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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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

Landslides are major natural disasters in mountainous areas, often caused by earthquakes and heavy rainfalls. Traditional manual delineation methods for identifying landslide features using optical imagery are inefficient, highlighting the need for automated detection techniques. Deep Convolutional Neural Networks (CNNs) have emerged as advanced solutions in computer vision for this purpose. Despite the reliance on pre-event and post-event imagery or various data sources like digital elevation models (DEMs), the success of deep learning models largely depends on the quality and availability of training data. This poses a challenge for their immediate application after a landslide. This study explores the transferability of a CNN model trained on data from the 2016 Kumamoto Earthquakes for detecting landslides in different events, specifically the 2018 Hokkaido earthquake and the 2017 Asakura Rainfall in Japan. These cases were chosen for their geographical similarities. The proposed deep transfer learning model, based on a DeepLabV3 + architecture built on a pre-trained ResNet50, automatically identifies landslide features without needing specific training data or model adjustments for each event. It achieved high accuracy in both cases, demonstrating CNNs' potential for broad application in landslide detection and enhancing disaster response efforts. [ABSTRACT FROM AUTHOR]

An effiecient landslide displacement prediction is important for early warning system of landslides. Based on time series method, the cumulative deformation of a landslide is decomposed into periodic and trend ones. A cubic polynomial is employed to forecast the trend deformation. Considering the periodic changes in rainwater and reservoir levels, the proposed model combines a convolutional neural network (CNN) with a gated recurrent unit (GRU) neural network to forecast periodic deformations. CNN effectively identifies the characteristics of the raw data, and GRU automatically controls the impact of historical information by adjusting the weights of the reset and update gates. C-GRU performance in predicting the periodic displacement is compared with GRU, and a backpropagation neural network optimised using particle swarm optimisation (PSO-BP). Monitoring points of Baishuihe landslide are employed to compare the performance of the various models. The findings show that the proposed model has strong data mining performance and deals with time series data efficiently. The new model can incorporate historical information more effectively than PSO-BP. Compared with GRU, the proposed model better captures the input data characteristics and improves the prediction accuracy. C-GRU achieves a low mean square error, representing a significant improvement in the accuracy of landslide predictions. [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]

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]

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]

We present and experimentally demonstrate a seismic ambient noise monitoring optical fibre sensor for early warning detection of sinkholes. The developed optical fibre sensor is designed for warning alert of subsidence and cover collapse sinkholes. The progressive process of sinkhole development causes structural change in the subterranean surface. The impact of this change and its influence on the subsurface acoustic modes was detected in the form of variations in the spectral content of the ambient noise signals monitored in the subsurface. Structural surface integrity was monitored through frequency response as the void increased. Vibrational states relating to unsteady structural conditions were identified. Significant instability events were captured giving timely warnings before collapse. The polarisation based single mode fibre sensor and monitoring method is proposed for implementation in a phase sensitive distributed acoustic sensor setup. Peak frequencies in the micro-seismic noise band of 0.1 Hz to 1.0Hz were observed through cavity development and growth. Extended peak frequency shifts and bandwidth in the band >1Hz were recorded, indicating weakness and imminence of collapse. Early warning detection by the structural field model was achieved prior to the sudden subsurface failure which results in collapse sinkholes. By monitoring variations in the vibrating frequency modes when a subsurface cavity develops within the structure, trigger events and collapse precursor conditions are identified. We have successfully demonstrated an early response warning annunciator by using an algorithm to analyse combinational characteristics of the spectral components of the detected signals. The fibre sensor reduces the risk and socio-economic impact of infrastructural damage due to sudden collapse of sinkholes and has extended potential of monitoring earthquakes and landslides. [ABSTRACT FROM AUTHOR]

The north-eastern border of CA represents the accretionary system developed during its continuous collision with Apulian block. The tardive oblique tectonic component is highlighted by pervasive NW-SE crustal transpressive left-lateral strike slip faults developed from the Upper Miocene to Quaternary tectonic stages. The seismotectonic and landslides Main Map represents the update of the main faults, the style of their arrangement and kinematics, the seismotectonic features and landslide area distribution with risk implications. Seismotectonics allows us to recognise a vertical crustal zonation with the distribution of earthquakes and their focal mechanism solution. The prevalent modality of ruptures depicts a mainly transcurrent and compressive solution in mid-lower crust portions respectively. This data, together with the historical seismicity analysis of the area, lead to consider the NW-SE strike-slip faults improved on the Main Map, as recent and tectonically active. The areal distribution of landslide and susceptibility of the area is due to the interaction between seismotectonic peculiarity, lithological typologies and fault zone architectures. Landslides and flood phenomena of this sector involve many municipalities, major and minor infrastructures and economic and social activities. The landslides and the faults have been identified, mapped and classified, originally at detail scale and, then, represented at 1:50,000 scale in the Main Map, included as supplementary material. The geo-structural and geomorphological data were analysed in a geographic information system allowing data management and implementation. The work presents an updated knowledge framework of risk conditions of the study zone, available to plan and reduce the fundamental elements that determine the landslide and seismic risks in this region. [ABSTRACT FROM AUTHOR]