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The Northeast part of India is experiencing an increase in infrastructure projects as well as landslides. This study aims to prepare the landslide susceptibility map of Tamenglong and Senapati districts, Manipur, India, and evaluates the state of landslide susceptibility along the Imphal-Jiribam railway corridor. Efficient statistical methods such as frequency ratio (FR), information value (IoV), weight of evidence (WoE), and weighted linear combination (WLC) were used in model preparation. A total of 322 landslide points were randomly divided into training (70%) and testing (30%) datasets. Nine causative factors were utilized for landslide susceptibility mapping (LSM). The importance of which was obtained using the information gain (IG) method. FR, IoV, WoE, and WLC were used to prepare the LSM using the training datasets and nine causative factors. Moreover, the accuracy and consistency were evaluated using AUC-ROC, precision, recall, overall accuracy (OA), balanced accuracy (BA), and F-score. The validation results showed that all methods performed well with the highest AUC and precision values of 0.913 and 0.95, respectively, for the IoV method, while the WLC method had the highest OA, BA, and F-score values of 0.808, 0.81, and 0.812, respectively. Finally, the results from LSM were used to evaluate the state of landslide susceptibility along the Imphal-Jiribam railway corridor. The results showed that 34% of the areas had high and very high susceptibility, while 40% were under less and significantly less susceptibility. The Tupul landslide area lay in medium susceptibility where the disastrous landslide occurred on 30 June 2022. Susceptibility values around the Noney and Khongsag railway station ranged from high to very high susceptibility. Thus, the study manifests the need for LSM preparation in rapidly constructing areas, which in turn will help the policymakers and planners for adopting strategies to minimize losses caused due to landslides., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The flash flood-induced erosion is the primary contributor to soil loss within the Indian Himalayan Region (IHR). This phenomenon is exacerbated by a confluence of factors, including extreme precipitation events, undulating topographical features, and suboptimal soil and water conservation practices. Over the past few decades, several flash flood events have led to the significant degradation of pedosphere strata, which in turn has caused landslides along with fluvial sedimentation in the IHR. Researchers have advocated morphometric, hydrologic, and semi-empirical methods for assessing flash flood-induced soil erosion in hilly watersheds. This study critically examines these methods and their applicability in the Alaknanda River basin of the Indian Himalayan Region. The entire basin is delineated into 12 sub-watersheds, and 13 morphometric parameters are analyzed for each sub-watershed. Thereafter, the ranking of sub-watersheds vulnerability is assigned using the Principal Component Analysis (PCA), compounding method (CM), Geomorphological Instantaneous Unit Hydrograph (GIUH), and Revised Universal Soil Loss Equations (RUSLE) approaches. While the CM method uses all 13 parameters, the PCA approach suggests that the first four principal components are the most important ones, accounting for approximately 89.7% of the total variance observed within the dataset. The GIUH approach highlights the hydrological response of the catchment, incorporating dynamic velocity and instantaneous peak magnifying the flash flood susceptibility, lag time, and the time to peak for each sub-watershed. The RUSLE approach incorporates mathematical equations for estimating annual soil loss utilizing rainfall-runoff erosivity, soil erodibility, topographic, cover management, and supporting practice factors. The variations in vulnerability rankings across various methods indicate that each method captures distinct aspects of the sub-watersheds. The decision-maker can use the weighted average to assign the overall vulnerability to each sub-watershed, aggregating the values from various methods. This study considers an equal weight to the morphometric, hydrological GIUH, and semi-empirical RUSLE techniques to assess the integrated ranking of various sub-watersheds. Vulnerability to flash flood-induced landslides in various sub-watersheds is categorized into three classes. Category I (high-priority) necessitates immediate erosion control measures and slope stabilization. Category II (moderate attention), where rainwater harvesting and sustainable agricultural practices are beneficial. Category III (regular monitoring) suggests periodic community-led soil assessments and afforestation. Sub-watersheds WS11, WS8, WS5, and WS12 are identified under category I, WS7, WS4, WS9, and WS6 under category II, and WS1, WS3, WS2, and WS10 under category III. The occurrence of landslides and flash-flood events and field observations validates the prioritization of sub-watersheds, indicating the need for targeted interventions and regular monitoring activities to mitigate environmental risks and safeguard surrounding ecosystems and communities., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.

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]

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.)

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.)

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.)

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The article discusses a special issue of the Journal of Marine Science & Engineering titled "Advances in Marine Engineering: Geological Environment and Hazards II." The special issue includes one review paper and fifteen research papers that cover various aspects of marine geological environments and hazards. The papers present the latest advancements in research, introducing state-of-the-art concepts, methodologies, and data. Topics covered include seabed response to nonlinear internal waves, evaluation of failure development during breaching, performance of composite bucket foundations, prediction of sediment sound speed, assessment of silt liquefaction hazards, evaluation of hydrodynamic performance of breakwaters, assessment of borehole instability in hydrate-bearing formations, prediction of seafloor sediment properties using deep learning, evaluation of wave-induced seabed liquefaction susceptibility, simulation of submarine landslides, movement of submarine turbidity currents, vortex-induced vibration characteristics of risers, and soil-structure interaction in marine engineering. The findings from these papers contribute to the understanding and development of marine geological environments and hazards. [Extracted from the article]

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]

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).)

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 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]

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]

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]

The eastern slope of Gongga Mountain is located in the mountainous region of Southwestern China, which has strong geologic tectonics that leads to frequent landslide hazards. A large number of such landslides were induced by the 2022 Luding Ms 6.8 earthquake. Therefore, it is necessary to identify the spatial distribution of landslides in the region. In this paper, the Google Earth platform and GF-1 and GF-6 satellite imagery were used to construct new pre-earthquake and co-seismic landslides. Then, we analyzed the relationship between the conditioning factors of the pre-earthquake and co-seismic landslide inventories and the spatial distribution of landslides, as well as the main controlling factors of landslide development. The main conclusions are as follows: (i) Through remote-sensing interpretation and field investigation, 1198 and 4284 landslides were recognized before and after the earthquake, respectively, and the scale was mainly small- and medium-sized. (ii) In two kinds of inventories, landslides are primarily distributed along the banks of the Dadu River basin, within elevations of 1200–1400 m and slopes of 30–50°. (iii) The distribution of pre-earthquake and co-seismic landslides was influenced by engineering geological layer combinations and earthquake intensity, with these two factors being the most significant. This paper plays an important role in hazard prevention and reconstruction planning in the Gongga Mountains. [ABSTRACT FROM AUTHOR]

The effects of global warming, coupled with the continuing expansion of urbanization, have significantly increased vulnerability to urban flooding, widespread erosion risks, and related phenomena such as shallow landslides and mudflows. These challenges are particularly evident in both lowland and hill/foothill environments of urbanized regions. Improving resilience to urban flooding has emerged as a top priority at various levels of governance. This paper aims to perform an initial analysis with the goal of developing an early warning system to efficiently manage intense convective rainfall events in urban areas. To address this need, the paper emphasizes the importance of analyzing different hazard scenarios. This involves examining different hydro-meteorological conditions and exploring management alternatives, as a fundamental step in designing and evaluating interventions to improve urban flood resilience. The Turin Metropolitan Area (TMA), located in north-western Italy, represents a unique case due to its complex orography, with a mountainous sector in the west and a flat or hilly part in the east. During the warm season, this urban area is exposed to strong atmospheric convection, resulting in frequent hailstorms and high-intensity rainfall. These weather conditions pose a threat to urban infrastructure, such as drainage systems and road networks, and require effective management strategies to mitigate risks and losses. The TMA's urban areas are monitored by polarimetric Doppler weather radars and a dense network of rain gauges. By examining various summer precipitation events leading to urban flooding between 2007 and 2021, this study assesses the practicability of deploying a weather-radar early-warning system. The focus is on identifying rainfall thresholds that distinguish urban flooding in lowland areas and runoff erosion phenomena in urbanized hills and foothills. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimizing the reconstruction of links in a network in the aftermath of natural disasters or human errors, such as landslides, floods, storms, earthquakes, bombing, war, etc. We aim to determine the optimal sequence for reconstructing the destroyed links within a specific time horizon, while simultaneously locating (k) portable emergency service centers (where (k > 2)) throughout the entire network. In this paper, the problem is considered in a tree structure. A greedy algorithm and a heuristic method, namely, maximum radius, are proposed to solve the problem. We evaluate the performance of the proposed algorithms using randomly generated data. The experimental results confirm the effectiveness of the proposed methods. [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]

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]

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]

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]