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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Submarine pipelines are likely to be impacted by frequently catastrophic landslides, resulting in broken pipelines and causing irreversible economic losses; however, because pipelines located in deep-sea environments are simply laid atop the seabed, effective protective measures have not yet been proposed. In this paper, a pipeline with a uniform distribution of honeycomb holes on the surface, namely, a honeycomb-hole submarine pipeline, is discussed and optimized in detail. A computational fluid dynamics (CFD) approach is verified and improved several times and then applied to conduct 120 numerical simulations for 12 types of suspended submarine pipelines under 10 conditions covering the Reynolds number range required by the project conditions. First, the typical characteristics of the impact forces acting on pipelines in a seawater environment are systematically investigated, and the peak drag force is isolated as the most dangerous load caused by a landslide impact. Second, according to the formation and reduction mechanism of the drag force, it is proposed that a honeycomb-hole submarine pipeline can effectively delay the separation of the boundary layer, reduce the impact velocity, prolong the arrival time, and decrease the differential pressure drag force. Finally, two normalized parameters, the depth ratio and area ratio, are established to develop an optimization design methodology, and the optimal design values of honeycomb-hole pipelines are achieved under the support of limited data. This work presents a protective technology for submarine pipelines and provides new insights into the response of fluid-structure interactions. [ABSTRACT FROM AUTHOR]

Geomorphological mapping can be described as a group of techniques employed to systematically record the morphology of the ground, landforms, landscape-forming processes and materials that constitute the surface of the Earth (Griffiths et al., [13]; Lee, [16]). In the paper by [10], the "geo-historical" map is described as a synthesis of historical and geomorphological data for landslides and landslide occurrence in the Ceno Torrent valley (Northern Apennines, Italy). The knowledge of flood-induced ground effects and flood-water dynamics is underlined as a crucial issue for hydro-geomorphic hazard assessment and mapping. This special issue presents contributions about the methodologies and tools for representing and mapping geomorphological hazards, with a focus upon hazard and risk classification and tools for land planning, risk reduction and mitigation. [Extracted from the article]

In this paper, a geomorphological map of Pescara del Tronto area (Sibillini Mts, Marche Region) is presented. The work focuses on the geomorphological analysis performed in a zone strongly struck by the 2016–2017 seismic sequence of Central Apennines. The geomorphological map (1:7,500 scale) was obtained through an integrated approach that incorporates geological-geomorphological field mapping and geomorphological profile drawing, supported by air-photo interpretation and GIS analysis. The main purpose of the work is to describe a geomorphological approach for representing and mapping the evidence of several debris flows and landslides recognized in the framework of seismic microzonation (SM) activities. Finally, in order to elevate geomorphological maps into effective tools for land management and risk reduction, it could provide a scientific and methodological basis to demonstrate that accurate mapping provides important information, readily available for local administrations and decision-makers, for the implementation of sustainable territorial planning and loss-reduction measures. [ABSTRACT FROM AUTHOR]

In this paper we present geomorphological hazard mapping of the San Vito Peninsula offshore (Sicilian margin of Tyrrhenian Sea), characterised by a very narrow continental shelf and a very dipping, tectonically active continental slope, no far from a seismogenic belt. The data set consists of morpho-bathymetric models achieved by means of multibeam survey and scattered high resolution seismic profiles. The morpho-bathymetric study highlighted some potential sites of geomorphological hazards, the most representative of which are: (i) two canyons developing from the upper slope off San Vito Cape down to the Erice basin; (ii) a dense network of small incisions across the upper slope; (iii) a very active landslide, developed onland and extending in the continental shelf close to the Scopello village. The geomorphological features that can generate submarine hazard are represented by: unstable sediment packages into canyon heads or along the upper slope; submarine canyons with regressive erosion; coastal landslides. [ABSTRACT FROM AUTHOR]

In landslide susceptibility modeling, the selection of the mapping units is a very relevant topic both in terms of geomorphological adequacy and suitability of the models and final maps. In this paper, a test to integrate pixels and slope units is presented. MARS (Multivariate Adaptive Regression Splines) modeling was applied to assess landslide susceptibility based on a 12 predictors and a 1608 cases database. A pixel-based model was prepared and the scores zoned into 10 different types of slope units, obtained by differently combining two half-basin (HB) and four landform classification (LCL) coverages. The predictive performance of the 10 models were then compared to select the best performing one, whose prediction image was finally modified to consider also the propagation stage. The results attest integrating HB with LCL as more performing than using simple HB classification, with a very limited loss in predictive performance with respect to the pixel-based model. [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]

Although the atmospheric compensation models developed to date have been generally robust and effective for InSAR, how to choose the right atmospheric correction method for ground-based InSAR is worth studying. This paper uses different methods for atmospheric phase screen (APS) compensation based on studies of ground-based radar (GBR) to zero-baseline acquisitions over the Woda landslide in Sichuan, China. This landslide has a steep topography, thick fog, turbulent rivers and strong rains. Data were acquired at the Ku-band using a GAMMA Portable Radar Interferometer II (GPRI-II) in multiple campaigns (4 July 2019–13 July 2019). In 2019, the Miyun open-pit mine was also studied; the maximum deformation reached 4.03 mm/hour. The collected data were processed within one unit based on the small baseline set (SBAS) approach. The experimental results show the following (1) For long-distance monitoring with complex atmospheric disturbances, range- and/or height-dependent models fail. (2) The iterative decomposition (ITD) method can effectively address complex atmospheric disturbances. (3) If the threshold of wavelength is set to be large, the ITD method becomes a stratified model. [ABSTRACT FROM AUTHOR]