Search

Landslide inventories provide the knowledge basis for many geomorphological applications and also planning and emergency management. Detailed landslide inventories should also be prepared where pre-existing inventories are available, as knowledge updates. In this paper, we present a new geomorphological landslide inventory for an area of the High Agri Valley, Southern Italian Apennines. The map was prepared through systematic interpretation of historical aerial photographs testing extensive use of anaglyph glasses in StereoPhoto Maker freeware. A total of 2124 landslides were classified based on the type of movement, estimated depth, estimated relative age and three levels of uncertainty, providing landslide attributes and map constraints useful for land planning and hazard studies. The map also documents the relationships between landslides and fluvial landforms of different generations, recording important information to investigate the geomorphological evolution of the area further. We expect that landslide mapping in similar environments will benefit from the workflow here presented. [ABSTRACT FROM AUTHOR]

Detailed surface topography is important when analyzing the stability of slopes. Recent advances in new technologies such as interferometric synthetic aperture radar and light detection and ranging have allowed us to obtain high-precision profiles of distant landscapes and objects including detailed slope information for three-dimensional (3D) slope stability analysis. However, techniques of reconstructing 3D numerical models from scanned data of slope geometry have not been well investigated or tested. This paper proposes a comprehensive approach that integrates laser scanning and finite element method for slope stability analysis, particularly failure prediction under precipitation scenarios. The methodology is applied to a slope in the Wenchuan earthquake-stricken mountainous region that failed in 2013, triggered by severe rainfall. The modelling results show that the surface sampling resolution can affect the prediction accuracy of the potential failure zones. When constructing the slope model, the selected surface grid should be fine enough to capture the important topographic features of the slope while minimizing computation demand. This paper confirms that the proposed method can be successfully used to identify the potential failure zones of the investigated slope under severe rainfall conditions. [ABSTRACT FROM AUTHOR]

Understanding and assessing the landslides is immensely important to scientists and policy-makers alike. Remote sensing conventional methods and modelling approaches in geographical information system (GIS) tend to be limited to authentic quality and spatial coverage. This study aims to identify challenges and quality of landslides assessment based on remotely sensed data by the mean of existing works of the literature and practices we attempted in the Zagros and Alborz Mountains in Iran and the red rock shield Lake, China. Remote sensing data for landslides investigations require a high-resolution digital elevation model (DEM) from either aerial photography, satellite images, airborne laser scanning (ALS) or terrestrial Light Detection and Ranging (LiDAR) derived in order to enable a reliable and valid output performance. This paper presents weaknesses and strengths of the existing remote sensing techniques in the last decades and further provides recommendations for a reliable approach to the future landslide studies. Also, this study estimates the operational use of state-of-the-art technologies (i.e. unmanned airborne vehicle (UAV)) for landslides assessment in the near future that is a realistic ambition if we can continue to build on recent achievements. However, this paper does not deliver a detailed methodology of a DEM generation from the remote sensing approach for landslides assessment. [ABSTRACT FROM AUTHOR]

Landslides represent one of the most frequent geodynamic phenomena in Slovakia. This contribution focuses on the relations of the selected landscape attributes and landslides in flysch area of the Western Carpathians. We presented three statistical methods of assessing and making a prognosis of the landslide hazard: multivariate, factor and zonal analysis. These methods complement each other and their aim is to analyse and recognize natural processes, which trigger the landslides in a more complex way. With multivariate analysis, we evaluated mutual interactions between the input attributes and determined their level of impact on landslide hazard. Using factor analysis, we extracted latent factors with the highest factor loads and identified the main factors that could trigger landslides. Areal extension of landslides in individual landscape attributes was expressed by zonal analysis, based on the superposition of the individual vector layers. Our evaluation shows that the slope predisposition to landslides is predominantly given by the geological, hydrogeological and morphological conditions. The proposed landslide risk assessment procedure allows for the functional sustainable development of the landscape development planning documents. The results presented in this paper are relevant for the area formed by flysch rock (Alps and Carpathians), but also for areas where clastic sediments alternate rhythmically. [ABSTRACT FROM AUTHOR]

The primary objective of these studies was to quantitatively assess the ground deformation velocities and rates and their natural and man-made controlling factors as the potential risks along the seismically active 70 km section of buried Baku-Tbilisi-Ceyhan Oil, South Caucasus Gas, Western Route Oil and South Caucasus Pipeline Expansion Gas pipelines in Azerbaijan using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique. PS-InSAR analysis showed that the continuous subsidence was prevailing in the kilometer range of 13-70 of pipelines crossing two active seismic faults. The ground uplift deformations were observed in the pipeline kilometer range of 0-13. The minimum and maximum vertical ground movement velocities were observed to be −21.3 mm/y and 14.1 mm/y along 70 km section of pipelines with 250 m buffer zone. Both of these sites were observed at the range of seismic faults. The spatial distribution of sites with ground deformation velocity less than −15 mm/y and more than 15 mm/y was diverse and random all along 70 km of pipelines without any cumulative spatial patterns. Based on the lower mean, variation and standard deviation of pixel values, the seismic fault in the kilometer range of 21-31 revealed its higher vulnerability to subsidence processes rather than the Seismic Fault in the kilometer range of 46-54. The ground deformation velocities within the range of Seismic Fault KP21-31 revealed the minimum and maximum values of −19.74 mm/y and 14.1 mm/y, respectively whereas at the Seismic Fault KP46-54, the minimum and maximum values were −17.07 mm/y and 9.29 mm/y, respectively. Encouraging level of agreement with the regression coefficients of 0.92 and 0.96 for known subsiding sites at KP28 + 500 and KP52 + 750 and 0.97 and 0.96 for known uplifting sites at KP04 + 900 and KP35 + 050 respectively was observed between the high-precision GPS and PS-InSAR measurements. The diverse spatial distribution and variation of ground movement processes along pipelines demonstrated that general geological and geotechnical understanding of the study area is not sufficient to find and mitigate all the critical sites of subsidence and uplifts for the pipeline operators. The prediction of the potential subsidence or uplift locations based on the field visual verifications holds a lot of uncertainties without broad and detailed scale airborne and satellite space observation technologies. The justification of the budget for the geotechnical maintenance activities along long-range oil and gas pipelines requires sophisticated prioritization and planning of the remediation sites and clear quantitative and qualitative risk assessment proving the activeness of these sites and effectiveness of the remediation measures. This means that the PS-InSAR – based approach outlined in this paper is a significant improvement over current ground-based monitoring practices or can significantly contribute them in the initial phase of risk assessment and prioritization. [ABSTRACT FROM AUTHOR]

Frequent rockfall events at the Huashiya cliff near the dam site of Jinsha Hydropower Station poses great threats to construction projects along the Jinsha River. In order to protect the construction area, a comprehensive analysis of rockfall hazard was carried out including field survey, numerical simulation, quantitative assessment and mitigation strategy. High resolution Digital Terrain Model (DTM) was created by Terrestrial Laser Scanning (TLS), then used for the identification of rockfall sources and simulation of rockfall run-out behaviours. Approximately 60 rockfall simulations were conducted to analyze the interaction between rockfall block and protection nets. Quantitative assessment of rockfall hazard was then made to visualize the distribution and hazard of rockfall. The difference between the 60 rockfall simulations was quantified through Rockfall Interception Ratio (RIR) and the Energy Ratio of Overflying Rockfall (EROR). By integrating RIR, EROR and cost, the performance of protection measures were quantitatively assessed. Then, an optimization strategy was proposed to improve the cost-efficiency of the protection system. At the end of this paper, some problems that can be encountered when creating DTM and hazard maps were discussed, and some advices for designing passive protection system were introduced. [ABSTRACT FROM AUTHOR]

Flood verification and mitigation based on ground survey has been impeded by many factors. For instance, damage assessment exclusively by onsite expedition could be delayed by restricted access, especially during heavy flooding. To devise appropriate mitigation measures, resources required to thoroughly investigate the incidents in response to disaster could be overwhelming. Moreover, inadequate equipment and non-standardized flood reporting process could also undermine the maneuver. As a consequent, rescue mission was inevitably deferred until the reported incidents had been confirmed. To address these issues, this paper presents an internetworking system for assisting flooding disaster mitigation. Its main contributions were two folds. Firstly, its data intelligent was driven by convergence of both user and official reports of flood incidents and those automatically detected from remotely sensed images. Secondly, the reliability of reported information was ensured by two-factor verification, i.e. by using a state-of-the-art deep learning strategy and by official investigations. The verified incidents were then presented in augmented graphical reports, which were practical in public inquiry and official uses in devising efficient and cost-effective disaster mitigation measures. The developed system was deployed in different cities, most affected by recent floods. Numerical and subjective assessments herein demonstrate the merits of the system. [ABSTRACT FROM AUTHOR]

The Jiuzhaigou earthquake on 8 August 2017, led to a significant increase in the amount of loose, solid material, which has been a rich source of debris-flow activity. In this paper, a comprehensive debris-flow prevention and control system consisting of stabilization, blocking, and deposition structures is proposed based on the topographic features of the debris-flow gullies in the Jiuzhaigou Valley. Taking the Xiajijie Lake gully as a case study, the history and formation of debris-flow disasters were analysed. This study proposes the main principles of debris-flow prevention and control as well as implementation measures, consisting of lateral diversion and deposition at the outlet of the gully. In addition, several environmental mitigation measures are proposed. Galvanized wire gabions can be placed in front of trees to lessen the debris-flow impact force, and bushes can be planted in front of deposition retaining walls to increase their aesthetic value. Alternatively, steel structures can be constructed on deposition retaining walls and filled with nutrient soil, and grass can be planted in these structures for an enhanced aesthetic appeal. [ABSTRACT FROM AUTHOR]

Since all available surrounding rock classification approaches don't consider the size effect of the tunnel excavation span and unfavourable geologic bodies, it's impossible to evaluate the systematic stability of the surrounding rock by rock mass classification alone. In addition, the evaluation results usually have poor robustness because of the uncertainty of structural information. In this paper, the actual distribution information and probability distribution model of the joints in tunnel face are obtained. Many random numbers that meet the probabilistic distribution models can be produced by Monte carlo simulation, and many random rating values of these evaluation indices are also obtained. The distribution probability affiliated with different rock mass levels can be attained by inductive statistics, and a robust evaluation of surrounding rock classification is carried out. The deformation and failure process of surrounding rock are simulated based on the discontinuous deformation analysis. A comparison analysis of removable blocks and their distribution characteristics is carried out by UJN. The most likely damaged parts and rockfall styles are indicated, and a comparison between calculation result and practical collapse of Laohushan tunnel is carried out, which are nearly the same. The research results offer practical guidance for field construction and supporting optimization. [ABSTRACT FROM AUTHOR]

Multiple reactivation of a large landslide might dam river course at the same site in different periods, which would exert long-term and considerable influence on society and environment. In this paper, we consider such successive damming episodes as an integrated system and take the 2018 Baige event due to successive landslides as an example to explore the hazard characteristics of successive landslide-dammed lakes. Based on the river valley conditions at dam site before the first and second blockage, the height of dams formed by landslides with different volumes is estimated, and then the stability and peak discharge of these landslide dams are evaluated. The calculation results show that the hazard of the dammed lake triggered by subsequent slide event in the successive landslides could increase significantly. The cause of such hazard increasement of the subsequent landslide dam is also discussed. The experience obtained in this case study can facilitate better understanding the hazard characteristics of similar landslide-dammed lakes, as well as planning engineered controls in advance in order to decease the hazard of dammed lake formed by the potential subsequent landslide. [ABSTRACT FROM AUTHOR]

Risk assessment and management of an individual landslide is the focus and difficulty. This paper analyzes the landslide losses in China from 1995 to 2018, combined with the ALARP approach, to research the risk acceptable model of landslide in China. Meanwhile, the Shiyantan landslide in Mayang county of Hunan Province is exemplified to quantitatively calculate the landslide risks under different conditions, and based on the risk acceptance level to judge whether the risk value is acceptable. The results show that the individual life risk acceptable criteria for Chinese landslide is 5.36 × 10−6/a, the upper and lower limits of the societal life risk (F-N curve) are 10−7/a and 10−8/a, the societal economic risk (F-D curve) are 10−1/a and 10−3/a. In addition, for the risk assessment of Shiyantan landslide, it is shown that the landslide may be damaged under extreme rainfall conditions. Its economic risk is tolerable risk and life risk is unacceptable risk. It is urgent to formulate relevant measures to mitigate the risk. The results can provide a reference for risk assessment and management conducted in other regions and offer a theoretical basis for early warning and forecast of landslide. [ABSTRACT FROM AUTHOR]

Physically-based distributed models are implemented for landslide susceptibility and hazard assessment around the world. Probabilistic methodologies are considered appropriate to study and quantify the uncertainties derived from the input parameters of these models. In this paper, three sets of Monte Carlo simulations, each one with 10,000 iterations, were applied for a slope stability analysis in a small basin of Envigado (Colombia), using the TRIGRS model, to characterise the uncertainty in the landslide assessment. Different parameters to determine the minimum number of realizations required to ensure a small variation in the failure probability were proposed and analyzed. The quality of the landslide susceptibility assessment was studied. Unexpected and probably erroneous results that may be common in the maps generated using this and other similar methodologies were identified and explained. Additionally, the distribution of the factor of safety was calculated for different grid cells of the basin, showing that the probability density function with the best adjustment to the frequency histogram of the factor of safety can vary between grid cells. The assumption of a normal distribution for the factor of safety would be inappropriate and would lead to miscalculations in this case study. [ABSTRACT FROM AUTHOR]

This paper presents the potential of airborne LiDAR digital terrain model (DTM) for engineering geological mapping in geologically complex and forested area. The multipurpose, comprehensive engineering geological map is created for the pilot area (16.75 km2) located in the Vinodol Valley, Croatia. Eight topographic datasets were derived from 1-m DTM and visually interpreted to identify lithologies and geomorphological processes. In total, 12 engineering geological units, more than 500 landslides, and gully erosion phenomena are outlined in the pilot area. Results confirmed the greatest potential of visual interpretation of LiDAR derivatives for mapping of geomorphological processes in a large scale. On the other hand, this method allowed identification and mapping of engineering formations that are basic engineering geological units appropriate for the medium-scale engineering geological maps. The produced map represents a valuable tool for a wide range of planning and engineering purposes, as well as for geological hazard and risk assessment. [ABSTRACT FROM AUTHOR]

Landslides are one of several natural hazards. As other natural hazards, landslides are difficult to predict, and their forecasts are uncertain. The uncertainty depends on the poor understanding of the phenomena that control the slope failures, and on the inherent complexity and chaotic nature of the landslides. This is similar to other natural hazards, including hurricanes, earthquakes, volcanic eruptions, floods, and droughts. Due to the severe impact of landslides on the population, the environment, and the economy, forecasting landslides is of scientific interest and of societal relevance, and scientists attempting to forecast landslides face known and new problems intrinsic to the multifaceted interactions between science, decision-making, and the society. The problems include deciding on the authority and reliability of individual scientists and groups of scientists, and evaluating the performances of individual scientists, research teams, and their institutions. Related problems lay in the increasing subordination of research scientists to politics and decision-makers, and in the conceptual and operational models currently used to organize and pay for research, based on apparently objective criteria and metrics, considering science as any other human endeavor, and favoring science that produces results of direct and immediate application. The paper argues that the consequences of these problems have not been considered fully. [ABSTRACT FROM AUTHOR]

The critical stage in the evaluation of rainfall-induced landslide failure is in formulating reasonable models to better simulate spatiotemporal changes of slopes in the hilly terrains. A physically based model can take into account the contribution of rainfall infiltration and shear strength of saturated soil layer, and therefore help revealing the landslide formation mechanisms. This paper presents a physically based approach to simulate the landslide process triggered by rainstorm. On the basis of previous solutions, we select the simplified infiltration model Slope-Infiltration-Distributed Equilibrium (SLIDE) to illustrate the dynamical relations between factor of safety (FS) and accumulation of rainfall over time. This model is tested with three representative landslide events in the southwest, southeast, and south central of China during rainstorm. Results show that the time of landslide failure predicted from the SLIDE model is consistent with the reality. Meanwhile, this paper illustrates the differences of FS among the different slope gradients in the vicinity of same soil texture and relationship between FS and rainfall accumulation. This work formulates a methodology of rainstorm-induced landslide evaluation and improves upon the existing landslide prediction methods. [ABSTRACT FROM AUTHOR]

Landslides are a significant geohazard, which frequently result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping using GIS and remote sensing can help communities prepare for these damaging events. Current mapping efforts utilize a wide variety of techniques and consider multiple factors. Unfortunately, each study is relatively independent of others in the applied technique and factors considered, resulting in inconsistencies. Further, input data quality often varies in terms of source, data collection, and generation, leading to uncertainty. This paper investigates if lidar-derived data-sets (slope, slope roughness, terrain roughness, stream power index, and compound topographic index) can be used for predictive mapping without other landslide conditioning factors. This paper also assesses the differences in landslide susceptibility mapping using several, widely used statistical techniques. Landslide susceptibility maps were produced from the aforementioned lidar-derived data-sets for a small study area in Oregon using six representative statistical techniques. Most notably, results show that only a few factors were necessary to produce satisfactory maps with high predictive capability (area under the curve >0.7). The sole use of lidar digital elevation models and their derivatives can be used for landslide mapping using most statistical techniques without requiring additional detailed data-sets that are often difficult to obtain or of lower quality. [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]

The paper presents a viscoplastic formulation including damage for creep and creep rupture. The model incorporates a damage evolution law which includes both the viscoplastic strain rate as well as stress rate. Primary, secondary and tertiary creep are incorporated in this formulation. Model parameters are obtained for polycarbonate using both creep test and the constant strain rate test data. It is found that the model can give a reasonable predictions for creep and creep rupture. However the model will need to be modified for the constant strain rate case. The paper also presents a multi-axial formulation for the model. The formulation is general enough so we will be able to apply it to a complex problem of landslides in soil masses which is stated in the paper as our plan for future research. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present a seismic risk assessment model for transportation network systems that includes the hazard exposure from ground shaking, liquefaction and landslides. Previous studies consider the risk from physical damage and loss of functionality of the network system due to a single scenario earthquake. The current paper extends these methods to include all possible events for complete risk curve estimation. Functionality loss is presented in terms of lack of connectivity, reduction in traffic flow and increase in travel time or length. The risk is provided in terms of the annual rate of exceeding monetary loss. The model is applied to the transportation network of five counties in the San Francisco Bay Area. The annual risk curve is generated based on the rate of occurrence of possible events and their corresponding loss. The loss due to commuter traffic delay is found to be higher than that from bridges damage. [ABSTRACT FROM AUTHOR]

Location of failure slip surfaces plays a critical role in landslide risk assessment and mitigation, particularly for unstable slopes, because it is a key input to design of stabilisation measures for unstable slopes and it determines the volume of the sliding soil mass (i.e. landslide consequence). The failure slip surfaces in the numerical analysis (e.g. finite element/different method, FEM/FDM) are often identified using shear strength reduction (SSR) method. A careful examination of FEM results showed that, although the SSR method performs well for stable slopes, it might provide misleading results for unstable slopes. To properly locate failure slip surfaces for unstable slopes, this paper presents a particle-based numerical method called smoothed particle hydrodynamics (SPH), which is mesh-free, immune to the mesh distortion problem in FEM/FDM, and able to directly simulate large deformation of soils that occurs during landslides. A series of slope stability analyses is performed using an in-house SPH programme. Failure slip surfaces are properly identified by SPH for both stable and unstable slopes. Furthermore, because SPH provides a spatial distribution of the post-landslide large displacement of soils, the failure slip surfaces can be identified conveniently using soil displacement. A displacement-based criterion is proposed to locate the failure slip surfaces. [ABSTRACT FROM AUTHOR]

Landslide mapping is the primary step for landslide investigation and prevention. At present, both the accuracy and the degree of automation of landslide mapping with remote sensing (LMRS) are still lower than those of general remote sensing classification. In order to improve the performance, previous attempts have been made to develop new features, classifiers, and rules, whereas few studies have investigated the in-depth causes and the corresponding solutions. In this paper, after reviewing the related literature, some of the fundamental difficulties hindering the improvement of LMRS are disclosed and discussed. Firstly, landslides do not have distinguishable spectral, spatial, or temporal characteristics, as they may actually be covered by other land covers. Secondly, the surface features of a landslide can vary greatly, affected by the different geological factors, geomorphological factors, hydrological factors, weather conditions, and other factors. Thirdly, the differences in the surface features are often remarkable and nonnegligible, and thus it is difficult to identify a landslide with only a few simple criteria. Finally, some solutions to the above difficulties are suggested. It is expected that the accuracy and applicability of LMRS could be greatly improved, by exploiting big data, utilizing the deep learning technique, and modelling the surface spatial structure of the landslide. [ABSTRACT FROM AUTHOR]

Inherent complex topography and drastic weather patterns together have concocted various natural disasters worldwide. In difficult terrains such as those prevalent in the North-Eastern regions of India, coupled with the factors such as population explosion and improper land use, lead them to witness some of the world's most drastic landslides with an astonishing frequency, reckoning landslide susceptibility assessment crucial in such regions. This paper focuses on exploring a promising machine learning ensemble technique of Majority-based voting which has seldom been employed for landslide susceptibility assessment. The ensemble comprises Logistic Regression (LR), Gradient Boosted Decision Trees (GBDT) and Voting Feature Interval (VFI) to prepare landslide susceptibility zonation maps for the Brahmaputra valley region (Assam & Nagaland) and its close vicinity. In the first stage of the study, a landslide inventory for the area comprising 436 landslide locations was prepared in geographic information system (GIS), substantiated by news reports and remote sensing data. In the second stage, 16 landslide causative thematic maps including Elevation, Slope, Slope Aspect, General Curvature, Plan Curvature, Profile Curvature, Surface Roughness, Topographic Wetness Index (TWI), Stream Power Index (SPI), Slope Length, Normalized Difference Vegetation Index (NDVI), Land Use/Land Cover (LULC), Distance from Roads, Rivers, Faults and Railways were prepared. In the third stage, the landslide inventory was annexed with the causative factor maps to obtain a dataset comprising coordinates of the locations and the values of aforementioned causative factors on the corresponding coordinates. The proposed model was then trained and tested on the prepared dataset (70%:30% split). Finally, the efficiency of the new model was tested using the area under receiver operating characteristic curve (AUC of ROC). The validation results demonstrate the mettle of the proposed majority-based voting ensemble LR-GBDT-VFI (AUC: 0.98) against the conventional techniques such as Decision Trees, Support Vector Machines, Random Forest, etc. Altogether, the study offers an approach with wide scope across the field of landslide hazard assessment. [ABSTRACT FROM AUTHOR]

After recent large earthquakes, field investigations confirmed that several bridges were severely damaged and collapsed not only due to the earthquake, as an independent hazard, but also to the subsequent tsunami, landslide or fault displacement. In addition, long-term material deterioration might have an important impact on seismic damage to bridges. Therefore, it is important to study both independent and interacting hazards and their effects on the reliability and risk of bridges and bridge networks. Although earthquake is still a dominant hazard to bridges in many earthquake-prone countries, a life-cycle reliability and risk approach has to consider both independent and interrelated hazards causing bridge failure. Such an approach is presented in this paper. In addition, issues related to life-cycle analysis, design, risk, resilience and management of bridges under earthquake and other hazards are discussed. Finally, the concepts and methods presented are illustrated on both single bridges and bridge networks. [ABSTRACT FROM AUTHOR]

Peat mass movements are relatively common geomorphological phenomena in the uplands of Ireland and parts of northern Britain. Geomorphological mapping of many peat landslides has led to the identification of different types of failures based on repeatedly occurring morphological and geometric characteristics. Furthermore, much of the recently enhanced understanding of mass failures in peat deposits, published in several recent reviews, has been obtained through detailed analysis of the geomorphological maps produced from the new field surveys that are herein presented together for the first time. The map that is the focus of this paper is a composite figure that shows geomorphological maps of the majority of extant bogflow and bog slide types of peat mass movements in Ireland, together with two peat flows and a large peat slide that has not previously been described. The paper examines the methodology used to create the maps, and highlights the value of the maps in presenting potentially critical evidence that may not be discernible by field inspection alone. Creating geomorphological maps of peat landslides from field sketches provides highly satisfactory results if selected features are geo-referenced using a hand-held GPS at an average frequency of around once for every 30 m of source area perimeter or other linear component of a landslide. In the case of very large failures and very old and degraded failures, lower frequencies of GPS points can be tolerated because critical morphological evidence appears to scale-up with failure size and smaller-scale morphological details are lost with increasing failure age due to natural degradation of the disrupted peat. [ABSTRACT FROM AUTHOR]

This paper quantitatively examines the effects of slope height and slope gradient on landslide size distributions. We developed a loess slide inventory by using field survey data. Statistical analysis shows that most landslides are concentrated in areas with slope heights less than 60 m, and approximately 30% of the loess slides occurred on slopes with a gradient between 30° and 40°. However, high and steep slopes are rare in nature. We calculated the relative density of landslides, and the results showed that the relative density of landslides is greater on higher slopes with steeper slope gradients. Moreover, landslide size is correlated with slope height and slope gradient. The results demonstrate that landslide size increases as slope height increases and decreases as slope gradient increases. Furthermore, we determined the probability density of landslide area using kernel density estimation. The results showed that the landslide size distribution exhibits power law scaling above a certain size threshold, and the size threshold differs for different slope heights and slope gradients. The exponential scalings are influenced by slope height and slope gradient. Our results indicate that the exponential scaling decreases with increasing slope height and increases with increasing slope gradient. Large landslides are more frequent with a higher slope height and gentler slope gradient. [ABSTRACT FROM AUTHOR]

Detecting landslides using remote sensing images involves converting gray level images into binary images. Given the complex background and non-uniform illumination in the regional remote sensing image, the commonly-used global thresholding methods are limited to interpret some landslides those hidden in the shadow. In this paper, we report on an enhanced image thresholding method for the noncontact detection of earthquake-induced landslides. The proposed method incorporates Monte-Carlo simulation into the local thresholding method, and essential issues, regarding complex illumination condition and uncertainties of determining block size in local thresholding method, are addressed. To better separate landslide candidate objects from the background, we incorporate Digital Surface Model (DSM) into the binary image, such that the interferences by built-up areas, terraces, and rivers can be significantly reduced considering the indicator of slope gradient. The described method has been tested using two benchmark tests, showing that the proposed method performs well dealing with the complex background and illumination condition. As a case study, we use the proposed method to detect landslides in a remote sensing image near Beichuan area after the 2008 Ms8.0 Wenchuan earthquake. Results demonstrate that the presented method interprets landslides resembling those of manually visual delineation. [ABSTRACT FROM AUTHOR]

The standards applied to reclassify landslide-conditioning factors differ among studies and may change the accuracy of identifying landslide-prone areas. Therefore, we identified two standards per factor (elevation, aspect, slope, proximity to roads and proximity to streams) from the existing literature and set them as predisposing criteria in this paper. In addition to the five factors, lithology represented by types and a landslide inventory map produced from field surveys were also used in mapping. Thirty-two landslide susceptibility maps were generated based on weights-of-evidence and evaluated using the relative operative characteristic method. The results show that the subdivision criteria of factors change the accuracy, with the success rate varying from 84.34% to 87.51%. The map with the highest value captures more landslides in relatively higher susceptibility classes and is therefore considered the optimal one. Ultimately, a simplified mode of combining subdivision criteria is proposed to simplify comparison. [ABSTRACT FROM AUTHOR]

This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran. These algorithms have been used for flood modelling, infrequently. A total of 287 flood locations were randomly categorized into training (70%; 201 floods), and validation (30%; 86 floods) datasets for modelling process and evaluation. The Step-wise Weight Assessment Ratio Analysis (SWARA) technique was applied to evaluate the role of nine dominant factors on flood occurrence. The results of using the ANFIS and the artificial intelligence ensemble algorithms were three flood susceptibility maps. Results indicated that the ANFIS-BBO had the highest accuracy in comparison with the ANFIS and ANFIS-BA models in flood modelling. In addition, BBO algorithm showed its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility. [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]

This paper makes a case for the analysis of urban grounds as urbanizing agents at the intersections of the material and cultural. Through a case study of four decades of landslide management in Hong Kong, it casts new light on the role of urban nature as a participant in the generation of urban forms and conceptions of city life. It demonstrates the active role of anticipation, its differential distribution and materialization through a close study of how geotechnical engineers, film, literature and capitalists portrayed and operated on a territory on the verge of material collapse. In conversation with theories of urban assemblage, this paper contributes the process of ‘consolidation’ as it operated in geotechnical discourses in Hong Kong (and elsewhere) as a useful framework for analysing urban socio-natural interactions in contexts of volatile nature. This positions the city within fraught entanglements with a ground that is projected upon, surfaced, shaped and metamorphosing at variable rates. [ABSTRACT FROM AUTHOR]

This paper contributes to the critical mobilities literature by analysing local mobilities in Gojal, northern Pakistan in the aftermath of the 2010 Attabad Landslide, in order to develop new insights regarding the dialectical relationship between mobility and immobility. The landslide destroyed a large section of the Karakoram Highway, the region's arterial roadway. Among its disastrous effects was prolonged disruption of the accustomed movements of 20,000 villagers stranded north of the slide. To show how mobility is constituted dialectically in relation to immobility in this context, we detail the social and economic demobilisations Gojalis faced when the highway became impassable, and outline new mobilities they developed to mitigate the disaster of protracted strandedness. Gojalis responded to demobilisation by remobilising, at different scales, along new routes, in different directions and via new mobility platforms, thereby re-establishing circulation as a paradigm of everyday life and demonstrating the paper's argument that disasters are social processes that have simultaneously demobilising and remobilising effects. We conclude that nurturing a multiplicity of mobile relations and practices in several directions and across scales during the disaster recovery process will help Gojalis avoid a similar mobility disaster in the future. [ABSTRACT FROM AUTHOR]

This paper describes the activities conducted for the development of a thematic map of shallow landslides (and other landforms) resulting from by consecutive rainfall events during the fall-winter seasons of 2008/2009 and 2009/2010 in the Catanzaro Strait (Central Calabria, South Italy). The methodology is based on the interpretation of Google Earth high-resolution satellite images (post-event images are dated 19th March 2010), followed by processing and management of the collected data through a geographic information system. The inventory covers a wide area (692 km2) and includes 3483 shallow landslides and many other geomorphological features (zones of incipient shallow landslides and areas affected by soil erosion) related to the 2008–2010 rainfall events. The map produced may provide useful data to define shallow landslide events, develop landscape evolution models and characterise active geomorphic processes. Furthermore, the map may also be used as a basic tool to produce landslide or erosion susceptibility maps, which can be used for land-use planning strategies and landslide hazard analyses. [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]

SAR Interferometry (InSAR) is a powerful technique for mapping land surface deformation, but single one-dimensional (1D) InSAR line-of-sight (LOS) measurement limits its application in three-dimensional (3D) displacement retrieval. Traditional methods were developed for measuring large-scale deformation. In this paper, to retrieve the slow 3D deformation process in the Jiaju landslide, a surface-parallel flow model is applied, according to the existing geological data gained from this landslide. Nine ENVISAT ASAR images and 19 ALOS PALSAR images were collected to reconstruct the 3D deformation field. An iterative method for correcting the characteristic value (IMCCV) with maximum likelihood estimation (M-estimation) adaptively generates an accurate stochastic model. The 3D deformation measurements show that the northern part of the Jiaju landslide clearly had horizontal deformation with the largest deformation velocity exceeding 15 cm year−1, while the southern part was relatively stable. Similarly, large vertical deformation appeared in the northern part (−2 cm year−1) and little deformation occurred in the southern part (−0.5 cm year−1). Compared with GPS measurements, the standard deviation is 1.4 cm year−1 and 0.7 cm year−1 in horizontal and vertical directions, respectively. Therefore, the Jiaju landslide exhibited movements in the horizontal direction accompanied by slight settlement in the vertical direction. [ABSTRACT FROM AUTHOR]

In order to optimise the selection of landslide monitoring points and save the cost of monitoring, a geological constitutive model was constructed by using 3D laser scanning and geological borehole data to simulate the relationship between rainfall and deformation. Thus, the main occurrence area and maximum deformation of the landslide were determined. Aiming at the deficiency of the single-epoch redundancy of the single-frequency GNSS receiver and the poor accuracy, this paper proposes a single-frequency BDS/GPS combined positioning and monitoring scheme with constraint of deformation features to restrict the search range of single-frequency ambiguity, obviously increase the ambiguity fixed success rate and then improve the BDS/GPS positioning accuracy. By contrast experiments, the landslide area obtained by numerical simulation basically matches with the on-site landslide area. The BDS/GPS combined positioning with constraint is consistent with the deep displacement changes, which can well reflect the displacement of the landslide body and make an early warning of disasters. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides occur during or immediately after rainfall events in which the pore water pressure builds up, leading to shallow slope failure. Thereby, low permeability layers result in high gradients in pore water pressure. The spatial variability of the soil permeability influences the probability such low permeability layers, and hence the probability of slope failure. In this paper, we investigate the influence of the vertical variability of soil permeability on the slope reliability, accounting for the randomness of rainfall processes. We model the saturated hydraulic conductivity of the soil with a one-dimensional random field. The random rainfall events are characterised by their duration and intensity and are modelled through self-similar random processes. The transient infiltration process is represented by Richards equation, which is evaluated numerically. The reliability analysis of the infinite slope is based on the factor of safety concept for evaluating slope stability. To cope with the large number of random variables arising from the discretization of the random field and the rainfall process, we evaluate the slope reliability through Subset Simulation, which is an adaptive Monte Carlo method known to be especially efficient for reliability analysis of such high-dimensional problems. Numerical investigations show higher probability of slope failure with increased spatial variability of the saturated hydraulic conductivity and with more uniform rainfall patterns. [ABSTRACT FROM AUTHOR]

Landslides are the most common natural disasters in mountainous regions, being responsible for significant loss of life as well as damage to critical infrastructure and properties. As the world population grows, people tend to move to higher locations to construct buildings, thereby making structures vulnerable due to landslides. This paper discusses previous research on the vulnerability assessment of structures exposed to landslides and presents a modified semi-quantitative approach to assess the scenario-based physical vulnerability of buildings based on their resistance ability and landslide intensity. Resistance ability is determined by integrating expert knowledge-based resistance factors assigned to five primary building parameters. Landslide intensity matrix defining different intensity levels is proposed based on combinations of landslide velocity and volume. Physical vulnerability of buildings is estimated and classified as class I, II or III for scenario-based low to very high landslide intensity. Finally, the application of the model is illustrated with a case study of 71 buildings from Garhwal Himalayas, India. [ABSTRACT FROM AUTHOR]

In this paper, the applicability of ground-based synthetic aperture radar (GB-SAR) as an early warning system for landslide monitoring is discussed. The effectiveness of the differential interferometric SAR (DInSAR) technique used in GB-SAR depends strongly on the geography of the monitored location. Therefore, an assessment of the system compatibility to select the most appropriate remote monitoring method is essential prior to any hardware implementation. In the preliminary part of this study, a 3D model was created using a LiDAR survey, and proposed locations for GB-SAR installation were examined. A 3D simulation was carried out to estimate the illumination from each of the proposed GB-SAR locations. The proposed model increased the efficiency of the GB-SAR positioning by minimising installation cost and time. Hardware configuration parameters, such as platform height, maximum range, and the direction and view angle of the radar line of sight were estimated by considering the optimum reflected power and ground illumination. Unlike on flat terrain, deployment of GB-SAR in a mountainous area is challenging because of surface anomalies and continuous changes in meteorological parameters, such as atmospheric temperature, pressure and relative humidity. In this study, the experimental site was located 3 km from the Aso volcano, and the weather conditions in the Aso caldera became a critical factor in accurately estimating the interferometric phase. The presence of atmospheric artefacts also compromises the applicability of the classical DInSAR technique. Here, we minimised the atmospheric phase screen by estimating the optimum data acquisition interval from GB-SAR monitoring under extreme weather conditions. The developed methodologies were then used to design a new landslide early warning system that measures real-time displacement over an area of 1 km2 within 10 s of scanning. This fully automatic monitoring system updates every 15 min and presents displacement information in a 3D interface. The system we have developed has been deployed for continuous monitoring of the mountainous environment of a road reconstruction site in Minami-Aso, Kumamoto, Japan where a large-scale landslide was triggered following the Kumamoto earthquake in 2016. [ABSTRACT FROM AUTHOR]

This paper aims at providing an answer as to whether generalization obtained with data-driven modelling can be used to gauge the plausibility of the physically based (PB) model's prediction. Two statistical models namely; Weight of Evidence (WofE) and Logistic Regression (LR), and a PB model using the infinite slope assumptions were evaluated and compared with respect to their abilities to predict susceptible areas to shallow landslides at the 1:10.000 urban scale. Threshold-dependent performance metrics showed that the three methods produced statistically comparable results in terms of success and prediction rates. However, with the Area Under the receiver operator Curve (AUC), statistical models are more accurate (88.7 and 84.6% for LR and WofE, respectively) than the PB model (only 69.8%). Nevertheless, in such data-sparse situation, the usual approaches for validation, i.e. comparing observed with predicted data, are insufficient, formal uncertainty analysis (UA) is a means for evaluating the validity and reliability of the model. We then refitted the PB model using a stochastic modification of the infinite slope stability model input scheme using Monte Carlo (MC) method backed with sensitivity analysis (SA). For statistical models, we used an informal Student t-test for estimating the certainty of the predicted probability (PP) at each location. Both modelling outputs independently show a high validity; and whereas the level of confidence in LR and WofE models remained the same after performance re-evaluation, the accuracy of the PB model showed an improvement (AUC = 72%). This result is reasonable and provides a further validation of PB model. So, in urban slope analysis, where PB diagnostic is necessary, statistical and PB modelling may play equally supportive roles in landslide hazard assessment. [ABSTRACT FROM AUTHOR]

This paper deals with the study of the dynamics of a landslide from two different but complementary point of views. The landslide is situated within the Miozza basin, an area of approximately 10.7 km2located in the Alpine region of Carnia (Italy). In the first part of the paper, the macro-scale analysis of volumetric changes occurred after the reactivation of landslide in 2004 is addressed by using a two-epoch laser scanning surveys from airborne (ALS) and terrestrial (TLS) platforms. airborne laser scanning (ALS) data were collected in 2003 (before reactivation of the phenomenon) with an ALTM 3033 OPTECH sensor while terrestrial laser scanning (TLS) measurements were acquired in 2008 with a Riegl LMS-Z620. The second part of the paper deals with the study of dynamic processes of the landslide at micro-scale. To this aim, a global navigation satellite system (GNSS)-based monitoring network is analysed using a statistical approach to discriminate between measurement noise and possible actual displacements. This task is accomplished using both “classical” statistical testing and a Bayesian approach. The second method has been employed to verify some apparent vertical displacements detected by the classical test. As regards the first topic of the paper, achieved results show that long-range TLS instruments can be profitably used in mountain areas to provide high-resolution digital terrain models (DTMs) with superior quality and detail with respect to aerial light detection and ranging data only, even in areas with very low accessibility. Moreover, ALS- and TLS-derived DTMs can be combined each other in order to fill gaps in ALS data, mainly due to the complexity of terrain morphology, and to perform quite accurate calculations of volume changes due to landslide phenomenon. Finally, the outcomes of the application of Bayesian inference demonstrate the effectiveness of this method to better detect statistically significant displacements of a GNSS monitoring network points. However, the application of this method in the geodetic field requires the identification of a preferring direction of displacements, what is not always feasible in advance. [ABSTRACT FROM PUBLISHER]

This paper reports on a project in which students designed, constructed and tested a model of an existing early warning system with simulation of debris flow in a context of a landslide. Students also assessed rural community members’ knowledge of this system and subsequently taught them to estimate the time needed for evacuation of the community in the event of a landslide. Participants were four undergraduate students in a civil engineering programme at a university in Thailand, as well as nine community members and three external evaluators. Results illustrate project and problem-based, experiential learning and highlight the real-world applications and development of knowledge and of hard and soft skills. The discussion raises issues of scalability and feasibility for implementation of these types of projects in large undergraduate engineering classes. [ABSTRACT FROM PUBLISHER]

This paper presents a mass movement inventory map at 1:5000 scale of the Verbicaro area (about 13 km2) located in the Calabria region (southern Italy). The Main Map results from the visual interpretation of aerial photographs, multi-temporal geomorphological field surveys, and field investigations of damage suffered by buildings. Some 53% of the study area is affected by a total of 252 landslides, comprising different types, state of activity, and size. The mapped landslides, mainly complex type, involve low-grade metamorphic rocks; among these, 15% are active and slow-move on pre-existing sliding surfaces. Moreover, out of 492 surveyed buildings, 347 are located on landslide-affected areas and experienced damages covering a broad range of severity levels. The Main Map can represent a useful tool for authorities in charge of land-use planning and urban management and can be used to pursue landslide risk analyses. [ABSTRACT FROM AUTHOR]

Landslides have been observed on several planets and minor bodies of the solar System, including the Moon. Notwithstanding different types of slope failures have been studied on the Moon, a detailed lunar landslide inventory is still pending. Undoubtedly, such will be in a benefit for future geological and morphological studies, as well in hazard, risk and susceptibility assessments. A preliminary survey of lunar landslides in impact craters has been done using visual inspection on images and digital elevation model (DEM) (Brunetti et al. 2015) but this method suffers from subjective interpretation. A new methodology based on polynomial interpolation of crater cross-sections extracted from global lunar DEMs is presented in this paper. Because of their properties, Chebyshev polynomials were already exploited for parametric classification of different crater morphologies (Mahanti et al., 2014). Here, their use has been extended to the discrimination of slumps in simple impact craters. Two criteria for recognition have provided the best results: one based on fixing an empirical absolute thresholding and a second based on statistical adaptive thresholding. The application of both criteria to a data set made up of 204 lunar craters’ cross-sections has demonstrated that the former criterion provides the best recognition. [ABSTRACT FROM AUTHOR]