation-based method for generating multi-hazard event sets (i.e., a sequence of hazard events and associated features throughout the system's life cycle) based on the theory of competing Poisson processes. The proposed approach incorporates the different types of interactions in a sequential Monte Carlo sampling method. The method outputs multi-hazard event sets that can be integrated into LCCon frameworks to quantify interacting hazard consequences. An application incorporating several hazard interactions is presented to illustrate the potential of the proposed method. [ABSTRACT FROM AUTHOR]

The largest and the deepest landslides in Serbia occurred on the right valley side of the Danube. General conclusions about landslides along the Danube were obtained on the basis of their comprehensive, detailed investigations: the Sloboda bridge in Novi Sad, the Beška bridge, the large settlement of Bocke, and the high-speed railway viaduct in Čortanovci. These areas are actually large, unstable slopes with deep and shallow landslides. Deep landslides consist of several sliding blocks with 20–40 m in depth. All these landslides were formed in clays and sands of the Pliocene age in the decayed crust of these sediments. The general conclusions about landslides presented in this paper will be of great use for the construction of new facilities in the entire unstable area along the Danube, which is about 100 km in length. [ABSTRACT FROM AUTHOR]

Virtual reality is a technological development that, among others, has revolutionized Earth sciences. Its advantages include an opportunity to examine places otherwise difficult or impossible to access and it may also become an important component of education, fostering a better understanding of processes and landforms, geohazard awareness, and an appreciation of geoheritage. This paper reports on the GeoVT project, which aims to create a platform to build and disseminate Virtual Field Trips (VFTs) focused on geomorphology, natural hazards associated with geomorphological processes, and geoheritage sites. To put the GeoVT project in context, an overview of applications of VR in geosciences is provided. This paper subsequently proceeds with a presentation of the project and the GeoVT Authoring application, which is an innovative platform designed to help teachers and students, followed by brief presentations of a number of VFTs developed within the project. They address themes such as fluvial landforms and valley development, coastal landforms, evidence of past glaciation, coastal erosion, wildfire effects, mud volcanoes, and landslides. [ABSTRACT FROM AUTHOR]

The stability of loess landslides affects the production and livelihood of the people in its vicinity. The stability of loess landslides is influenced by various factors, including internal structure, collapsibility, water content, and shear strength. The landslide stability of loesses can be analyzed by several geophysical methods, such as seismic refraction tomography (SRT), electrical resistivity tomography (ERT), micro-seismic technology, and ground penetrating radar (GPR). Geotechnical tests (compression and shear tests) and remote sensing techniques (Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR) and airborne 3D laser technology) are used for studying the landslide stability of loesses as well. Some of the methods above can measure parameters (e.g., fractures, water content, shear strength, creep) which influence the stability of loess landslides, while other methods qualitatively indicate the influencing factors. Integrating parameters measured by different methods, minimizing disturbances to landslides, and assessing landslide stability are important steps in studying landslide hazards. This paper comprehensively introduces the methods used in recent studies on the landslide stability of loesses and summarizes the factors which affect the landslide stability. Furthermore, the relationships between different parameters and methods are examined. This paper enhances comprehension of the underlying mechanisms of the stability of loess landslides to diminish disastrous consequences. [ABSTRACT FROM AUTHOR]

This paper examines a flowslide involving a glacial deposit of low-plasticity silty sand triggered by a karstic spring after a rainfall period. The work aims at explaining the triggering, propagation and kinematics of flow-like landslides in a unique framework. In particular, a material point method open-source code, able to solve coupled hydro-mechanical problems for saturated/unsaturated soils, was developed. Laboratory and field experiments revealed a liquefaction potential of the mobilised material. To simulate such potential, a recent liquefaction model (Ta-Ger), validated so far at a laboratory scale, was selected, extended to unsaturated conditions, implemented and calibrated. The analysis indicates a complex behaviour of the moving mass and explains the mechanisms developing sequentially in the flowslide. The impact of the upper unstable soil mass against the soil at lower elevations is a key phenomenon to generalise soil liquefaction in the entire slope. Patterns of soil velocity and displacements are far from being a uniform flow of liquefied material. The model developed is a powerful tool to interpret flowslides involving a saturated and unsaturated soil profile. The paper includes sensitivity analyses and discusses the discrepancies observed in the run-up of the flowslide climbing on the opposite slope of the valley. [ABSTRACT FROM AUTHOR]

This review paper provides an overview of recent research on landslide susceptibility. Landslides are a natural phenomenon that can cause significant damage to infrastructure and endanger human lives. The paper presents an in-depth analysis of the factors that contribute to landslide susceptibility, including geological, hydrological and anthropogenic factors. It also discusses various methods and techniques used to assess landslide susceptibility, including statistical models, geographic information systems (GIS) and remote sensing. The paper examines the advantages and limitations of these methods and highlights the need for an integrated approach that combines multiple techniques to improve accuracy and reliability. Additionally, the paper discusses the challenges associated with developing landslide susceptibility maps and emphasises the importance of considering uncertainties and risk assessments. The review paper concludes by identifying the gaps in current research and suggesting potential directions for future studies. Overall, this review paper provides a comprehensive analysis of landslide susceptibility, which can serve as a valuable resource for researchers, practitioners and policymakers working in this field. [ABSTRACT FROM AUTHOR]

Resilience of systems to natural hazards has become an interesting concept in civil engineering and it is based on the determination of the losses due to the impacts of natural hazards. In the last decades, many contributions have focused on the assessment of losses that may occur at the time of the event, as generally assumed for earthquakes. However, this assumption may be incorrect when the interval between the time of occurrence and the time when the system functionality reaches the minimum value needs to be considered. This paper aims to propose a novel method to quantify this interval, which is called disruption time, by proposing a novel formulation of the loss model based on infrastructure redundancy. The proposed method was herein applied to a case study that considers landslides in Sri Lanka. The main goal of the paper is to propose a formulation that can be implemented in a more comprehensive framework to calculate more realistically the resilience of systems to natural hazards. [ABSTRACT FROM AUTHOR]

In areas affected by mining, which are undergoing reclamation, their geotechnical characteristics need to be monitored and the level of landslide risk should be assessed. This risk should preferably be reduced by nature-based solutions. This paper presents a KurZeS slope stability assessment technique based on areal data. This method is suitable for large areas. In addition, a procedure is presented for how to incorporate a prediction of the impact of nature-based solutions into this method, using the example of vegetation root reinforcement. The paper verifies the KurZeS method by comparing its results with the results of stability calculations by GEO5 software (version 5.2023.52.0) and validates the method by comparing its results with a map of closed areas in the area of the former open-cast mine Lohsa II in Lusatia, Germany. The original feature of the KurZeS method is the use of a pre-computed database. It allows the use of an original geometrical and geotechnical concept, where slope stability at each Test Point is evaluated not just along the fall line but also along different directions. This concept takes into account more slopes and assigns the Test Point the lowest safety factor in its vicinity. This could be important, especially in soil dumps with rugged terrain. [ABSTRACT FROM AUTHOR]

There are two technical challenges in predicting slope deformation. The first one is the random displacement, which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide. The second one is the dynamic evolution of a landslide, which could not be feasibly simulated simply by traditional prediction models. In this paper, a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria (SSSC-EMD) and deep bidirectional long short-term memory (DBi-LSTM) neural network. In the proposed model, the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components, viz. trend displacement, periodic displacement, and random displacement. Then, by analyzing the evolution pattern of a landslide and its key factors triggering landslides, appropriate influencing factors are selected for each displacement component, and DBi-LSTM neural network to carry out multi-data-driven dynamic prediction for each displacement component. An accumulated displacement prediction has been obtained by a summation of each component. For accuracy verification and engineering practicability of the model, field observations from two known landslides in China, the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation. The case study verified that the model proposed in this paper can better characterize the "stepwise" deformation characteristics of a slope. As compared with long short-term memory (LSTM) neural network, support vector machine (SVM), and autoregressive integrated moving average (ARIMA) model, DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation, with the mean absolute percentage error reduced by 3.063%, 14.913%, and 13.960% respectively, and the root mean square error reduced by 1.951 mm, 8.954 mm and 7.790 mm respectively. Conclusively, this model not only has high prediction accuracy but also is more stable, which can provide new insight for practical landslide prevention and control engineering. [ABSTRACT FROM AUTHOR]

Machine learning models are often viewed as black boxes in landslide susceptibility assessment, lacking an analysis of how input features predict outcomes. This makes it challenging to understand the mechanisms and key factors behind landslides. To enhance the interpretability of machine learning models in wide-area landslide susceptibility assessments, this study uses the Shapely method to explore the contributions of feature factors from local, global, and spatial perspectives. Landslide susceptibility assessments were conducted using random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, focusing on the geologically complex Sichuan-Tibet region. Initially, the study revealed the contributions of specific key feature factors to landslides from a local perspective. It then examines the overall impact of interactions among feature factors on landslide occurrence globally. Finally, it unveils the spatial distribution patterns of the contributions of various feature factors to landslide occurrence. The analysis indicates the following: (1) The XGBoost model excels in landslide susceptibility assessment, achieving accuracy, precision, recall, F1-score, and AUC values of 0.7815, 0.7858, 0.7962, 0.7910, and 0.86, respectively; (2) The Shapely method identifies the leading factors for landslides in the Sichuan-Tibet region as Elevation (3000-4000 m), PGA (1-2 g), NDVI (<0.5), and distance to rivers (<3 km); (3) Using the Shapely method, the study explains the contributions, interaction mechanisms, and spatial distribution patterns of landslide susceptibility feature factors across local, global, and spatial perspectives. These findings offer new avenues and methods for the in-depth exploration and scientific prediction of landslide risks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The ecological security pattern can harmonize the relationship between natural environmental protection and socio-economic development. This study proposes a regional ecological security pattern optimization framework by integrating theory and practice with landslide sensitivity and landscape structure. Using Yan'an City as an example, this study optimizes the landscape layout of preliminary ecological sources. The landslide sensitivity index is generated using the information value model and then used to adjust the ecological resistance surface. The Minimum Cumulative Resistance (MCR) approach is used to extract ecological corridors, locate ecological nodes utilizing circuit theory, and outline crucial ecological control areas. The results demonstrate: (1) the ecological sources are primarily composed of forestlands, with a total area of 2,352.2400 km 2 , concentrated in the southwest, central, and southeast regions. The optimal landscape granularity for the source patches is 600 m. (2) Yan'an is divided into four landslide sensitivity level zones: extremely high, high, medium, and low, with the overall landslide sensitivity of the region being high. (3) The highest ecological resistance is observed in built-up land and the lowest in forestland. The total number of ecological corridors is 26, avoiding most of the highly sensitive areas of landslides. (4) The number of ecological pinch points is 61, while the ecological barrier points amounted to 54. The critical ecological control areas consist mainly of cropland, forestland, and grassland, and differentiated restoration strategies are proposed to address their unique characteristics. The findings of the research can offer scientific guidance for the practice of ecological security protection in geohazard-prone areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In recent years, construction and demolition waste (CDW) landfills landslide accidents have occurred globally, with consequences varying due to surrounding environmental factors. Risk monitoring is crucial to mitigate these risks effectively. Existing studies mainly focus on improving risk assessment accuracy for individual landfills, lacking the ability to rapidly assess multiple landfills at a regional scale. This study proposes an innovative approach utilizing deep learning models to quickly locate suspected landfills and develop risk assessment models based on surrounding environmental factors. Shenzhen, China, with significant CDW disposal pressure, is chosen as the empirical research area. Empirical findings from this study include: (1) the identification of 52 suspected CDW landfills predominantly located at the administrative boundaries within Shenzhen, specifically in the Longgang, Guangming, and Bao'an districts; (2) landfills at the lower risk of landslides are typically found near the northern borders adjacent to cities like Huizhou and Dongguan; (3) landfills situated at the internal administrative junctions generally exhibit higher landslide risks; (4) about 70 % of these landfills are high-risk, mostly located in densely populated areas with substantial rainfall and complex topographies. This study advances landfill landslide risk assessments by integrating computer vision and environmental analysis, providing a robust method for governments to rapidly evaluate risks at CDW landfills regionally. The adaptable models can be customized for various urban and broadened to general landfills by adjusting specific indicators, enhancing environmental safety protocols and risk management strategies effectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The topography and landforms of Guizhou Province in China are complicated, and the climatic conditions of heavy precipitation make landslide disasters in Guizhou Province occur frequently. To avoid damage to people's lives and economic property caused by disasters, a reliable early landslide identification method and landslide monitoring method are urgently needed. Traditional landslide identification and monitoring methods have limitations. InSAR technology has unique advantages in large-scale landslide identification and monitoring, but landslide identification results based on a single deformation value are one-sided. Therefore, this paper uses Sentinel-1A radar satellite image data and uses InSAR technology and optical remote sensing technology to carry out large-scale surface deformation monitoring and identification of dangerous deformation areas in Liupanshui City, Tongren City, Guiyang City and other regions in Guizhou Province. The potential landslide identification methods based on the time series normalized difference vegetation index and landslide development environment elements are combined to investigate hidden landslide hazards in the study area. In this paper, time series InSAR technology is used to monitor three key landslides in Jichang Town, Yujiaying and Fana, to grasp the movement status of the landslide in time. The method of landslide identification and monitoring in this paper is of great significance for disaster prevention and management in Guizhou Province. [ABSTRACT FROM AUTHOR]

The disaster response currently involves an increasing number of different stakeholders, and understating their wants and needs is essential to managing them. Thus, the research goal of this paper is to identify and analyze the satisfactions of the main stakeholders involved in complex relationships during the disaster response process. First, this research applies a structured review that considers 72 papers gathered from the Scopus database and details the following: 28 wants and needs from the stakeholders and the identification of similarities related to (i) the complex and extensive flow of resources; (ii) the complementary coordination required to provide resources; (iii) the desire to be considered in the response plan; and (iv) the interdependency of the flow of resources and ideas, influences and interests. Then, these findings are discussed considering a real disaster response to landslides that occurred in 2011 and a corresponding simulation that was developed in 2017 in Brazil. The analysis addresses the Military's perspective and validates the findings while also adding new insights to the topic and two new wants and needs from the stakeholders. Finally, the paper discusses the implications of the findings for academics and practitioners overseeing collaborations in stakeholder management and presents a research agenda. [ABSTRACT FROM AUTHOR]

Over the past two decades, advances in remote sensing methods and technology have enabled larger and more sophisticated datasets to be collected. Due to these advances, the need to effectively and efficiently communicate and visualize data is becoming increasingly important. We demonstrate that the use of mixed- (MR) and virtual reality (VR) systems has provided very promising results, allowing the visualization of complex datasets with unprecedented levels of detail and user experience. However, as of today, such visualization techniques have been largely used for communication purposes, and limited applications have been developed to allow for data processing and collection, particularly within the engineering–geology field. In this paper, we demonstrate the potential use of MR and VR not only for the visualization of multi-sensor remote sensing data but also for the collection and analysis of geological data. In this paper, we present a conceptual workflow showing the approach used for the processing of remote sensing datasets and the subsequent visualization using MR and VR headsets. We demonstrate the use of computer applications built in-house to visualize datasets and numerical modelling results, and to perform rock core logging (XRCoreShack) and rock mass characterization (EasyMineXR). While important limitations still exist in terms of hardware capabilities, portability, and accessibility, the expected technological advances and cost reduction will ensure this technology forms a standard mapping and data analysis tool for future engineers and geoscientists. [ABSTRACT FROM AUTHOR]

Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate change. More in general, preparation of landslide inventories is a crucial activity since it provides the basic data for any subsequent analysis. In this work we present an event landslide inventory map (E-LIM) that was prepared through a systematic reconnaissance field survey in about 1 month after an extreme rainfall event hit an area of about 5000 km2 in the Marche-Umbria regions (central Italy). The inventory reports evidence of 1687 triggered landslides in an area of ~550 km2. All slope failures were classified according to type of movement and involved material, and documented with field pictures, wherever possible. The database of the inventory described in this paper as well as the collection of selected field pictures associated with each feature is publicly available at figshare. [ABSTRACT FROM AUTHOR]

Landslides not only cause great economic and human life losses but also seriously affect the safe operation of infrastructure such as highways. Rainfall is an important condition for inducing landslides, especially when a fault and weak interlayer exist on the slope, which can easily transform into a landslide and cause instability under the action of rainfall. To explore the effects of a soft interlayer, a fault, and extreme rainfall on slope stability, this paper takes the landslide on the right side of the G104 Jinglan Line in Shengzhou City, Shaoxing City, Zhejiang Province, China, as an example. The cause, failure mechanism, and characteristics of the landslide are analyzed through field investigation and borehole exploration in the landslide area. The slope is simulated by numerical analysis, and the stability of the landslide under natural conditions and extreme rainstorm conditions is calculated using the strength reduction method. The stability of the slope before and after treatment is compared, and the effectiveness of the treatment measures is verified by combining the field monitoring data. At the same time, the complex geological structure and rainfall are considered to have been the main factors leading to the G104 landslide. Near the fault, the weak interlayer of the landslide was easily disturbed, the deformation trend of the deep displacement was consistent with rainfall, and the axial force of the anti-slide piles at the weak interlayer was correspondingly large. For a wedge rock slope, "excavation unloading" and "prestressed anchor + prestressed anchor cable + anti-slide pile" are effective treatments. This paper reveals the effects of a weak interlayer, a fault, and strong rainfall on a rocky high slope, providing predictions of instability modes and time evolution patterns for similar complex geological slopes under rainfall infiltration conditions and providing references for their treatment measures. [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]

The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement of remote sensing and machine learning, data-driven methods have emerged as the mainstream in landslide prediction. Despite their strong generalization capabilities and efficiency, data-driven methods suffer from the loss of semantic information during training due to their reliance on a 'sequence' modeling method for landslide scenarios, which impacts their predictive accuracy. An innovative method for landslide prediction is proposed in this paper. In this paper, we propose an innovative landslide prediction method. This method designs the NADE ontology as the schema layer and constructs the data layer of the knowledge graph, utilizing tile lists, landslide inventory, and environmental data to enhance the representation of complex landslide scenarios. Furthermore, the transformation of the landslide prediction task into a link prediction task is carried out, and a knowledge graph embedding model is trained to achieve landslide predictions. Experimental results demonstrate that the method improves the F1 score by 5% in scenarios with complete datasets and 17% in scenarios with sparse datasets compared to data-driven methods. Additionally, the application of the knowledge graph embedding model is utilized to generate susceptibility maps, and an analysis of the effectiveness of entity embeddings is conducted, highlighting the potential of knowledge graph embeddings in disaster management. [ABSTRACT FROM AUTHOR]