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Firstly, Landslide Susceptibility Maps of the study area were produced using Frequency Ratio and Modified Information Value models. Nine factors were defined and the Landslide Inventory Map was used to produce these maps. In the Landslide Susceptibility Maps obtained from the Frequency Ratio and Modified Information Value models, the total percentages of high and very high-risk areas were calculated as 10% and 15%, respectively. To determine the accuracy of the produced Landslide Susceptibility Maps, the success and the prediction rates were calculated using the receiver operating curve. The success rates of the Frequency Ratio and Modified Information Value models were 82.1% and 83.4%, respectively, and the prediction rates were 79.7% and 80.9%. In the second part of the study, the risk situations of 125 km of forest roads were examined on the map obtained by combining the Landslide Susceptibility Maps. As a result of these investigations, it was found that 4.28% (5.4 km) of the forest roads are in very high areas and 4.27% (5.3 km) in areas with high landslide risk areas. In the last part of the study, as an alternative to forest roads with high and very high landslide risk, 9 new forest road routes with a total length of 5.77 km were produced by performing costpath analysis in with geographic information systems. [ABSTRACT FROM AUTHOR]

Tourism in Bali has surged post-COVID-19, with a 74.60% rise in arrivals from September 2022 to 2023, driving infrastructure development, notably in areas like Candidasa. However, safety concerns arise, especially in steep slope regions prone to landslides. This study employs cone penetration testing (CPT) data to assess its suitability for slope stability analysis amidst tourism development. By interpreting CPT data based on prior research, it shows obtaining ample soil parameters for such analysis is feasible. The research site, a Candidasa resort, exemplifies risks in hilly terrains. Fellenius-Morgenstern analysis reveals varying safety factors, indicating landslide susceptibility in certain scenarios. While CPT testing offers valuable insights, comprehensive geotechnical investigations are recommended for critical infrastructure projects to mitigate risks effectively. This study highlights the importance of comprehensive soil analysis and safety measures in the development of tourism infrastructure, especially in areas prone to geological hazards. [ABSTRACT FROM AUTHOR]

Jember is surrounded and limited by highlands such as Mount Argopura, Mount Ijen, Mount Argopura, and the southern karst mountains. In 2015-2022, the Jember area flooded during the rainy season and dried during the dry season. Changes in land cover that do not follow the science of soil preservation will cause disasters, including landslides and erosion. The purpose of this study is to assess the risk of landslides on the slopes of Mount Argopura through the Geographic Information System. The study used a field survey method that was divided into several stages, including making a working map, conducting a field survey, and analyzing the data in the laboratory. The sensitivity analysis of the landslide level used as the basis for the assessment used the relationship between the parameters of soil erodibility, soil erosion, slope and soil conservation, and slope length. The sensitivity of the level of landslide risk on Mount Argopura is divided into five classes, from very light to very heavy. The very light category covers 4.92% of the total area with erosion of 0.47 t/ha/year. The very heavy class covers 39.70% of the total area, with 1,360.79 t/ha/year erosion. [ABSTRACT FROM AUTHOR]

Slope stability analysis is undoubtedly one of the most complex problems in geotechnical engineering and its study plays a paramount role in mitigating the risk associated with the occurrence of a landslide. This problem is commonly tackled by using limit equilibrium methods or advanced numerical techniques to assess the slope safety factor or, sometimes, even the displacement field of the slope. In this study, as an alternative approach, an attempt to assess the stability condition of homogeneous slopes was made using a machine learning (ML) technique. Specifically, a meta-heuristic algorithm (Harmony Search (HS) algorithm) and K-means algorithm were employed to perform a clustering analysis by considering two different classes, depending on whether a slope was unstable or stable. To achieve the purpose of this study, a database made up of 19 case studies with 6 model inputs including unit weight, intercept cohesion, angle of shearing resistance, slope angle, slope height and pore pressure ratio and one output (i.e., the slope safety factor) was established. Referring to this database, 17 out of 19 slopes were categorized correctly. Moreover, the obtained results showed that, referring to the considered database, the intercept cohesion was the most significant parameter in defining the class of each slope, whereas the unit weight had the smallest influence. Finally, the obtained results showed that the Harmony Search algorithm is an efficient approach for training K-means algorithms. [ABSTRACT FROM AUTHOR]

Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms. [ABSTRACT FROM AUTHOR]

one of the most important parts of the general complex for studying the regime of landslide processes are geodetic measurements of the development of landslide displacements. Therefore, the justification of the necessary accuracy of geodetic measurements, the choice of the method of such observations and the development of technology for their implementation should proceed from the principle of obtaining the most complete and reliable information about the landslide slope. This article is the result of studying the possibility of using geodetic systems in the research of landslide processes. Methods of investigation of the stress-strain state of rocks of the landslide slope are considered. There are many options for building maps of dangerous areas. The advantages and disadvantages of each method are described. [ABSTRACT FROM AUTHOR]

Geologically, La Paz City is located in an unstable area. During the history of La Paz city, many landslides have destroyed houses and valuable infrastructures. In the last decades, time series Interferometric Synthetic Aperture Radar (InSAR) technologies have demonstrated a great capacity for detecting slow ground displacement, achieving an accuracy of millimetre-level. In order to have a better landslide monitoring of La Paz city, in this study, the Sentinel-1 SAR images have been processed by Persistent Scatterer Interferometry (PSI) and the Small Baseline Subset (SBAS) techniques. The time span of the datasets is from March 2015 to August 2016. Both ascending and descending Synthetic Aperture Radar (SAR) images have been processed to obtain the line of sight (LOS) ground velocity, and then the results have been combined to estimate the up-down and east-west displacement. Several active movement areas have been identified, showing a surface velocity up to 158 mm year−1 westward and 49 mm year−1 eastward. Furthermore, two important findings have been discovered. First, the InSAR result has detected movement in Auquisamaa hill before the area collapsed (15 February 2017), where five houses are buried. Second, the InSAR result has identified that there are still some unstable sites in Callapa area, where a mega-landslide has destroyed more than a thousand of houses in February 2011. In conclusion, we have verified that the InSAR technology could be a very useful tool to help La Paz public institutions for a better management of urban planning, landslide areas delimitation and landslide risk mitigation. [ABSTRACT FROM AUTHOR]

We analyze landslides on Ceres using several quantitative approaches to constrain the composition and structure of the top few kilometers of Ceres' crust. We focus on a subset of archetypal landslides classified morphologically as thick, steep‐snouted "type 1" (T1) flows and thin spatulate "type 2" (T2) flows (Schmidt et al., 2017, https://doi.org/10.1038/ngeo2936) to explore the landslides' mechanical properties. Our results confirm earlier observations showing that T1 landslides are typically found poleward of 70° latitude and T2 mostly equatorward of 70° latitude. Measurements of landslide drop height and runout length imply effective friction coefficients lower than common friction coefficients in any of Ceres' identified or suggested non‐ice surface materials, including saturated clays. Our measurements of the volume and area of landslide scars suggest that T1 landslides can fail to greater depths than T2 for a given scar area, consistent with depth‐limited failure in T2 landslides. These results are consistent with a layer of lower shear strength material overlying a stronger layer in Ceres' outer shell at low to middle latitudes and a single layer without an overlying weak layer at polar latitudes. Combining these observations with known constraints on Ceres' near‐surface composition, we propose that Ceres' crust at low to middle latitudes consists of a topmost layer with an ice content in excess of the spectral and elemental detection depths, thins out at high latitudes, and overlies a stronger and more ice‐rich layer. Plan Language Summary: We use landslides on Ceres to explore the possible ice content and distribution in the upper few kilometers of Ceres. The landslides used here are a subset of the best examples of the main types of landslides identified in earlier work. We confirm that type 1 landslides are only common poleward of 70° latitude and type 2 landslides are commonly equatorward of 70° latitude. Friction coefficients estimated from the landslide drop height and runout length span a range suggestive of ice. Volume and area measurements of the landslide failure scar are consistent with a weak layer overlying a stronger layer at latitudes where type 2 landslides are common and a single layer in the case of type 1 landslides, located primarily in polar latitudes. We propose that the weak layer suggested at middle and low latitudes thins out at the poles and that the stronger layer has higher ice content. Key Points: Landslide types 1 and 2 mobilities are consistent with cold ice and warm ice respectively, with significant overlapType 2 landslides are consistent with a layer of weaker shear strength overlying a stronger one, which we suggest thins out by 70° latitudeWe propose the low shear strength layer contains 3–19 vol% ice with the underlying layer composed of 24–40 vol% ice [ABSTRACT FROM AUTHOR]

Landslide disasters are one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the patterns of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou–Chengdu–Chongqing (LCC) long-distance multiproduct oil pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a susceptibility assessment and a vulnerability assessment. We used an entropy weight method to establish a system for the vulnerability assessment, whereas a Levenberg–Marquardt back propagation (LM-BP) neural network model was used to conduct the susceptibility assessment. The risk assessment was carried out on the basis of two assessments. The first, the system of the vulnerability assessment, considered the pipeline position and the angle between the pipe and the landslide (pipeline laying environmental factors). We also used an interpolation theory to generate the standard sample matrix of the LM-BP neural network. Accordingly, a landslide susceptibility risk zoning map was obtained based on susceptibility and vulnerability assessment. The results show that about 70 % of the slopes were in high-susceptibility areas with a comparatively high landslide possibility and that the southern section of the oil pipeline in the study area was in danger. These results can be used as a guide for preventing and reducing regional hazards, establishing safe routes for both existing and new pipelines, and safely operating pipelines in the Guangyuan area and other segments of the LCC oil pipeline. [ABSTRACT FROM AUTHOR]

Landslides are the most dangerous and common natural phenomena causing higher number of victims and damages in Piemonte (north-western Italy). An integrated statistical approach to identify the main predisposing and triggering factor causing shallow landslides occurrence based on a large dataset of historical rainfall-induced shallow landslides and a long-time series of rainfall data is presented. Historical shallow landslide events were compared with territorial and rainfall characteristics. Exploratory Spatial Data Analysis (ESDA) has been performed in order to explore the spatial distributions of landslides with the environmental quantitative variables (morphometry and rainfall) to identify possible patterns of spatial autocorrelation. ESDA showed strong autocorrelation of landslide locations with rainfall variables, while resulting in low values for morphometric parameters at the investigated scale. Moreover, by intersecting landslides distribution with categorical land thematic parameters (land thematic maps) allowed to identify areas with different landslide density occurrence. Resulting susceptibility-related areas were used to improve the landslides regional Early Warning System of Piemonte based on refined empirical intensity vs duration (I-D) rainfall thresholds. [ABSTRACT FROM AUTHOR]

We propose a simple method to estimate the surface displacement of earth slides. It is based on the assumptions that earth slides move by sliding along a discrete basal-slip surface and extensional structures, which commonly characterize their depletion zone, completely accommodate the occurred displacement. The proposed method is based on the reconstruction of the cross-sectional geometry of structures crossed by an arbitrary chosen longitudinal profile, aligned with the direction of the movement. Structure geometry reconstruction is completed using high-precision GPS survey and the reconstructed geometry is then used as basis to measure horizontal and vertical components of the displacement accommodated by each single extensional structure. Total horizontal and vertical earth-slide displacements are calculated as the sum of displacements accumulated at each single structure. We test the method at the Summonte earth slide in southern Italy using real-time kinematic GPS field mapping. Results from our estimation were validated considering a single displacement vector derived from the direct and indirect measurement of the position of an isolated tree located below the extensional zone of the slide. [ABSTRACT FROM AUTHOR]

The main aim of this study is the description and the analysis of the monitoring strategies implemented within local landslide early warning systems (Lo-LEWS) operational all around the world. Relevant information on 29 Lo-LEWS have been retrieved from peer-reviewed articles published in scientific journals and proceedings of technical conferences, books, reports, and institutional web pages. The first part of the paper describes the characteristics of these early warning systems considering their different components. The main characteristics of each system are summarized using tables with the aim of providing easily accessible information for technicians, experts, and stakeholders involved in the design and operation of Lo-LEWSs. The second part of the paper describes the monitoring networks adopted within the considered systems. Monitoring strategies are classified in terms of monitored activities and methods detailing the parameters and instruments adopted. The latter are classified as a function of the type of landslide being monitored. The discussion focuses on issues relevant for early warning, including appropriateness of the measurements, redundancy of monitoring methods, data analysis, and performance. Moreover, a description of the most used monitoring parameters and instruments for issuing warnings is presented. [ABSTRACT FROM AUTHOR]

This document describes technical concepts and terms related to landslide risk on slopes and hillsides, from a comprehensive approach. A general methodology is presented for the risk analysis and assessment according to the threat, given by the interrelation susceptibility - probability of landslide occurrence; and the vulnerability of the exposed elements, obtained from a multifactorial analysis that considers physical and social aspects. Susceptibility is obtained considering various conditioning and triggering factors of instability and the probability of event occurrence is estimated from deterministic methods. The vulnerability analysis is done considering the spatial and temporal components of the potential events, as well as the impact that these could have on the exposed elements, in terms of social and material losses. This methodology is applied to the study of an urban slope in the city of Tijuana, Mexico. [ABSTRACT FROM AUTHOR]

Landslide disaster is one of the main risks involved with the operation of long-distance oil and gas pipelines. Because previously established disaster risk models are too subjective, this paper presents a quantitative model for regional risk assessment through an analysis of the laws of historical landslide disasters along oil and gas pipelines. Using the Guangyuan section of the Lanzhou-Chengdu-Chongqing (LCC) Long-Distance Products Oil Pipeline (82 km) in China as a case study, we successively carried out two independent assessments: a hazard assessment and a vulnerability assessment. We used an entropy weight method to establish a system for the vulnerability assessment, whereas a Levenberg Marquardt-Back Propagation (LM-BP) neural network model was used to conduct the hazard assessment. The risk assessment was carried out on the basis of two assessments. The first, the system of the vulnerability assessment, considered the pipeline position and the angle between the pipe and the landslide (pipeline laying environmental factors). We also used an interpolation theory to generate the standard sample matrix of the LM-BP neural network. Accordingly, a landslide hazard risk zoning map was obtained based on hazard and vulnerability assessment. The results showed that about 70% of the slopes were in high-hazard areas with a comparatively high landslide possibility and that the southern section of the oil pipeline in the study area was in danger. These results can be used as a guide for preventing and reducing regional hazards, establishing safe routes for both existing and new pipelines and safely operating pipelines in the Guangyuan section and other segments of the LCC oil pipeline. [ABSTRACT FROM AUTHOR]

Abstract Landslides are a type of mass wasting and denote any downslope movement of soil and rock under the influence of gravity; as such they can represent a dangerous natural hazard process, especially in case that they affect inhabited areas or transport infrastructure. Because the occurrence of landslides is typically favoured by terrain and lithological conditions, the process is frequently concentrated in relatively small, isolated regions exhibiting suitable initial conditions of terrain instability. Extensive regional assessments of landslide activity have been used in the past to uncover common triggers and process patterns, mostly in the aftermath of large, regional disasters. By contrast, however, regional reconstructions of past landslide activity have not been realized with dendrogeomorphic techniques, and with the aim to date past landslide histories over extended time periods and with annual dating precision. This study therefore aims at disentangling landslide dynamics at the regional scale (covering ca. 600 km2) by using growth-ring records of trees that grow on active landslides. The studied area of the Hostýnsko-vsetínská hornatina Mts. (Central Europe) is well known for its high landslide activity, but has so far been lacking a detailed chronology of past events. To this end, we dated past activity on 26 landslide bodies using tree-ring series from 1322 disturbed trees to reconstruct 327 landslide reactivations during the last century. The reconstructed landslide database allowed correlations between landslide types and their frequencies or occurrence, as well as inferences between selected morphometric parameters with landslide frequency and magnitude. We also observe periods of increased landslide activity (1940s, 1960s, 1980s, and 1990s) and events of regional importance (e.g., 1961, 1985, and 1997), as well as a significant decrease in landsliding during the last two decades. Highlights • Regional reconstruction of landslides activity for Outer Western Carpathians • Tree-ring based analysis of 26 landslides • Chronologies of various landslide types • Comparison of activity with the morphometry of landslides [ABSTRACT FROM AUTHOR]

Landslide is one of the devastating natural phenomenon that threatens human life and property. Every year a number of persons lost their lives due to the landslides. Therefore, a better understanding and characterization of landslide is very essential for adopting mitigation strategies to contain the adversities of this natural hazard. Information on landslides from different climatic setup are very essential for better understanding of the influence of weathering, rainfall, or topography on landslide generation. Weathering is one of the important causative factor for landslide generation in the moderate topography or inactive mountainous terrain. The Western Ghats including the Deccan Traps, an inactive mountain range, receives torrential rainfall. Intense rainfall in these areas enhances the weathering processes and fabricates thick soil covers. Mahabaleshwar area, Maharashtra was chosen as a case study, where high elevated part is covered by lateritic layer and each lava flow unit is separated by a thin weathered bed of red bole. The area experiences series of landslides during the summer monsoon months. Mainly two types of landslides have been identified in the area confined with the red bole bed and powdery lateritic soil. The first type of landslides occur at higher elevations (≥1200m) where horizontal beds of permeable laterites underlined by impermeable thick basalt beds. The rain water infiltrates down and spread laterally within the permeable lateritic beds. It finally spouts at lower plateau elevations and triggers mainly debris flows. The other category of landslides occurs where the weathered red bole bed separates two successive lava flows. The percolating water from the secondary porosities (joints and inter connected vugs) comes out from the contact zones of basalt and red bole bed in the form of seepages. It erodes the red bole bed and as a result the overlying masses hang and consequently lead to rock fall. The Chemical Index of Alteration (CIA) of the representative samples from landslide locations indicates significant weathering. The CIA values for the fine lateritic soil are up to 98% whereas for the red bole bed it varies from 77 to 85%. This suggests a high chemical weathering and higher erodibility. The association of active landslide locations with the red bole bed and fine lateritic soil suggests a close relation between weathering and landslide occurrences in the area. [ABSTRACT FROM AUTHOR]

To assess the risk of slope failure, this study employs a rigorous method that is referred to as the random finite difference method (RFDM). The RFDM is capable of considering the spatial variability of soil properties subject to different auto-correlation structures. Comparisons with the collected data are made with respect to two idealized slopes, and a parametric analysis is presented. The results demonstrate that the spatial auto-correlation structures significantly affect the slope failure risk. Depending on the rotated angle, the rotated anisotropy auto-correlation structure shows a dual effect on the reliability of the slope compared to a transverse anisotropy case. [ABSTRACT FROM AUTHOR]

Rainfall threshold determination is a pressing issue in the landslide scientific community. While main improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event - duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR=(α±Δα)*S(β ± Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in: (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide first regional rainfall thresholds for landsliding in tropical Africa. [ABSTRACT FROM AUTHOR]

Landslides constitute a hazard to life and infrastructure, and their risk is mitigated primarily by reducing exposure. This requires information on landslide hazard at a scale that can enable informed decisions about how to respond to that hazard. Such information is often unavailable to, or not easily interpreted by, those who might need it most (e.g., householders, local government, and NGOs). To address this shortcoming, we develop simple rules to identify landslide hazard that are understandable, communicable, and memorable, and that require no prior knowledge, skills, or equipment to evaluate. We examine rules based on two common metrics of landslide hazard, local slope and upslope contributing area as a proxy for hillslope location, and we introduce and test two new metrics: the maximum angle to the skyline and the hazard area, defined as the upslope area with slope >39° that reaches a location without passing over a slope of <10°. We then test the skill with which each metric can identify landslide hazard - the probability of being hit by a landslide - using inventories of landslides triggered by six recent earthquakes. We find that the maximum skyline angle and hazard area provide the most skilful predictions, and these results form the basis for two simple rules: minimize your maximum angle to the skyline and avoid steep (>10°) channels with many steep (>39°) areas that are upslope. Because local slope alone is a skilful predictor of landslide hazard, we can formulate a third rule as minimise local slope, especially on steep slopes and even at the expense of increasing upslope contributing area, but not at the expense of increasing skyline angle or hazard area. Upslope contributing area, by contrast, has a weaker and more complex relationship to hazard than the other predictors. Our simple rules complement, but do not replace, detailed site-specific investigation; they can be used for initial estimation of landslide hazard or guide decision-making in the absence of any other information. [ABSTRACT FROM AUTHOR]

The article focuses on the Taroko National Park in Taiwan where the wet slopes and earthquakes have made landslides common. Niels Hovius, a geomorphologist and his colleagues have made it a landscape dynamics observatory by installing various instruments. It is noted that monitoring landslides will help the researchers understand what triggers them and how landscapes recover. Landslide researcher Robert Emberson has found that landslides in Taiwan are sources of Carbon dioxide.

Most regional landslide warning systems utilize empirically derived rainfall thresholds that are difficult to improve without recalibration to additional landslide events. To address this limitation, we explored the use of synthetic rainfall to generate thousands of possible storm patterns and coupled them with a physics‐based hydrology and slope stability model for various antecedent soil saturation scenarios to analyze pore water pressure and factor of safety metrics. We used these metrics to generate two‐tiered alert thresholds that can be employed to assess shallow landslide potential for any given combination of storm and antecedent wetness. When applied to the San Francisco Bay region (CA, USA), the results are consistent with events that caused widespread landsliding. Our deterministic modeling approach, which accounts for plausible ranges in soil hydraulic and mechanical properties, can inform the development of the next generation of warning systems for rainfall‐induced landsliding. Plain Language Summary: Providing accurate warning in advance of rainfall‐induced landsliding can help reduce loss of life and property but requires knowledge of hillslope wetness and future rainfall conditions to forecast when widespread landsliding is likely. However, typical landslide warning systems consider only the rainfall characteristics (e.g., rainfall amount, duration, and/or intensity) that were measured during past landslide events. These are used to define a set of rainfall conditions, or a threshold, for which landsliding should be expected within a given area. When a rainfall threshold does not perform well for subsequent landsliding events, it can be difficult to diagnose the problem because hillslope wetness is not considered. We present an approach to develop landslide thresholds that accounts for both rainfall and hillslope wetness. The results are consistent with historical and recent widespread landsliding events in our study region and, more importantly, can be related to measurable properties of hillslopes. This more direct approach could revolutionize the development of the next generation of landslide warning systems. Key Points: Thresholds for rainfall‐induced landsliding of soil‐mantled hillslopes identified with physics‐based hydrologic and slope stability modelingLibrary of modeled infiltration events based on variations in rainfall and antecedent soil saturation facilitates timely hazard assessmentDeterministic modeling approaches that account for parameter variability can inform the next generation of landslide warning systems [ABSTRACT FROM AUTHOR]

Yang, J., 2018. Landslide damage from extreme rainstorm geological accumulation layers within plain river basins. In: Ashraf, M.A. and Chowdhury, A.J.K. (eds.), Coastal Ecosystem Responses to Human and Climatic Changes throughout Asia. To provide the basis of an early warning and detection for landslide disasters and to provide decision support for local governments in disaster prevention and mitigation, this research studied landslide damage assessment from the geological accumulation layer in a plain river basin under conditions of an extreme rainstorm. Based on an analysis of the relationship between landslide geology and rainfall and a damage model of landslides under conditions of extreme rainfall, the landslide damage assessment described in this article used the SINMAP model. The layer of geological accumulation in the Wenchuan–Diexi River section in the upper reaches of the Minjiang River was the study area. Spatial distribution characteristics and the main factors inducing a landslide disaster were studied, and a danger-evaluation model for landslides in that region was established for landslide assessment research and for applying that research. Based on the SINMAP model, the effects of different extreme rainstorm events on the stability of regional slopes were analyzed. The results of a stability evaluation for landslides show that the area has an unstable zone of 4339.8 km2, which accounts for 64.49% of the total area. The unstable area in this evaluation was basically consistent with the distribution of an existing landslide, which shows that the model can adequately evaluate slope stability and landslide potential from the layer of geological accumulation in this area. [ABSTRACT FROM AUTHOR]

Many landslides were triggered by heavy rainfall from 29 June to 1 July 2017 in Ningxiang County, Hunan Province, China. A field investigation into one of the landslides, known as the Wangjiawan landslide, was undertaken on July 3, 2017, to understand the landslide mechanisms and the factors that triggered the event. The landslide is a translational and rotational slide that degraded downslope to an earth flow. It occurred on a steeply dipping mud-rich slate intercalation. Field investigation shows that the landslide had a movement rate of more than 25 m/s and had been triggered by up to 338 mm of continuous rainfall over 2 days. The landslide was fast-moving because it occurred on a steep slope and showed a rapid reduction in shear strength. The landslide resulted in nine fatalities and 19 injuries, most during initial rescue efforts. To mitigate a possible secondary landslide disaster, later emergency measures including evacuation of the survivors, setting up warning signs, and covering landslide cracks with plastic sheeting were adopted. [ABSTRACT FROM AUTHOR]

Recent investigations have sought to understand the spatial-temporal distribution of landslides in Teziutlán, Puebla, a municipality historically affected by landslides. The latest initiative, under the umbrella of the ICL-IPL Project “Landslide disaster risk communication in mountain areas,” was the publication of a book of Atlas type comprising a collection of 142 maps and their corresponding explanatory texts that included a context analysis of landslide disaster risk drivers at various scales, from regional to local. This paper aims to recognise and address the necessity to further enhance the guiding principle of the Sendai Framework for Disaster Risk Reduction to focus on the understanding of disaster risk drivers at local level, for the determination of measures to reduce disaster risk. We present an initial contribution to promote landslide disaster risk awareness in the urban area of Teziutlán by providing to the community direct access to maps of landslide disaster risk at local scale; this is a first step towards the establishment of a robust strategy to communicate landslide risk in the long term. Effective implementation calls for decreasing vulnerability and exposure. Beyond contexts of vulnerability reflected by social, economic, cultural, political, and institutional conditions, it may be difficult to picture the spatial interactions of exposure of communities, assets, and the environment because the means of analysing spatial relationships between society and nature are not commonly available in mountain areas. Therefore, for people to better understand risk, maps of landslide susceptibility and risk exposure present a good way for the inhabitants to familiarise themselves with the spatial context of the dynamics in which they are immersed. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to compare the performance of two statistical analysis models like weight of evidence and logistic regression (LR) with a soft computing model like artificial neural networks for landslide susceptibility assessment. These models were applied for the Selinous River drainage basin (northern Peloponnese, Greece) in order to map landslide susceptibility and rate the importance of landslide causal factors. A landslide inventory was prepared using satellite imagery interpretation and field surveys. Eight causal factors including altitude, slope angle, slope aspect, distance to road network, distance to drainage network, distance to tectonic elements, land cover, and lithology were considered. Model performance was tested with receiver operator characteristic analysis. The validation findings revealed that the three models show promising results since they give good accuracy values. However, the LR model proved to be relatively superior in estimating landslide susceptibility throughout the study area. [ABSTRACT FROM AUTHOR]

Landslides are a ubiquitous hazard in terrestrial environments with slopes, incurring human fatalities in urban settlements, along transport corridors and at sites of rural industry. Assessment of landslide risk requires high-quality landslide databases. Recently, global landslide databases have shown the extent to which landslides impact on society and identified areas most at risk. Previous global analysis has focused on rainfall-triggered landslides over short ~5- year observation periods. This paper presents spatiotemporal analysis of a global dataset of fatal non-seismic landslides, covering the period from January 2004 to December 2016. The data show that in total 55 997 people were killed in 4862 distinct landslide events. The spatial distribution of landslides is heterogeneous, with Asia representing the dominant geographical area. There are high levels of interannual variation in the occurrence of landslides. Although more active years coincide with recognised patterns of regional rainfall driven by climate anomalies, climate modes (such as El Niño-Southern Oscillation) cannot yet be related to landsliding, requiring a landslide dataset of 30C years. Our analysis demonstrates that landslide occurrence triggered by human activity is increasing, in particular in relation to construction, illegal mining and hill cutting. This supports notions that human disturbance may be more detrimental to future landslide incidence than climate. [ABSTRACT FROM AUTHOR]

In this work residents' social vulnerability and buildings' physical vulnerability of the Loures municipality (Portugal) were combined to locate the areas where the vulnerability is the highest, and to analyse the landslide risk. The social vulnerability of Loures was assessed using the Geographic Basis for Information Reference (BGRI) terrain units by combining sensitivity and lack of resilience based on the population and housing Census 2011 data. The physical vulnerability was assessed in a previous study based on an inquiry of a pool of European landslide experts and a sub-pool of landslide experts who know the study area. A matrix approach was used to cross the classes of the social and physical vulnerabilities. Finally, the landslide risk was analysed for each terrain unit considering the combined vulnerability, the buildings' economic value and the landslide susceptibility for a specific landslide magnitude (3-metre-deep rotational slide). Results show that 0.9% of the population reside in the area of the municipality where 75% of the future landslide should occur, and 0.8% of the buildings of the municipality--which represent a value of EUR 146,170,000--are also located in this dangerous area. This approach is reproducible: the risk analysis can be applied for another magnitude scenario in Loures, and the combined vulnerability can be assessed in any Portuguese municipality thanks to the availability of the data. [ABSTRACT FROM AUTHOR]

Research on landslide in Sri Mulyo Village, Dampit Subdistrict, Malang District, Indonesia has been implemented. This research is conducted to know the condition of the subsurface area for information in landslide vulnerability analysis. In addition, this research was undertaken to mitigate landslide in minimizing casualties and material losses. The study was done using resistivity method, configuration of dipole-dipole supported by drilling core lab test result. There are five resistivity measuring lines with trajectory lengths ranging from 100 m, 200 meters, and 300 meters. The results of the resistivity indicate that the subsurface lithology of Sri Mulyo Village is composed of clays (9.3-85.8 Ω.m), tuff (178-779 Ω.m) and breccia (≥ 1629 Ω.m). The sliding plane at the research area is the boundary between clays and tuffs. The result of correlation between geoelectric and drilling core data showed that the research area was dominated by a 10m depth of clay. High rainfall, clay thickness and vegetation (coffee) exhibit high levels of vulnerability to occur landslides. Possible landslide direction is from West-West to Southeast with creep avalanche type. [ABSTRACT FROM AUTHOR]

Empirical rainfall thresholds are commonly used to forecast landslide occurrence in wide areas. Thresholds are affected by several uncertainties related to the rainfall and the landslide information accuracy, the reconstruction of the rainfall responsible for the failure, and the method to calculate the thresholds. This limits the use of the thresholds in landslide early warning systems. To face the problem, we developed a comprehensive tool, CTRL–T ( C alculation of T hresholds for R ainfall-induced L andslides− T ool) that automatically and objectively reconstructs rainfall events and the triggering conditions responsible for the failure, and calculates rainfall thresholds at different exceedance probabilities. CTRL−T uses a set of adjustable parameters to account for different morphological and climatic settings. We tested CTRL−T in Liguria region (Italy), which is highly prone to landslides. We expect CTRL−T has an impact on the definition of rainfall thresholds in Italy, and elsewhere, and on the reduction of the risk posed by rainfall-induced landslides. [ABSTRACT FROM AUTHOR]

This study deals with the numerical modelling of large deep-seated instability processes affecting the Cinque Torri Group (Dolomites, Italy), which is a UNESCO World heritage site. The aim is to evaluate the role of intrinsic causal factors and to assess the failure mechanism proposed in previous investigations. The geological model is based on topographic, geological and geomechanical surveys, complemented with mineralogical, physical and geotechnical lab analyses for parameters determination. The numerical simulations have been carried out using a Distinct Element Method code on conceptual and site-specific slope models. A series of parametric analyses have been performed to characterize the role of the different predisposing factors potentially related with the ongoing rock spreading: i) load of the rocky pinnacles; ii) lithology, stratigraphy and attitude of the geological units; iii) discontinuity sets in the rock mass; iv) degradation of the mechanical properties. The results of this study suggest that the shear zone initiation is controlled by stress concentration due to the loading of the dolomitic pinnacles, whereas its shape appears to be structurally controlled by the dip-slope attitude of bedding in clay-rich mudstones. The interpretation of the modelling results has provided a better understanding of the ongoing deformation process, which can help in targeting effective and low-impact landslide risk mitigation strategies in this iconic landscape. [ABSTRACT FROM AUTHOR]

The development of unmanned aerial vehicle photogrammetry over the last decade has allowed terrain that is very difficult for humans to access to be captured at very high spatial and temporal resolutions. This paper deals with the application of this technique to the study of extreme topography in a near-vertical road cut-slope. Three photogrammetric projects were carried out: one derived from images taken with the camera oriented horizontally, one derived from images taken with the camera tilted at 45°, and one derived from both sets of images. Point clouds and orthophotos were generated for each of these projects. The best accuracies were achieved by the photogrammetric products derived from the combined images set, which had RMSE equal to 0.053 m, 0.070 m and 0.061 m in X, Y and Z direction, respectively. A software program was developed to generate contour lines and cross-sections derived from the point cloud, which was able to represent all terrain geometric characteristics, such as several Z coordinates for a given planimetric (X, Y) point. Furthermore, comparing the contour lines and cross-sections generated from the point cloud using the program developed in this project to those generated from the digital surface model showed that the former are capable of representing geometric terrain characteristics that the latter cannot. [ABSTRACT FROM AUTHOR]

Landslides that occur due to the rapid motion of a rock-mass are a primary risk in mountainous terrains and are a danger to human life and civil infrastructure. The application of geotechnical engineering methodologies for remedial purposes is to assess unpredictable behavior and the stability of the slope, and for analysis and design. The aim of the study is to quantify the global factor of safety (FOS) and describe a correlative and comparative assessment of the conventional Limit Equilibrium Model as compared with the advanced Numerical Model for the rock slopes of Amiya, Nainital, Uttarakhand in India utilizing Mohr-Coulomb failure criterion. The rock-mass characteristics were determined as a result of an estimation of the physio-mechanical properties of rock and debris from collected field samples with the help of the joint weakening coefficient for limit equilibrium (LE) and the numerical solution. Global FOS estimated by means of stability analysis procedures, such as Swedish slip circle, Ordinary method of slices, Modified Bishop, Janbu method, Finite difference static analysis, Finite element static, and Finite difference dynamic analysis, was found to be 0.65, 1.34, 1.38, 1.29, 1.57, 1.144, and 0.84, respectively. The results helped to deduce that the slope remains substantially stable and failure may only occur in the case of a down slope movement of rock debris (clastic mass of rocks) with a small local/global tremor. Combination of the LE technique and numerical approach (hybrid approach) has been found to be a better method for critical slip surface and FOS determination. [ABSTRACT FROM AUTHOR]

A massive landslide was triggered on 19 May 2017 at Vishnuprayag near Joshimath, Uttarakhand, India. The landslide blocked the Rishikesh-Badrinath highway and halted traffic for about 24 h till the debris was removed and the road reopened. The Hathi Pahar hill in Vishnuprayag has been a landslide-prone zone because of the presence of loose boulders and large rock blocks on very steep slopes. The site was visited immediately after the landslide and a rapid field assessment of slope stability was carried out. The stability analysis was performed with limited geological and geotechnical data collected from field and laboratory. Based on the two-dimensional finite element analysis, it was inferred that the slope is marginally stable under static load, but fails under seismic loading. The slope could fail in future due to triggering factors such as heavy rainfall, earthquake and anthropogenic activities. This communication presents field observations and the subsequent stability analysis along with possible suggestive control measures. [ABSTRACT FROM AUTHOR]

Large wood (LW) plays a key role in physical, chemical, environmental, and biological processes in most natural and seminatural streams. However, it is also a source of hydraulic hazard in anthropised territories. Recruitment from fluvial processes has been the subject of many studies, whereas less attention has been given to hillslope recruitment, which is linked to episodic and spatially distributed events and requires a reliable and accurate slope stability model and a hillslope-channel transfer model. The purpose of this study is to develop an innovative LW hillslope-recruitment estimation approach that combines forest stand characteristics in a spatially distributed form, a probabilistic multidimensional slope stability model able to include the reinforcement exerted by roots, and a hillslope-channel transfer procedure. The approach was tested on a small mountain headwater catchment in the eastern Italian Alps that is prone to shallow landslide and debris flow phenomena. The slope stability model (that had not been calibrated) provided accurate performances, in terms of unstable areas identification according to the landslide inventory (AUC = 0.832) and of LW volume estimation in comparison with LW volume produced by inventoried landslides (7702 m 3 corresponding to a recurrence time of about 30 years in the susceptibility curve). The results showed that most LW potentially mobilised by landslides does not reach the channel network (only about 16%), in agreement with the few data reported by other studies, as well as the data normalized for unit length of channel and unit length of channel per year (0–116 m 3 /km and 0–4 m 3 /km y −1 ). This study represents an important contribution to LW research. A rigorous and site-specific estimation of LW hillslope recruitment should, in fact, be an integral part of more general studies on LW dynamics, for forest planning and management, and positioning in-channel wood retention structures. [ABSTRACT FROM AUTHOR]

This paper proposes an approach for landslide inventory mapping considering actual conditions in Indonesia. No satisfactory landslide database exists. What exists is inadequate, focusing, on data response, rather than on pre-disaster preparedness and planning. The humid tropical climate also leads a rapid vegetation growth so past landslides signatures are covered by vegetation or dismantled by erosion process. Generating landslide inventory using standard techniques still seems difficult. A catalog of disasters from local government (village level) was used as a basis of participatory landslide inventory mapping. Eyewitnesses or landslide disaster victims were asked to participate in the reconstruction of past landslides. Field investigation focusing on active participation from communities with the use of an innovative technology was used to verify the landslide events recorded in the disaster catalog. Statistical analysis was also used to obtain the necessary relationships between geometric measurements, including the height of the slope and length of run out, area and volume of displaced materials, the probability distributions of landslide area and volume, and mobilization rate. The result shows that run out distance is proportional to the height of the slope. The frequency distribution calculated by using non-cumulative distribution empirically exhibits a power law (fractal statistic) even though rollover can also be found in the dataset. This cannot be the result of the censoring effect or incompleteness of the data because the landslide inventory dataset can be classified as having complete data or nearly complete data. The so-called participatory landslide inventory mapping method is expected to solve the difficulties of landslide inventory mapping and can be applied to support pre-disaster planning and preparedness action to reduce the landslide disaster risk in Indonesia. It may also supplement the usually incomplete data in a typical landslide inventory. [ABSTRACT FROM AUTHOR]

Stability analysis is of great significance to landslide hazard prevention, especially the dynamic stability. However, many existing stability analysis methods are difficult to analyse the continuous landslide stability and its changing regularities in a uniform criterion due to the unique landslide geological conditions. Based on the relationship between displacement monitoring data, deformation states and landslide stability, a state fusion entropy method is herein proposed to derive landslide instability through a comprehensive multi-attribute entropy analysis of deformation states, which are defined by a proposed joint clustering method combining K-means and a cloud model. Taking Xintan landslide as the detailed case study, cumulative state fusion entropy presents an obvious increasing trend after the landslide entered accelerative deformation stage and historical maxima match highly with landslide macroscopic deformation behaviours in key time nodes. Reasonable results are also obtained in its application to several other landslides in the Three Gorges Reservoir in China. Combined with field survey, state fusion entropy may serve for assessing landslide stability and judging landslide evolutionary stages. [ABSTRACT FROM AUTHOR]

In the face of climate change, the assessment of land transport infrastructure exposure towards adverse climate events is of major importance for Europe's economic prosperity and social wellbeing. In this study, a climate index estimating rainfall patterns which trigger landslides in central Europe is analysed until the end of this century and compared to present-day conditions. The analysis of the potential future development of landslide risk is based on an ensemble of dynamically downscaled climate projections which are driven by the SRES A1B socio-economic scenario. Resulting regional-scale climate change projections across central Europe are concatenated with Europe's road and railway network. Results indicate overall increases of landslide occurrence. While flat terrain at low altitudes exhibits an increase of about 1 more potentially landslide-inducing rainfall period per year until the end of this century, higher elevated regions are more affected and show increases of up to 14 additional periods. This general spatial distribution emerges in the near future (2021-2050) but becomes more pronounced in the remote future (2071-2100). Since largest increases are to be found in Alsace, potential impacts of an increasing amount of landslides are discussed using the example of a case study covering the Black Forest mountain range in Baden-Württemberg by further enriching the climate information with additional geodata. The findings derived are suitable to support political decision makers and European authorities in transport, freight and logistics by offering detailed information on which parts of Europe's ground transport network are at particularly high risk concerning landslide activity. [ABSTRACT FROM AUTHOR]

ABSTRACT: Resistivity monitoring surveys are used to detect temporal changes in the subsurface using repeated measurements over the same site. The positions of the electrodes are typically measured at the start of the survey program and possibly at occasional later times. In areas with unstable ground, such as landslide‐prone slopes, the positions of the electrodes can be displaced by ground movements. If this occurs at times when the positions of the electrodes are not directly measured, they have to be estimated. This can be done by interpolation or, as in recent developments, from the resistivity data using new inverse methods. The smoothness‐constrained least squares optimisation method can be modified to include the electrode positions as additional unknown parameters. The Jacobian matrices with the sensitivity of the apparent resistivity measurements to changes in the electrode positions are then required by the optimisation method. In this paper, a fast adjoint‐equation method is used to calculate the Jacobian matrices required by the least squares method to reduce the calculation time. In areas with large near‐surface resistivity contrasts, the inversion routine sometimes cannot accurately distinguish between electrode displacements and subsurface resistivity variations. To overcome this problem, the model for the initial time‐lapse dataset (with accurately known electrode positions) is used as the starting model for the inversion of the later‐time dataset. This greatly improves the accuracy of the estimated electrode positions compared to the use of a homogeneous half‐space starting model. In areas where the movement of the electrodes is expected to occur in a fixed direction, the method of transformations can be used to include this information as an additional constraint in the optimisation routine. [ABSTRACT FROM AUTHOR]

Landslides triggered by rainfall are a major multiple geological hazard on Qin-ba Mountain. Debris landslides in the shallow layer make up a large proportion of all landslides. A flume experiment was conducted to successfully initiate shallow debris landslides using artificial rainfall in Zhouzhi County, Qin-ba Mountain. The results confirmed a strong correlation between the deformation of the slope and pore-water pressure and moisture content. Slope deformation and failure were observed, and a possible mechanism was interpreted based on the experimental results. The primary effects of rainfall on shallow debris landslides on Qin-ba Mountain are the generation of a seepage force and a decrease in the effective stress of the sliding surface as a result of increasing pore-water pressure. The effect of rainfall on the shear strength parameters is inconspicuous, which might be attributed to the relatively high saturation of debris soils under natural conditions. The failure type in Test 1 is considered to be a progressive failure, which is a typical failure mode for homogeneous shallow debris slopes on Qin-ba Mountain, particularly for slopes exposed to artificial excavation. The deformation in Test 2 with higher density, merely taking the form of surface erosion, usually occurs in the regions with the least artificial disturbance. The two deformation types can be primarily attributed to differences in the soil density; the higher density reflected the smaller number and size of inside pores and led to lower soil permeability, which further influenced the distribution of the pore-water pressure, direction of seepage force, and slope deformation. Therefore, higher soil density changed by compaction is beneficial for the stability of shallow debris slopes on Qin-ba Mountain, even with high pore-water pressure. In subsequent work, a series of experiments with various densities should be conducted to explore the dividing point between the two deformation modes, and in addition, tests with larger slope gradients should also be considered. [ABSTRACT FROM AUTHOR]

The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and −2 Log likelihood, Cox & SnellR-Square and NagelkerkeR-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making. [ABSTRACT FROM PUBLISHER]

The occurrence of landslide in the hilly region of Darjeeling during monsoon season is a matter of serious concern. Every year this natural hazard damages the major roads at several places and thus disrupts the transport and communication system in this region. This paper tries to prepare a landslide susceptibility zone (LSZ) map for the Gish River basin. A total number of 16 spatial parameters have been taken for this study and these are categorised under six factor clusters or groups for example, triggering factors, protective factor, lithological factors, morphometric factors, hydrological factors and anthropogenic factors. The LSZ map is prepared by integrating all the parameters adopting the weighting base as logistic regression. The landslide susceptibility map shows that nearly 9.11% of the area falls under the very high landslide-susceptible zone while 40.28% of the area of the total basin lies under the very low landslide-susceptible zone. The landslide-susceptible model is validated through the receiver operating characteristic curve. This curve shows 86% success rate in defining landslide-susceptible zones and 83.40% prediction rate for the occurrence of landslides. The spatial relationship between the landslide susceptibility model and other factors’ groups shows that the morphometric factors’ cluster (mainly slope) is the focalone for the determination of landslide-susceptible zone. [ABSTRACT FROM PUBLISHER]

The paper deals with the use of thermography to assess landslide risk. Landslide is a natural process which affects soil. In specific conditions, it can even become cause of death and damages to buildings and structures. Due to climate changes, landslide events have increased in the last years. Sensor networks are today widely used to monitor landslide triggering events. Main problems concern the development costs of such networks, so today attention is principally focused on the use of measurement systems which allow to monitor wide zones at distance. Recently researchers are considering the use of infrared thermography as a promising tool for monitoring landslide process. The paper aims to describe the current potentialities of thermography and its limitations. Several issues and open problems are still to be faced before considering such technique as an effective monitoring tool. For this reason, the author reports the results of a measurement campaign to prove the strengths and weakness of the methodology. Relevant aspects concerning the limits of this measurement technique are described in the following to provide suggestions to users and researchers working in this field. [ABSTRACT FROM AUTHOR]

This study evaluated the generalizability of five models to select a suitable approach for landslide susceptibility modeling in data-scarce environments. In total, 418 landslide inventories and 18 landslide conditioning factors were analyzed. Multicollinearity and factor optimization were investigated before data modeling, and two experiments were then conducted. In each experiment, five susceptibility maps were produced based on support vector machine (SVM), random forest (RF), weight-of-evidence (WoE), ridge regression (Rid_R), and robust regression (RR) models. The highest accuracy ( AUC = 0.85) was achieved with the SVM model when either the full or limited landslide inventories were used. Furthermore, the RF and WoE models were severely affected when less landslide samples were used for training. The other models were affected slightly when the training samples were limited. [ABSTRACT FROM AUTHOR]

On February 14, 2010, a large landslide affected the urban centre of San Fratello town (Sicily Island, Southern Italy), causing severe damage to buildings, roadways, and infrastructure, as well as about 2000 evacuees. This large complex landslide, covering more than 1 km2 in extension, represents one of the major phenomena that ever occurred in Sicily. In order to manage the landslide risk, the civil protection system was activated as part of the initial response to the emergency (the “emergency phase”). This involved the Civil Protection Departments both at national (DPC) and regional (DRPC) levels, as well as scientific institutions (namely “Competence centres”, CdCs), local administration personnel, and technicians. On March 8, 2010, during the post-event recovery phase, a ground-based synthetic aperture radar (GB-InSAR) system was installed in order to monitor the ground surface deformation, assess the landslide residual risk, and analyse its displacement trend. Accurate field surveys and building inspections were also performed for a validation of the GB-InSAR data, in order to map the ground deformation, plan building evacuation-demolishment, as well as check the efficiency of the landslide mitigation works. This paper describes the outcomes of the 57 month monitoring campaign (March 2010–December 2014), reporting the use of GB-InSAR data for near real-time monitoring, mapping, and post-emergency/recovery management activities. The final aim was to provide the civil protection personnel with a reliable, rapid, and easy communication system of the monitoring results, designed to an enhance understanding of the landslide phenomena, and to support the decision-making process. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to establish a data-driven approach and assess its potential for shallow landslide susceptibility mapping of the Shennongjia Forestry District, China. For the data processing, Fisher segmenting was used for the classification of topographical factors (gradient, relief amplitude, plan curvature, and normalized difference vegetation index) and the improved Otsu method was used to determine the buffer length thresholds of the locational factors (proximity to roads, rivers and faults). The computations show that these two objective methods help to avoid the uncertainty caused by the subjective judgments and the obtained thresholds are more consistent with actual conditions. Considering the different data types, the locational and topographical factors were evaluated by fuzzy comprehensive evaluation while the geological factors were evaluated by frequency index, and then the three separate evaluations were combined using radar chart analysis. The validation result shows that the susceptibility map based on a data-driven approach has a high accuracy of prediction and this approach will be salutary for risk mitigation and land-use management in mountainous regions. [ABSTRACT FROM AUTHOR]

Landslide is a natural hazard that results in many economic damages and human losses every year. Numerous researchers have studied landslide susceptibility mapping (LSM), each attempting to improve the accuracy of the final outputs. However, few studies have been published on the training data selection effects on the LSM. Thus, this study assesses the training landslides random selection effects on support vector machine (SVM) accuracy, logistic regression (LR) and artificial neural networks (ANN) models for LSM in a catchment at the Dodangeh watershed, Mazandaran province, Iran. A 160 landslide locations inventory was collected by Geological Survey of Iran for this investigation. Different methods were implemented to define the landslide locations, such as inventory reports, satellite images and field survey. Moreover, 14 landslide conditioning factors were considered in the analysis of landslide susceptibility. These factors include curvature, plan curvature, profile curvature, altitude, slope angle, slope aspect, distance to faults, distance to stream, topographic wetness index, stream power index, terrain roughness index, sediment transport index, lithology and land use. The results show that the random landslide training data selection affected the parameter estimations of the SVM, LR and ANN algorithms. The results also show that the training samples selection had an effect on the accuracy of the susceptibility model because landslide conditioning factors vary according to the geographic locations in the study area. The LR model was found to be less sensitive than the SVM and ANN models to the training samples selection. Validation results showed that SVM and LR models outperformed the ANN model for all scenarios. The average overall accuracy of LR, SVM and ANN models are 81.42%, 79.82% and 70.2%, respectively. [ABSTRACT FROM AUTHOR]