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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]

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

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

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

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

Motivated by some landslide models, and related estimation challenges, this paper presents an optimal estimation method for state and parameter in a special class of so-called ODE–PDE system based on the adjoint method for discrete-time asynchronous measurements. This system is described by a pair of coupled Ordinary Differential Equation (ODE) and Partial Differential Equation (PDE), with a mixed boundary condition for the PDE. The coupling appears both in the ODE and in the Neuman boundary condition of the PDE. For this system, initial conditions or state variables and some empirical parameters are assumed to be unknown and need to be estimated. The Lagrangian multiplier method is used to connect the dynamics of the system and the cost function defined as the least square error between the simulation values and the available measurements. The adjoint state method is applied to the objective functional to get the adjoint system and the gradients with respect to parameters and initial state. The cost functional is optimised, employing the steepest descent method to estimate parameters and initial state. This general approach is illustrated by two application examples corresponding to two different landslide models that validate the presented optimal estimation approach. The first one is about state and parameter estimation in an extended sliding-consolidation landslide model, and the second one is in the viscoplastic sliding-consolidation landslide model. [ABSTRACT FROM AUTHOR]

Displacement prediction is a direct and effective method for mitigating geohazards. Due to the influence of rainfall and reservoir water level variations, landslides often display step-like deformations with an increasing trend and periodic fluctuation, indicating long-term memory in displacement time series. Traditional data-driven methods are mostly suitable for short-term prediction, and extra data processing is applied to solve this problem. This paper proposes a novel deep learning-based displacement prediction method using long short-term memory (LSTM) networks. Based on open-source frameworks for deep learning, namely, Keras and TensorFlow, a detailed implementation of displacement prediction is proposed and illustrated. The Baishuihe landslide, a typical landslide with long-term monitoring, is taken as a case study, and both single-factor and multi-factor predictions are performed. The results indicate that multi-factor prediction can reduce overfitting and improve accuracy. Compared with the existing method, the multi-factor deep-learning model displays better performance. This study indicates that the LSTM-based deep-learning model is suitable and convenient for displacement prediction and has broad prospects in safety monitoring of water-induced landslides. [ABSTRACT FROM AUTHOR]

This paper accompanies the Maps of Geohazard features of the Cilento and the Calabro- Tyrrhenian continental margin in the southern Tyrrhenian Sea (Italy). The main geohazardrelated features were derived from extensive seafloor mapping through the collection of high-resolution multibeam data acquired during several oceanographic cruises. They encompass many fluids seepage features, fault scarps, landslides scars, gullies, channels, and canyons. Hazards related to coastal landslides and shelf-indenting canyons are very high in these sectors (especially in southern Calabria) due to active seismicity coupled with rapid uplift, high sedimentation rates and narrow or totally absent continental shelf, thus promoting a direct connection between steep slopes and coastal areas. In this setting, masswasting features can directly impact coastal or submarine infrastructures or indirectly create local tsunami waves, as observed in historical times. Moreover, this physiographic setting of the margin facilitates the transfer of marine litter toward deep-sea areas. [ABSTRACT FROM AUTHOR]

The MaGIC project (Marine Geohazard along the Italian Coasts) had the aim of mapping the geohazard in the Italian seas and resulted in the production of numerous maps covering parts of the Italian Seas. In this paper, we present the maps: 'The submerged portions of the Aeolian volcanic islands and the north-eastern Sicilian margin', located in the south-eastern Tyrrhenian Sea. Both areas are affected by active geological processes, which represent important geohazards elements. Inthe submarine parts of the Stromboli volcanoremobilization of volcaniclastic deposits occur along the Sciara del Fuoco, where small-scale instabilities may represent a source of geohazard. Hydrothermal activity occurs on Enarete and Enaretino conical seamounts. The north-eastern Sicilian margin has a narrow continental shelf. Numerous canyon heads indent the shelf and, sometimes, reach close to the coast. Canyons have often a retrogradational trend and further eventual landward shift through sliding can iendangeri coastal or offshore infrastructures. Many of the canyons connect with leveed channels with widespread sediment instability. In the Gioia Basin, some of the channels connect to form the Stromboli slope Valley. Volcanic unrest or local and regional earthquakes are proven to have caused submarine landslides and tsunamis. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding LSM's developmental drivers. Utilizing SciMAT, a scientometric tool, we analyzed 1661 papers on LSM from the Web of Science core collection database, spanning 1993 to 2022. We employed cluster and thematic evolution analysis in SciMAT to unveil trends. The results indicate a consistent upward trend over the past three decades. LSM modeling methods, geological data, and contributing factors are major focal points. Notably, the evolution of LSM models, with a rising adoption of machine learning and deep learning for risk assessment, emerges as a central knowledge pathway. This study offers valuable insights to scholars by identifying literature gaps and highlighting crucial research directions, facilitating informed decision-making in the realm of LSM. SciMAT tool initially employs cluster analysis and thematic evolution analysis to pinpoint the current popular themes and evolutionary trends of landslide susceptibility. The main trajectory of knowledge lies in the progression of landslide modeling methods. The latest trend in landslide susceptibility research is centered on the use of deep learning techniques, particularly convolutional neural networks, to achieve precise risk zonation. The study aims to highlight critical research directions, predict the future of LSM technology, and provide guidance for scholars. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment (LSA) is a significant part of landslide research, which plays an important role in preventing landslide disasters. It has gained an increasing attention in both the academic and practice fields for the past two decades. However, there have been few bibliometric analyses on this topic, although bibliometric analysis can inspire future researchers by exploring the overall characteristics of the published literature. This article aims at collecting and analyzing the information of the abstracts, authors, institutions, countries, journals, funds, and keywords of the recent 4,732 papers published from 1999 to 2021 in Web of Science (www.webofscience.com). In particular, latent Dirichlet allocation (LDA), a machine learning and text analysis method, is utilized to analyze the abstract of each article to identify the hottest research topics related to LSA. The results revealed that: (1) The amount of annual publications related to LSA generally shows an increasing trend, which accounts for about 22% of the total landslide publications in 2021; (2) The author of Pradhan B, the institution of the Chinese Academy of Sciences, the country of China, the journal of Natural Hazards and the funding agency of the National Natural Science Foundation of China, are the productive performers in each aspect of LSA; and (3) Machine learning methods have gained a rapid increase in LSA in recent five years, which have become the most popular research topic. [ABSTRACT FROM AUTHOR]

Ensemble learning synthesizes the advantages of different models and has been widely applied in the field of spatial prediction. However, the nonlinear constraints of spatial heterogeneity on the model ensemble process make it difficult to adaptively determine the ensemble weights, greatly limiting the predictive ability of the ensemble learning model. This paper therefore proposes a novel geographical spatial heterogeneous ensemble learning method (GSH-EL). Firstly, the geographically weighted regression model, geographically optimal similarity model, and random forest model are used as three base learners to express local spatial heterogeneity, global feature correlation, and nonlinear relationship of geographic elements, respectively. Then, a spatially weighted ensemble neural network module (SWENN) of GSH-EL is proposed to express spatial heterogeneity by exploring the complex nonlinear relationship between the spatial proximity and ensemble weights. Finally, the outputs of the three base learners are combined with the spatial heterogeneous ensemble weights from SWENN to obtain the spatial prediction results. The proposed method is validated on the PM2.5 air quality and landslide dataset in China, both of which obtain more accurate prediction results than the existing ensemble learning strategies. The results confirm the need to accurately express spatial heterogeneity in the model ensemble process. [ABSTRACT FROM AUTHOR]

AbstractSubmarine landslides are a critical geological process that significantly influences seafloor stability and geological evolution, making them a focal point of geological disaster research. Particularly, rapid sedimentation as a crucial preconditioning factor for submarine landslide initiation is increasingly recognized in contemporary research. However, no comprehensive review currently examines the correlation between rapid sedimentation and submarine landslides. To elucidate the interaction mechanisms between submarine landslides and rapid sedimentation and to provide a new theoretical foundation for the prediction and prevention of geological hazards, the study categorizes three primary sedimentation patterns in marine environments and establishes a detailed historical database of sedimentation-related submarine landslide events. Moreover, the paper systematically reviews and critiques the existing research methods and advancements in addressing rapid sedimentation-related submarine landslides, and a bibliometric analysis of recent research progress is conducted. Finally, the study highlights several challenges in the study of rapid sedimentation and proposes future recommendations, aiming to provide robust support for the in-depth investigation of submarine landslides. [ABSTRACT FROM AUTHOR]

Over the last few decades, the impact of landslides has increased in Latin America. However, there is a deficiency in the region of diagnostic studies on landslide hazards. This review analyzes the state of the art of studies on landslide hazard occurred in Latin America. A search for articles published from 2000 to 2019 was carried out in Scopus, WoS, SciELO, REDIB, and Redalyc. The search yielded 1,365 publications, which were purged obtaining 283 articles, analyzed through a database organized in four categories: general information, study area, research objectives, and methodology. We identified an increase in the number of publications from the year 2011. Seventy-five percent of the 20 countries in the region published papers on landslides that had occurred in these countries, mainly research focus on susceptibility and characterization, and a reduced number, on hazardousness, inventories, trigger factors and conditioning factors. The results suggest the opportunity to incorporate novel methods, tools, techniques, and inputs into research of landslides in Latin America. This review constitutes an analytical and critical contribution to the study of landslide hazards in the region and indicates the studies that are urgent to enforce its integral management in the region and in other intertropical zones. [ABSTRACT FROM AUTHOR]

Spatial variability of properties of geo-materials has a significant impact on the stability of landslides. In this paper, the spatial variability of soil properties of a landslide is modelled using random-field generated by the discrete cosine transform. The method developed can be readily applied to determine the probability of failure of the landslide using Monte Carlo simulation. A case study of a landslide with spatially variable properties is performed and discussed. The proposed approach is shown to be effective and efficient for probabilistic stability analysis of landslides modelled using 2-D random-field. [ABSTRACT FROM AUTHOR]

On 1st of August 2012, two successive flash floods hit Nasiri hamlet in Maluku archipelago, Indonesia. The floods destroyed 61 houses and swept some of them down into the sea. A monitoring and community-based early warning system (MCBEWS) was developed 2 years later. This paper presents the approach, development, and implementation of the system. There are two components, namely community capacity-building and the establishment of telemetric monitoring. The flood warning system considers two causes: extreme rainfall and landslide dam breaks. Raising awareness, workshops, training, and disaster organisation establishment appeared to effectively increase flood disaster preparedness capacity and community commitment. The adoption of a rainfall depth vs. intensity chart method for issuing flood warnings seems to be promising. The presence of an unusual flow reduction in the record during extreme rainfall indicates damming at the upstream of the record station due to landslides. Possible landslide dams near the downstream to the middle reaches might provide dangerous high peak discharge flash floods with short warning times. [ABSTRACT FROM AUTHOR]

Many models have been developed for landslide displacement prediction, but owing to complex landslide-formation mechanisms and landslide-inducing factors, such models have different prediction accuracies. Thus, landslide displacement prediction remains a popular but difficult topic of research. In this paper, a landslide prediction model is proposed by combining extreme learning machine (ELM) and random search support vector regression (RS-SVR) sub-models. Particularly, the combined model decomposed accumulative landslide displacement into two terms, trend and periodic displacements, using a time series model, and simulated and predicted the two terms using the ELM and RS-SVR sub-models, respectively. The predicted trend and periodic terms are then summed to obtain the total displacement. The ELM and RS-SVR sub-models are applied to predict the deformation of Baishuihe landslide in the Three Gorges Reservoir Area (TGRA) as an example. The results showed that the model effectively improved the accuracy, stability, and scope of application of landslide displacement prediction, thus providing a new method for landslide displacement prediction. [ABSTRACT FROM AUTHOR]

The offshore Campos Basin, located in Southeastern Brazil represents an important area in the Brazilian economy as it is one of the most prolific oil-producing basins in the country. With the development of offshore infrastructures in this region, structural design and location must consider different geohazards. This paper addresses the seismic response of gentle submarine slopes subjected to small-to-moderate-sized earthquakes, representative of the Brazilian seismic conditions. The seismic analyses employed numerical modeling, with a nonlinear constitutive model. A set of bedrock ground motions representative of the offshore Campos Basin seismicity was obtained, employing a Uniform Hazard Response spectrum for the region and earthquakes from the Pacific Earthquake Engineering Research (PEER) database. Numerical modelling results showed that, even for small slope angles and moderate earthquakes, there is an accumulation of permanent displacements and shear strains in the downslope direction can affect the stability and performance of existing offshore infrastructures. The findings of this research provide a valuable reference for the prediction of seismic displacements, shear strains and seabed amplification or attenuation of earthquakes on gentle slopes in deep-water deposits similar to those found in the offshore Campos Basin. [ABSTRACT FROM AUTHOR]

The protection of cultural heritage from water-soil interaction related threats is a crucial challenge for the scientific community. Among the hazards threatening heritage buildings, slow-moving landslides have been recently found to produce significant damage to historic masonry churches. However, assessing the effects of these phenomena is very challenging, as detailed information about slow-moving landslide movements is not generally available. To tackle this problem, geotechnical analyses can be performed, which enable the landslide-induced soil displacement profiles to be predicted. In view of this, this paper investigates the effects of slow-moving landslides on historic masonry churches by performing geotechnical analyses on two case studies located in the Liguria region (Italy). For each case study, limit equilibrium (LE) and finite element (FE) analyses were carried out with reference to representative sections of the slope on which the building was located. Different scenarios of water table levels were also simulated to evaluate a potential evolution of the slope stability conditions towards failure. Finally, the damage level of the building under study was assessed through a damage criterion available in the literature and compared with the severity of the damage observed on site. [ABSTRACT FROM AUTHOR]

This paper focuses on Deep-Seated Gravitational Slope Deformations (DSGSDs) identified in the Molise region (Italy), along the central-southern sector of the Apennines chain. These phenomena were detected and mapped through a combination of aerial-photo interpretation, morphological analyses, and field surveys, integrated through satellite SAR interferometry (InSAR). The obtained general map is organized in different sections: inventory map with identification of DSGSDs and main geological and orographic features, geostructural scheme, representative cross sections, list and characteristics of DSGSDs, geomorphometric maps, landslides map, and seismotectonic setting. This study allowed us to realize a complete inventory of DSGSDs in the Molise region and define the main characteristics of each phenomenon. Through this research, it was possible to establish a general correlation between the analyzed deformations and the geological and geomorphological setting of the region as well as with its geodynamic context. [ABSTRACT FROM AUTHOR]

The coastline between Tetouan and Bou Ahmed in the northernmost Rif of Morocco and its hinterland has become immensely hazardous due to frequent triggering of diversified landslides from last two decades. This paper describes the potential application of a set of multisource data and the GIS platform for zoning and identifying anomalous areas prone to landsliding and its associated landslide hazards. For this purpose, Information value (IV), Statistical index SI (Wi), Weighting factors (WF) and Evidential belief function (EBF) models have been used in this study. Eleven conditioning factors such as elevation, slope, aspect, curvature, shaded/relief, proximity to streams, proximity to faults, proximity to roads, land use, lithology, annual rainfall and an inventory of 905 unstable spots were used to develop the spatial database for landslides susceptibility mapping (LSM). The factors have been used after a test of multi-collinearity. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) methods were used for validation of the LSM. The AUC results showed good prediction accuracy for all models with a prediction rate of 78% (IV), 77% (SI), 73% (WF) and 70% (EBF) respectively. However, the results indicated that comparatively, the IV model followed by WI model is more precise and accurate for landslides susceptibility mapping than other models. According to the presented models, about 64% of the study area is located in high to very high landslide susceptible zone. The findings presented in this study are imperatively valuable especially wherein large development projects and land use planning activities are going on. [ABSTRACT FROM AUTHOR]

In this paper, an original methodology for landslide susceptibility mapping (LSM) is presented. It consists of bagging ensembles of artificial neural networks (ANNs) and random forests (RFs), and hybrid bagging ensembles of these models. It is applied on the area of the Itajaí-Açu river valley. In December 2020, there was an extreme rainfall in the region, which triggered landslides. The RF ensemble presented slightly higher accuracy (0.941) than the ANN ensemble (0.940), but the ANN ensemble had a more balanced relation between sensitivity (0.966) and specificity (0.915) than the RF ensemble (specificity = 0.992, sensitivity = 0.891). The mixed ANN-RF ensemble presented the higher accuracy of all (0.950), and a good balance between sensitivity (0.948) and specificity (0.951), being considered the best model within those analyzed. The hybrid ensemble, together with classification threshold adjustment, removed discrepancies on the maps between both models by attenuating them. [ABSTRACT FROM AUTHOR]

Susceptibility based on different landslide types has rarely been assessed. Therefore, this paper aims to compare the prediction performance of hybrid approaches by combining the logistic model tree (LMT) with Decorate, random subspace, and rotation forest ensemble techniques (De-LMT, RS-LMT, and RoF-LMT) for susceptibility assessments of different landslide types. The Yuzi River catchment along the eastern margin of the Tibetan Plateau was selected as the study area. In this catchment, 478 rockfalls and 167 landslides were identified, and further partitioned into training (70%) and validation (30%) datasets, respectively. Subsequently, 13 conditioning factors were initially considered, and then 11 and 8, respectively, were selected as the final rockfall and landslide influencing parameters to conduct susceptibility models through multicollinearity analysis and information gain. Finally, the model performances were evaluated by area under the receiver operating characteristic curves (AUC). The obtained results demonstrated that elevation and rainfall were most influential factors for rockfall and landslide. For the rockfall prediction, the De-LMT model achieved the best prediction accuracy with the highest AUC (0.939), followed by the RS-LMT (0.938), RoF-LMT (0.928), and LMT (0.919) models. For the landslide prediction, the De-LMT model (AUC = 0.931) outperformed and outclassed the RoF-LMT (0.926), RS-LMT (0.922), and LMT (0.915) models. Therefore, it is reasoned out that all models exhibited satisfactory performance (AUC > 0.9), and the De-LMT model was superior in rockfall and landslide spatial prediction. [ABSTRACT FROM AUTHOR]

This paper explores the scope for upscaling and transnational transfer of participatory landslide risk management strategies for informal settlements in Latin America. Drawing on lessons from transdisciplinary action-research in three neighbourhoods in Medellín, Colombia, and one in São Paulo, Brazil, the paper discusses how bottom-up approaches were developed to co-produce landslide risk management in both Global South cities, in a way that optimises the collaboration between communities and relevant governmental bodies. The analysis focuses on mitigation and 'agreement-seeking', from the perspectives of scale, power and knowledge, which help understand the parallels between co-production of landslide risk management and co-production of urban services. Two key conclusions are that landslide risk management should be built into neighbourhood upgrading and management, and that both community and the state have stronger roles to play in landslide risk management from their respective capacities. The paper also reflects on the role of academia in enabling co-production of landslide risk management through engaging with local communities. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is used to predict the occurrence probability of landslides, and the results can provide a scientific basis for preventing and reducing landslides. Traditional assessment models usually assume that the contribution degree of landslide influence factors is fixed throughout an entire study area. Therefore, the results can only compare the relative weights of the different influencing factors, thereby ignoring changes in their contributions in the study area. This paper introduces a geographically weighted logistic regression (GWLR) model to assess the susceptibility of study area to landslides, fully considers the spatial heterogeneity of the influencing factors, and further explores the spatial relationships between the influencing factors and landslide occurrence. Based on landslide data from Zhenxiong County, Yunnan Province, China, collected in 2015, we selected 10 influencing factors covering the five aspects of topographies and geomorphologies, geological structures, meteorological conditions, ecological environments and human activities and implemented multicollinearity and significance tests to obtain the most suitable factors required by the model. Then, the GWLR model was used to analyze the degrees of landslide susceptibility across Zhenxiong County. The results show that the area under the receiver operating characteristic curve (ROC) of the GWLR model was 0.904, which suggests that the model had a high prediction accuracy. In addition, the regression coefficient maps of the influencing factors based on the GWLR model reflected changes in the relative contributions of each influencing factor in the study area, providing a reference for managers to formulate targeted decision-making measures. [ABSTRACT FROM AUTHOR]

The concept of leveraging the predictive capacity of predisposing factors for landslide susceptibility (LS) modeling has been continuously improved in recent work focusing on computational and machine learning algorithms. This paper explores the predictive capacity of different approaches to LS modelling using artificial intelligence. The key objective of this study is to estimate a LS map for the Taleghan-Alamut basin of Iran using Credal Decision Tree (CDT)-based (i.e. CDT-Bagging, CDT-Multiboost and CDT-SubSpace) hybrid machine learning approaches, which are state-of-the-art soft computing approaches that are hardly ever utilized in the assessment of LS. In this study, we used eighteen landslide predisposing factors (LPFs) that we considered to be the most important local morphological and geo-environmental factors influencing the occurrence of landslides. We calculated the significance of each of the LPFs in the landslide susceptibility assessment using the Random Forest Method. We also employed the Receiver Operating Characteristic curve, precision, performance, map robustness measurement and selection of the best-fitting models. The results shows that, compared to the other models, the CDT-Multiboost is the excellent model in this perspective with an average area under curve (AUC) of 0.993 based on a 4-fold cross-validation. We, therefore, consider the CDT-Multiboost models to be an effective method for improving spatial prediction of LS where landslide scarps or bodies are not clearly identified during the preparation of landslide inventory maps. Therefore, it will be helpful for preparing future landslide inventory maps and mitigate landslide damages. [ABSTRACT FROM AUTHOR]

In this paper, the global sliding mode control technique is designed for anti-sway control of offshore crane mounted on surface vessel. The offshore cranes are responsible for moving containers from mega container ships to a smaller vessel. In this regard, after introducing the dynamics of the crane mounted on the surface vessel, the sliding surfaces and control laws are designed to maintain the stability of the system against external disturbances such as sea waves and wind induced movements. Furthermore, using the Lyapunov's stability analysis, it is proven that the position/velocity errors are converged to the origin. Finally, to determine the effectiveness of the proposed method, the simulation results of the global sliding mode control technique are compared to those of the continuous integral sliding mode control and proportional-integral-derivative approaches. The development of this procedure due to its high productivity and economic feasibility can be used as a new tool in the modern maritime industry. [ABSTRACT FROM AUTHOR]

The paper develops an analytical method to design the plane arrangement of stabilising piles under spatial lateral load of 3D colluvial landslides with multilayer sliding masses. Firstly, an approach based on the geological map and binary Lagrange Interpolation method was presented to establish 3D landslide calculation models. The improved 3D limit equilibrium method was then proposed to calculate the stability coefficient and spatial distributed lateral load of landslide with multilayer sliding masses. And main sliding direction of 3D multilayer landslide can be determined by the developed principle of minimum potential energy. Moreover, through assuming the piles and soil as elastic transversely isotropic continuum medium, analytical approach was employed to accurately calculate the stress distribution of soil between adjacent piles; and the maximum pile spacing can be calculated. Finally, the reasonable arrangement of stabilising piles can be determined based on Monte-Carlo method and cost of all piles. The finite difference method was employed to validate the theoretical results through the case study of Bazimen landslide. And it is found that the proposed method for the design of piles' arrangement is economically beneficial compared with the traditional method on the premise of ensuring safety. [ABSTRACT FROM AUTHOR]

China is a country with frequent geological disasters. The geological disasters in different regions are different, such as earthquakes, landslides, collapses, karst collapses, and ground subsidence. With the development of science and technology and the improvement of engineering technology, the combination of image technology and digital technology is constantly developing. People can improve the accuracy of prediction by studying the characteristics of rock formation under different geological conditions while collecting geological data and sorting various information. And reliability, can more accurately identify poor geological conditions, can increase construction safety and reduce economic losses. Based on the comprehensive CT geological survey data, this paper uses nanomaterials to construct software DEM to model the porous medium of the Yanziyan dangerous rock mass in Jinfo Mountain, Nanchuan District, Chongqing and to estimate the area and volume of the collapse potential area of the high-steep dangerous rock mass parameter. [ABSTRACT FROM AUTHOR]

The Piuro 1618 landslide is renowned as one of the most catastrophic historical events of the Central Alps. The landscape of the Bregaglia Valley has been successively modified to such an extent to make it difficult to ascertain the source of the landslide, the extent of its deposits and the setting of the ancient village. This study focuses on the identification of the source area of the landslide, its dynamics, the extent of its deposits and the estimation of the involved volumes. The geological map here proposed permitted to reconstruct the relationships between the pre-1618 setting, the 1618 restructuration of the landscape and the post-event changes that occurred owing to erosion by the trunk river, deposition of debris flow fans and slope dynamics. The paper shows how geological mapping may help to unravel landscape evolution of an Alpine valley even at the high-resolution required by historical and archaeological studies. [ABSTRACT FROM AUTHOR]

Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90. [ABSTRACT FROM AUTHOR]

Submarine telecommunication cables, which carry about 99% of international telecommunications, are usually buried shallowly beneath seabed and vulnerable to seabed movements. In recent years, many submarine cable damages have been observed when significant lateral seabed movements occur due to geohazards, e.g., offsets of strike-slip faults and submarine landslides. However, research on the interaction between submarine telecommunication cables and lateral seabed movements is limited. There is no existing method for evaluating the cable safety when subjected to lateral seabed movements. This paper aims to carry out finite element (FE) analysis and physical modeling to investigate the responses of submarine telecommunication cables to lateral seabed movements. A FE model is developed and validated by full-scale physical test results. After validation of the FE model, a series of parametric studies is performed covering typical ranges of soil properties, cable burial depths, cable characteristics and seabed movement characteristics. The parametric study results are summarized in a dimensionless plot, followed by a regression analysis. A simple but effective formula is proposed to predict the maximum cable strains induced by lateral seabed movements. The dominant parameters that control submarine cable responses are identified and incorporated into the formula. Safety criteria for submarine telecommunication cables are also proposed for evaluating the cable safety when subjected to lateral seabed movements. [ABSTRACT FROM AUTHOR]

Starting on 24th August 2016, Central Italy was struck by a six-month earthquake sequence that caused 303 victims and extensive major damages to urban areas and infrastructures, in some cases entire villages needed complete rebuilding. In this paper we present a map that portrays the overall susceptibility to multiple landslide types and the exposure to landslides of the rural-urban areas of the Castelsantangelo sul Nera Municipality, a typical village of the central Italian Apennine. The map is based on a procedure that ingests geomorphological data and models and groups the individual landslide susceptibility maps in a joint susceptibility and exposure map based on expert-defined criteria. The procedure has been applied to built-up and to undeveloped areas to highlight their exposure and was used as a tool for planning post-seismic reconstruction. We advise that such maps are used also as basic tool for ordinary urban planning. [ABSTRACT FROM AUTHOR]

The paper describes the multitemporal landslide inventory map prepared for the urban areas of Motta Montecorvino and Volturino, two municipalities located in the Southern Apennines (Apulia Region, Italy). These territories show a high propensity to landslides of different types and magnitude, which periodically interfere with the anthropic structures and infrastructures. For the study area, the spatial and temporal distribution of landslides is detected for the period between 1954 and 2003, through the visual interpretation of multiple sets of black and white digital stereoscopic aerial photographs at different scales. The analysis reveals locally high frequency of landslide occurrence and built-up areas on existing landslides, either on the body or on the crown areas. In particular, we show that over the years new residential areas were developed despite the presence of large old mass movements. [ABSTRACT FROM AUTHOR]

Rapid and accurate landslide inventory mapping is significant for emergency rescue and post-disaster reconstruction. Nowadays, deep learning methods exhibit excellent performance in supervised landslide detection. However, due to differences between cross-scene images, the performance of existing methods is significantly degraded when directly applied to another scene, which limits the application of rapid landslide inventory mapping. In this study, we propose a novel Domain Style and Feature Adaptation (DSFA) method for cross-scene landslide detection from high spatial resolution images, which can leverage labeled source domain images and unlabeled target domain images to mine robust landslide representations for different scenes. Specifically, we mitigate the large discrepancy between domains at the dataset level and feature level. At the dataset level, we introduce a domain style adaptation strategy to shift landslide styles, which not only bridges the domain gap, but also increases the diversity of landslide samples. At the feature level, adversarial learning and domain distance minimization are integrated to narrow large feature distribution discrepancies for learning domain-invariant information. In addition, to avoid information omission, we improve the U-Net3+ model. Extensive experimental results demonstrate that DSFA has superior detection capability and outperforms other methods, showing its great application potential in unsupervised landslide domain detection. [ABSTRACT FROM AUTHOR]

Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing images (such as U-Net) have received a lot of attention. However, because of the variable shape and texture features of landslides in remote sensing images, the rich spectral features, and the complexity of their surrounding features, landslide extraction using U-Net can lead to problems such as false detection and missed detection. Therefore, this study introduces the channel attention mechanism called the squeeze-and-excitation network (SENet) in the feature fusion part of U-Net; the study also constructs an attention U-Net landside extraction model combining SENet and U-Net, and uses Sentinel-2A remote sensing images for model training and validation. The extraction results are evaluated through different evaluation metrics and compared with those of two models: U-Net and U-Net Backbone (U-Net Without Skip Connection). The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%, which is about 2% and 3% higher than U-Net and U-Net Backbone, respectively, with less false detection and more accurate extraction results. [ABSTRACT FROM AUTHOR]

Spatial slope deformation pattern is crucial for landslide early warning and management. Quantitative modelling of the effects of multiple driving factors on the landslide spatial pattern has not yet been fully explored. To address this, this study utilizes data-driven models and Interferometric Synthetic Aperture Radar (InSAR) techniques to unveil the spatial pattern of landslides based on SUs (slope units). Specifically, we propose a methodology that establishes a correlation between InSAR deformation and topographic/meteorological factors by constructing a Generalized Additive Mixed Model (GAMM). Using this method, we analysed ALOS-2 ascending SAR images from April 2020 to September 2021 and successfully obtained cumulative minimum deformation during the period. Through the GAMM, we linked the observed cumulative minimum deformation in each SU to various factors, including topographic and meteorological factors. In terms of accuracy, there is an excellent agreement between the observed and fitted deformation, with a Pearson Correlation Coefficient (PCC) of 0.83. In terms of interpretability, our method can well explain the contribution of each factor to landslide deformation. We conclude that using the data-driven GAMM and InSAR techniques can effectively reveal the spatial pattern of landslides based on SUs. Furthermore, based on the method proposed in this study, it is possible to predict landslide deformation in the future. [ABSTRACT FROM AUTHOR]

The hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant factors in landslides is intensive rainfall. The present work proposes the use of data generated by weather stations to predict landslides. We give special treatment to precipitation information as the most influential factor and whose data are accumulated in time windows (3, 5, 7, 10, 15, 20, and 30 days) looking for the persistence of meteorological conditions. To optimize the dataset composed of geological, geomorphological, and climatological data, a feature selection process is applied to the meteorological variables. We use filter-based feature ranking and Self-Organizing Map (SOM) with Clustering as supervised and unsupervised machine learning techniques, respectively. This contribution was successfully verified by experimenting with different classification models, improving the test accuracy of the prediction, and obtaining 99.29% for Multilayer Perceptron, 96.80% for Random Forest, and 88.79% for Support Vector Machine. To validate the proposal, a geographical area sensitive to this phenomenon was selected, which is monitored by several meteorological stations. Practical use is a valuable tool for risk management decision making, can help save lives and reduce economic losses. [ABSTRACT FROM AUTHOR]

Landslide scale measurement and hazard risk assessment are crucial, especially since landslide deposits often have an expansion effect, and can lead to more serious secondary hazard events. In this study, we obtained substantial high-resolution terrain and orthophoto data through multiple UAV photography and field surveys. Based on GIS spatial analysis and landslide geometric models, sliding and deposit data for 42 loess landslides in the Heifangtai tableland were calculated. The results show that sliding volume and sliding area have the highest correlation with the power-law distribution ($V_e = 1.204A^{1.1360}$ V e = 1.204 A 1.1360 ). The ratio of $H_{max}/L_{max}$ H max / L max increases with the increase of sliding volume and deposit volume of the landslides, with an average value of 0.306. The ratio of H/L has a higher correlation with deposit volume, and the fitting expression is $H_{max}/L_{max} = 0.187V_D^{-0.123}$ H max / L max = 0.187 V D − 0.123 . The VEC of 42 loess landslides ranged from 1.05 to 2.53, while the AVEC was about 1.503. The correlation analysis show that the VEC is positively correlated with AEC, Hmov, Hmov/Lmax and Lmov/Lmax but negatively correlated with Dave. The essence of the volume expansion effect is that landslide fluidity increases, resulting in looser deposit distribution, larger pore scale in the deposit and lower overall compaction. Loess collapse in the Heifangtai tableland lack protection from the liquefaction of the sliding mass basement, resulting in large VEC values. The semi-ellipsoid model is a valuable approach for landslide volume assessment in the Chinese Loess Plateau, yielding an average error rate of restoring the sliding volume of landslides of 13.55%. This study provides a useful reference for accurate evaluations at the landslide scale. [ABSTRACT FROM AUTHOR]

This paper presents the results of applying GIS–MCA–AHP integrated model in landslide susceptibility assessment for warning of dangerous areas in the Tan Uyen district of Vietnam. The main research method is a Multi-criteria assessment that is based on a hierarchical analysis process and a combination of the GIS spatial analysis model. Research results have shown four main groups of factors causing landslides including hydrogeological factors, topographic and geomorphological factors, surface cover factors and human impact factors. The landslide susceptibility assessment map has identified 22.69% of the area (204.96 km2) with a high and very high risk of landslides. Based on the overlapping population distribution map and landslide susceptibility assessment map. It has been shown that residential areas, including 723 households located at a high risk of landslides need to be moved to a safe area. This is a very significant research direction in a warning and minimizing the risk of landslides in mountainous areas of Vietnam. [ABSTRACT FROM AUTHOR]

Climate change impact on rainfall-induced landslide susceptibility of a certain region is often implied based on expected changes in rainfall patterns and rarely explicitly quantified. This study aims to address this gap by implementing coupled landslide and climate modelling chains to explicitly assess the effects of changing rainfall patterns on rainfall-induced landslide susceptibility. The effects of climate change are integrated into the landslide modelling chain via Intensity-Duration-Frequency (IDF) curves for the present and future climate conditions for a landslide-prone study area located in central Norway. The effects of climate change on landslide susceptibility are examined by using a physical-based landslide prediction model with rainfall events of varying duration and intensity that are simulated based on the climate-dependent IDF curves. The novelty of this study is the proposition of a novel probabilistic framework to assess the climate change impact on landslide susceptibility for rainfall events with a given duration. The proposed framework accounts for both the uncertainties of rainfall events through probabilistic interpretation of IDF curves and the uncertainties in the landslide model with the Monte Carlo method. Compared to results based on only intense rainfall events, the proposed framework leads to a lower increase in the probability of landslide initiation and landslide-susceptible extents due to climate change. [ABSTRACT FROM AUTHOR]

Landslide is a frequent geological hazard in the Three Gorges reservoir area of China. Impulse waves could be created and cause substantial damage if the landslide collides with a water body. A nonlinear shallow water equation in combination with a progressive landslide model is proposed to simulate this catastrophic disaster. The proposed approach is distinguished by rapid calculation, less computational burden, and high practical value. By simulating the 2008 Three Gorges Reservoir Gongjiafang landslide and the subsequent impulse waves, we illustrated and validated this methodology. The initiation and motion processes of the landslide, as well as the generation, propagation, and run-up processes of impulse waves, were thoroughly performed. The simulation results were in good agreement with the field survey data, demonstrating the viability of the proposed approach for simulating the impulse waves triggered by landslide events. This numerical approach is beneficial for engineers to perform a preliminary estimation of landslide-generated waves and further facilitate the prevention and mitigation of geological hazards. [ABSTRACT FROM AUTHOR]

The assessment of landslide hazards is crucial for disaster prevention and mitigation, but it has not considered the dynamic influencing factors that trigger landslides. The timeliness and practical value of the assessment results still need to be further improved. This study constructed a dynamic landslide hazard assessment system using information value model, dynamic precipitation data, and Backpropagation Neural Network (BPNN) model. Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. It is predicted that the percentage of very high and high areas in 2025 and 2030 will be 50.5% and 57.5% respectively. [ABSTRACT FROM AUTHOR]

Traditional landslide susceptibility assessment methods often rely on single models, which can be biased and less accurate. In this article, we introduce a two-tiered strategy to enhance landslide susceptibility predictions. Initially, we employ an ensemble stacking technique that combines the strengths of three machine learning classifiers. This combination leverages the support vector classifier (SVC) as the key meta-classifier to optimize and refine predictions. Subsequently, we integrate the extreme gradient boosting (XGB), random forest (RF) and gradient boosting decision tree (GBDT) models with SVC to create hybrid approaches. In this study, we evaluate and compare the effectiveness of six machine learning algorithms for predicting landslide susceptibility in Anhua County, Hunan Province, China. The results demonstrated that the stacking ensemble model outperforms traditional models. The XBGþSVC model achieves the highest AUC value (0.9468), which is followed by the GBDTþSVC (0.9316), RFþSVC (0.9162), XGB (0.9393), GBDT (0.9009), and RF (0.8693). These findings indicate that stacking machine learning approaches hold promise for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area's disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation's findings mostly agreed with the survey's actual findings. The study's findings demonstrated that: (1) the FCNN model's AUC value was 0.910, while the I-AHP model's AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened. [ABSTRACT FROM AUTHOR]

The periodic storage and release of water in reservoirs have the potential to accelerate deformation or trigger ancient landslides. Interferometric synthetic aperture radar (InSAR) technology, renowned for its wide coverage and high precision, has emerged as an effective technique for the identification and monitoring of landslides. This study utilized spaceborne InSAR technology to identify and interpret landslides in the Liujiangxia reservoir area in China, leading to the detection of a total of 20 landslides. Moreover, among these 20 landslides, the landslide with substantial deformation (LJX02) was chosen, and the wavelet quantitative analysis technique was employed to investigate the correlation and time lag between various elevation positions of this landslide, the reservoir water level, and rainfall. The study successfully demonstrated that the deformation of the landslide lagged behind the reservoir water level by 152.10-182.50 days (from low to high elevation) and was delayed by 45.60-68.44 days compared to rainfall (from high to low elevation). [ABSTRACT FROM AUTHOR]

Landslide inventory incompleteness (LII) may significantly affect the model performance in landslide susceptibility mapping (LSM). However, traditional methods, including heuristic, statistical and deterministic models, cannot address LII issue. In this work, we introduce a novel hybrid LEO-MAHP model, blending landslide frequency, empirical adjustments, optimization functions, and multi-participated analytic hierarchy process to address it by taking Badong County as the study area. This hybrid model mitigates the drawbacks of data-heavy statistical approaches and subjective heuristic models by incorporating LII into weight determination. The findings show that the LEO-MAHP model demonstrates superior performance (AUROC = 0.809 and 0.805) over conventional statistical (AUROC = 0.714 and 0.770) and heuristic models (AUROC = 0.738 and 0.741) across different LII levels. We further discuss alternative LII solutions, proposing an updated landslide management strategy that accounts for climate change and human activities. Our findings underscore the necessity of evaluating LII before applying statistical or machine learning methods in LSM. List of Abbreviations: AHP: Analytic hierarchy process; AUROC: Area under receiver operating characteristic curve; CR: Consistency ratio; CI: Consistency index; CF: Conditional factor; DEM: Digital elevation model; FN: False negative; FP: False positive; FPR: False positive rate; InSAR: Interferometric Synthetic Aperture Radar; IV: Information value; LD: Landslide density; LII: Landslide inventory incompleteness; LS: Landside susceptibility; LSI: Landslide susceptibility index; LSM: Landslide susceptibility mapping; MAHP: Multi-participated analytic hierarchy process; MC: Model construction; MCDA: Multi-criteria decision analysis; MV: Model verification; MPP: Model prediction performance; ORS: Optical remote sensing; P&C: Prevention and control; PGA: peak ground acceleration; ROC: Receiver operating characteristic; RI: Random index; TN: True negative; TP: True positive; TPR: True positive rate. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has achieved a remarkable place in solving complex problems in almost all disciplines. Based on the recent notable performances of machine learning and deep learning techniques for rapid and automatic landslide identifcation, it is observed that availability of quality training data, proper model training and associated cost are crucial for developing such frameworks. Therefore, the primary objective of the study is to propose a novel empirical algorithm, DvD, for rapid landslide identification using Sentinel-2 imagery and comparatively evaluate its performance to a deep learning architecture popularly used in feature extraction problems, binary semantic segmentation U-NET (BSS-UNET) framework. The empirical method has been investigated over a dataset diverse in topography and land cover to evaluate its efficacy. The proposed BSS-UNET framework is trained on the landslide database provided by the Institute of Advance Research in Artificial Intelligence (IARAI) in Landslide4Sense 2022 challenge which achieved a high mIoU value of 0.78 with 84.23% precision, 65% recall and 73.32 F1-score. The DvD algorithm outperformed the BSS-UNET framework and achieved 0.80 mIoU when applied to the IARAI dataset. The proposed empirical method has the potential to serve as large-scale rapid landslide inventory preparation subject to the availability of cloud-free satellite imagery. [ABSTRACT FROM AUTHOR]

The mountain state of Uttarakhand constitutes a part of the North-western Indian Himalayan region and is inherently vulnerable to natural disasters. It is witnessing faster melting of glaciers and increased occurrences of disasters like cloudbursts, flash floods, landslides, landslide/glacial lake outburst floods, rock–ice avalanches, and debris flows. This study provides a comprehensive assessment of the influence of changing climate on the occurrence of natural hazards. It assesses the status of two major rapidly developing infrastructures in the state (viz., hydropower plants and transportation network) under climate variability. Detailed causative analysis of two major disaster events (in Kedarnath and Chamoli in 2013 and 2021, respectively) and associated destruction is presented to understand the vulnerability of infrastructure and communities to hazards. Based on the challenges identified, a unique two-fold framework is proposed, and recommendations are articulated for building resilient and sustainable mountain infrastructures and communities in the region. [ABSTRACT FROM AUTHOR]

Loess landslide is one of the most harmful and serious geological hazards in the Loess Plateau of China. Early identification of potential loess landslide is an urgent need for its prevention. Traditional methods, e.g. support vector machines and decision trees, often suffer complicated data pre-processing, multitudinous causative factors, or low accuracy. This study aims to develop a high-performance loess landslide early identification model based on convolutional neural networks (CNNs). A case study was carried out in northwest Lvliang, China, where loess landslide is a major concern. Two hundred and six loess landslide cases were interpreted by comparing remote sensing images of two time phases, and were randomly divided into a training set (80%; 165) and a validation set (20%; 41). Four algorithms were developed, including a CNN structure with skip connection using data with (S–C) or without (S–N) slope crest and plain CNN structure using data with (P–C) or without (P–N) slope crest. The results show that the S–C structure is the most suitable for early identification of potential loess landslides because it achieved the highest overall accuracy (OA = 0.902) and largest area under the receiver operating characteristic curve (AUC = 0.932) on the validation set. [ABSTRACT FROM AUTHOR]