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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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Landslides are a natural hazard that presents a major threat to human life and infrastructure. Although they are a very common phenomenon in Colombia, there is a lack of analysis that entails national and comprehensive spatial, temporal, and socioeconomic evaluations of such events based on historical records. This study provides a detailed assessment of the spatial and temporal patterns and the socioeconomic impacts associated with landslides that occurred in the country between 1900 and 2018. Two national landslide databases were consulted and this information was complemented by local and regional landslide catalogues. A total of 30,730 landslides were recorded in the 118-year period. Rainfall is the most common trigger of landslides, responsible for 92 per cent of those registered, but most fatalities (68 per cent) are due to landslides caused by volcanic activity and earthquakes. An 'fN curve' revealed a very high frequency of small and moderate fatal landslides in the time frame., (© 2019 The Authors Disasters © 2019 Overseas Development Institute.)

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.)

The article provides information on assessment of landslide hazard mapping and reliability of thematic set of papers.

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]

A set of four thematic articles dealing with "Community Participation for Landslide Disaster Risk Reduction" will be published in this issue of Landslides following a call for papers, promoted by the authors, aimed at gathering field experiences in implementing and adopting landslide risk reduction measures at the community level in different social and economic environments. This editorial article briefly introduces the topic, states the objectives and the main findings of the four research studies, and provides some concluding remarks. [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]

A landslide, to the west of Montemurro (a small village in Southern Italy), has recently caused damage to buildings and other infrastructures in an urbanized area, as a result the development of new economic activities has been prohibited. The landslide was discovered in the last century and has been studied since the 1990's using classical geotechnical methods, but the sliding body continues to move. In this paper, we will present the results of a study carried out using field surveys, geognostic investigations and TDR (Time Domain Reflectometry) measurements in order to reconstruct the stratigraphy of the sediments involved and to further understand the geological and geomorphological context of the slope. This study is part of a larger multidisciplinary project of which the results will also be presented in this paper. The landslide (rotational slide in the upper sector, developing into a translational slide in the lower part) affects Quaternary continental clastic deposits resting on a bedrock formed by Tertiary siliciclastic sediments of the Gorgoglione Flysch. TDR measurements did not show any significant movement during the period monitored (January 2013-January 2014). Slip zone geometries were hypothesized using inclinometric measurements taken from previous studies, stratigraphic data and geomorphological interpretations of topographic scarps. Feedback from monitoring will confirm this hypothesis. [ABSTRACT FROM AUTHOR]

The 2008 Wenchuan earthquake-induced landslides destroyed larger areas of mountain vegetation and produced large volume of landslide-debris, which made the vegetation's hydrological adjusting function diminished and made the hydrological progresses in slopes changed, resulting in severe erosion and catastrophic debris flows for a rather long time. Since 2008, the landslide-damaged vegetation and its hydrological function have been recovering. In this paper, the Minjiang Upstream watersheds around Yingxiu Town were selected. First, the landslide-damaged vegetation was identified and monitored via multi-temporal (2001-2014) satellite images. Then, the slope materials stabil ity was assessed through topographic analysis of the vegetation survival environments. Then, the hydrological connectivity index (HCI) was defined to describe the upstream sediment production and downstream transport pathway. Finally, results indicated that HCI decreased annually with the vegetation recovery after the obvious increases during the earthquakes. While, analysis of 2008-2013 debris flow events indicated that the areas, the vertical drop to river<1000m and the horizontal distance to river<2500m, have high HCI increases and are more susceptible for debris flow formation. Monitoring the landslide-damaged vegetation recovery processes can contribute to assess the hydrological connectivity changes and understand the debris flow formation. [ABSTRACT FROM AUTHOR]

The article discusses a few points emerging from the paper "Expected Damage From Displacement of Slow-Moving Slides," by M. F. Mansour et al. The paper presents a list of published cases concerning slow-moving slides and their interaction with man-made works. It reports on two schematic diagrams that focused on the three basic phases of creep.

Rainfall-induced landslides not only cause property loss, but also kill and injure large numbers of people every year in mountainous areas in China. These losses and casualties may be avoided to some extent with rainfall threshold values used in an early warning system at a regional scale for the occurrence of landslides. However, the limited availability of data always causes difficulties. In this paper we present a method to calculate rainfall threshold values with limited data sets for the two rainfall parameters: maximum hourly rainfall intensity and accumulated precipitation. The method has been applied to the Huangshan region, in Anhui Province, China. Four early warning levels (Zero, Outlook, Attention, and Warning) have been adopted and the corresponding rainfall threshold values have been defined by probability lines. A validation procedure showed that this method can significantly enhance the effectiveness of a warning system, and finally reduce the risk from shallow landslides in mountainous regions. [ABSTRACT FROM AUTHOR]

Rainfall-induced shallow landslides are a widespread phenomenon that frequently causes substantial damage to property, as well as numerous casualties. In recent years a wide range of physically-based models has been developed to analyze the triggering process of these events. Specifically, in this paper we propose an approach for the evaluation of different shallow landslide triggering scenarios by means of TRIGRS numerical model. For the calibration of the model, a back-analysis of the landslide event occurred in the study area (located SW of Messina, north-eastern Sicily, Italy) on 1 October 2009 was performed, by using different methods and techniques for the definition of the input parameters. After evaluating the reliability of the model through the comparison with the 2009 landslide inventory, different triggering scenarios were defined using rainfall values derived from the rainfall probability curves, reconstructed on the basis of daily and hourly historical rainfall data. The results emphasize how these phenomena are likely to occur in the area, given that even short-duration (3-6 h) rainfall events having a relatively low return period (e.g. 10 years) can trigger numerous slope failures. On the contrary, for the same rainfall amount, the daily simulations overestimate the instability conditions. The tendency of shallow landslides to trigger in this area agrees with the high number of landslide/flood events occurred in the past and summarized in this paper by means of archival researches. Considering the main features of the proposed approach, the authors suggest that this methodology could be applied to different areas, even for the development of landslide early warning systems. [ABSTRACT FROM AUTHOR]

Among the most critical issues, climatic abnormalities caused by global warming also affect Taiwan significantly for the past decade. The increasing frequency of extreme rainfall events, in which concentrated and intensive rainfalls generally cause geohazards including landslides and debris flows. The extraordinary Typhoon Morakot hit Southern Taiwan on 8 August 2009 and induced serious flooding and landslides. In this study, the Kao-Ping River watershed was adopted as the study area, and the typical events 2007 Krosa Typhoon and 2009 Morakot Typhoon were adopted to train the susceptibility model. This study employs rainfall frequency analysis together with the atmospheric general circulation model (AGCM) downscaling estimation to understand the temporal rainfall trends, distributions, and intensities in the Kao-Ping River watershed. The rainfall estimates were introduced in the landslide susceptibility model to produce the predictive landslide susceptibility for various rainfall scenarios, including abnormal climate conditions. These results can be used for hazard remediation, miti gation, and prevention plans for the Kao-Ping River watershed. [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]

Slot-check dam system is commonly used to control sediment transport associated with debris flows and mitigate debris flow hazards. This paper aims to estimate the performance of the slot-check dam system in the field and set up a verification to evaluate the effciency of a slot-check dam system and each subsystem in debris flow sediment control. Field survey on a group of a series of slot-check dams at Shengou Basin in Yunnan, China reveals that the conserving sediment volume of each dam is related to its relative location in the group, gradually decreasing from upstream to downstream. The cumulative sediment volume within a subsystem of slot-check dams closely re10 lated to the characteristics of the catchment controlled by the subsystem. It increases with the controlled catchment area of the most downstream dam in subsystem and the distance from the dam to the upstream most. Evaluation models for the conserving effciency of a slot-check dam system on debris flow control in a river basin and each subsystem within the group associate to the controlled catchment characteristics have been proposed. The layout principle of a slot-check dam system in a river basin has been developed based on the conserving effciency of a subsystem of slot-check dams which would allow the slot-check dam system to be designed in a more scientific way. [ABSTRACT FROM AUTHOR]

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]

The main aim of this paper is to test economic benefits of landslide prevention measures vs. post-event emergency actions. To this end, small and large scale analyses were performed in a training area located in the North-Eastern Italian pre-Alps that was hit by an exceptional rainfall event occurred in November 2010. At the small-scale, landslide susceptibility was initially assessed using a simple probabilistic analysis, which allowed to highlight the main landslide conditioning factors and the most hazardous areas. However, this approach revealed to be quite insufficient to reach planned goals, so a large-scale case-by-case analysis was performed: a study case was defined, accord ing to landslide occurrence frequency and assessment of elements at risk. Numerical modeling demonstrated that remedial works carried out after the landslide - waterremoval intervention such as a drainage trench - could have improved slope stability if applied before its occurrence. Then, a cost-benefit analysis was finally employed. It defined that prevention would have been economically convenient compared to a non preventive and passive attitude, allowing a 30%saving relative to total costs. Therefore, this kind of approach could be actually used as a mean toward preventive soil protection not only within the investigated case study, but also in all those hazardous areas where preventive measures are needed. [ABSTRACT FROM AUTHOR]

Most landslides in Korea are classified as shallow landslides with an average depth of less than 2 m. These shallow landslides are associated with the advance of a wetting front in the unsaturated soil due to rainfall infiltration, which results in an increase in water content and a reduction in the matric suction in the soil. Therefore, this study presents a modified equation of infinite slope stability analysis based on the concept of the saturation depth ratio to analyze the slope stability change associated with the rainfall on a slope. A rainfall infiltration test in unsaturated soil was performed using a column to develop an understanding of the effect of the saturation depth ratio following rainfall infiltration. The results indicated that the rainfall infiltration velocity due to the increase in rainfall in the soil layer was faster when the rainfall intensity increased. In addition, the rainfall infiltration velocity tends to decrease with increases in the unit weight of soil. The proposed model was applied to assess its feasibility and to develop a regional landslide susceptibility map using a Geographic Information System (GIS). For that purpose, the spatial databases for input parameters were constructed and landslide locations were obtained. In order to validate the proposed approach, the results of the proposed approach were compared with the landslide inventory using ROC (Receiver Operating Characteristics) graph. In addition, the results of the proposed approach were compared with the previous approach used steady state hydrological model. Consequently, the approach proposed in this study displayed satisfactory performance in classifying landslide susceptibility and showed better performance than the steady state approach. [ABSTRACT FROM AUTHOR]

The 3 August 2014 Ludian, China Ms 6.5 earthquake has spawned a mass of severe landslides. Of them the biggest occurred at Hongshiyan near the epicenter, which has 1200×104 m³, clogging the Niulanjiang River, and creating a large dammed lake. Post-event field investigations yield detailed data on following aspects: rock structure of landslides, lithology, and geometry of the dam, composition and grain sizes of debris avalanches. Based on these data, this work further analyzes the geology and topography of the Hongshiyan area, and explores the mechanism for occurrence of such an unusual big landslide at this place. Our analysis suggests the following conditions are responsible for this catastrophic event: (1) during the Ms 6.5 earthquake, the special terrain and site conditions led to abnormally strong ground shake. (2) Hongshiyan lies nearby an active fault, where intense crustal deformation resulted in rock fractures and weathering. (3) Intense incision on the river increased topographic relief with steep slopes and scarps. (4) Combined structures, including unloading fissures, high-angle joints and low-angle beds along the river as well as upper-tough and lower-soft structure on the slopes. It is the joint functions of these conditions that triggered such seldom seen landslides during a moderated-sized earthquake. [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]