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As one of the most destructive geological disasters, a myriad of landslides has revived and developed in the Three Gorges Reservoir area under the combined action of various detrimental factors. Therefore, the pertinently regional landslide susceptibility mapping (LSM) is of great significance for disaster prevention and mitigation. In this study, LSM is prepared by using a boosting-C5.0 decision tree model. Under the landslide verification of on-site investigations, the study area is divided into accumulation and rock areas, and a total of 12 impact factors are selected. TOL and VIF are employed to determine the multicollinearity among the impact factors. The independent training (80%) and validation (20%) datasets are constructed by random sampling for LSM. ANN, C5.0, and SVM are selected for comparative analysis. The results show that there is no rigorous multicollinearity among the impact factors proposed in this paper. The landslide susceptibility in the study area is divided into low, moderate, high, and very high. The highest susceptibility area distributes along the riverside where the landslide ratio is 37.05% in boosting-C5.0 model. Then the ROCs are expropriated to infer the accuracy of each model. The boosting-C5.0 performs the best with the largest area under the curve in both accumulation and rock areas, reaching at 0.991 and 0.990 in the validation sets, respectively. Finally, the composite modification of the 5 validation sets shows that the uncertainty of boosting-C5.0 is concentrated in the intermediate probability areas of susceptibility. This study reveals the feasibility of machine learning in landslide susceptibility assessment, which could provide a basis for the risk management and control of geological disasters. [ABSTRACT FROM AUTHOR]

Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020). [ABSTRACT FROM AUTHOR]

Most previous studies on the quantitative risk assessment (QRA) of landslides focused on the probability of slope failure at the pre-failure stage and adopted empirical models for consequence analysis. The conventional approaches simplify the relationship between the pre-failure state and the post-failure behavior and cannot reasonably account for the effects of uncertainty on the entire landslide process. In this paper, an efficient QRA method that involves the direct simulation of the entire landslide process is proposed. A QRA formula that considers the probability of only those landslides that can impact the element at risk is used. The coupled Eulerian–Lagrangian method is used to simulate the entire landslide process and to identify slopes that can impact the element at risk and determine the failure consequences. The subset simulation method is adopted to efficiently estimate the probability of landslide impact, and parameter uncertainty is considered. Two case histories of landslides are investigated. First, the 2011 Baqiao loess landslide in Xi'an, China, is investigated, and the results of the proposed method are compared with those of the conventional approaches. Second, the proposed method is applied to assess the risk of the 2015 Ganjingzi landslide in the Three Gorges Reservoir. The effects of the risk mitigation works are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, the artificial bee colony (ABC) algorithm is used to generate landslide susceptibility maps. To the best of our knowledge, it is the first time the ABC algorithm is applied as a classifier on landslide susceptibility mapping. Twelve features are considered as inputs for the ABC model in this research work. These features contain redundant and correlated information. Therefore, normalization and dimension reduction are applied in the pre-processing stage. Three different normalization methods are investigated, i.e. minimum and maximum (Min-Max), mean standard deviation (Mean-SD), and median and interquartile range (Med-IQR). Two dimensionality reduction techniques, i.e., principal component analysis (PCA) and canonical variate analysis (CVA), are utilized to deal with high-dimensional information and eliminate correlated information in the data. In total, six different schemes of input data based on the three normalization methods and two techniques of dimension reduction are applied to train the ABC model. There are a total of 382 landslide incidents occurred at various locations on Penang Island, out of which 255 are selected for training and remaining 127 are used for the validation purpose of the model. The model classification accuracies and receiver of characteristics (ROC) curve are applied to evaluate the model's performance. The results show that the ABC model trained using Min-Max normalization and CVA scheme generates the best results among these six schemes. The landslide susceptibility maps generated in this research offer significant information for landslide risk management. [ABSTRACT FROM AUTHOR]

Long-runout landslides can be extremely hazardous and unpredictable. The prediction of long-runout landslides is challenging because it is unclear what factors control their mobility, and it is unclear how to best measure their runout given that they traverse changing topography. In this paper, we document the development and statistical evaluation of a Landslide Runout Score (LRS) system to predict short, medium, and long runout using a new mobility measure, the unitless Runout Number L/A1/2 (where L is landslide length and A is landslide area). The Runout Number has previously been correlated to three geomorphological factors (planimetric curvature, sand content, and upslope contributing area normalized to landslide area), so the LRS uses these factors as inputs. These factors are readily calculated using geographic information systems (GIS) and publicly available data sources. The LRS system predicts runout categories (short, medium, and long) with one-vs.-all accuracies (i.e., percentage of correct predictions within a given class) of 75, 58, and 72%, respectively, for a total weighted accuracy of approximately 65%. The results of this work are summarized in a worksheet that can be used by geologists and engineers to develop preliminary predictions of landslide runout behavior, which can be incorporated into or used alongside regional-scale landslide hazard assessments. [ABSTRACT FROM AUTHOR]

Landslides are common occurrences along Chinese rail routes. Accordingly, the accurate determination of sliding-surface locations and the underlying causes become critical from the landslide-disaster-management and loss-mitigation viewpoints. In this study, the landslide in the K66 section of the Jiheng Railway (installed in Jinggangshan City, Jiangxi, China) was investigated. The causative factors and characteristics of the landslide were analyzed using geophysical methods as well as drilling and monitoring techniques. The results reveal that a combination of the transient electromagnetic method and electrical resistivity tomography can be used to identify the location of water-rich landslide-prone regions. When combined with the drilling information, these results reveal the tendency of landslide occurrence in regions comprising strongly weathered carbonaceous shale. Moreover, an uneven slope deformation is observed along the railway line. The seepage of surface water into the slope is revealed as the primary cause of landslides. This is because the reduced strength of the strongly weathered carbonaceous shale facilitates the formation of sliding surfaces. This paper presents a comprehensive survey plan for landslides occurring along railway routes. Furthermore, an assessment of the risk of future landslide occurrences in the investigation area is presented, thereby providing an appropriate reference for landslide prevention and loss mitigation. [ABSTRACT FROM AUTHOR]

Landslide is a uniquely dynamic large-deformation process that can present serious threat to human lives and infrastructures. The natural soil properties often exhibit inherent spatial variability, which affects the landslide behavior significantly. This paper focuses on combined Monte Carlo simulation and three-dimensional (3D) dynamic large-deformation finite-element (LDFE) analysis using the coupled Eulerian-Lagrangian method to investigate the whole runout process of landslide induced by the earthquake in spatially varying soil. The results from LDFE analysis show that the mean value of runout distance in spatially varying soil is significantly higher than that of the deterministic value obtained from a homogeneous slope due to the slope failure developed along the weakest path in soils. The mean runout distance increases and converges with increasing slope length in 3D-LDFE stochastic analysis. The advantages and necessities of 3D-LDFE analysis were illustrated by comparing it with two-dimensional (2D) LDFE analysis of landslide in spatially varying soil. The results show that the calculated mean runout distance using 3D-LDFE method is at least 16.1% higher than that calculated using 2D-LDFE analysis. Finally, a linear regression formula was established to estimate the mean runout distance of landslide due to horizontal inertia acceleration. Such a formula may facilitate the risk assessment of landslide in practical engineering. [ABSTRACT FROM AUTHOR]

Giant landslides can cause significant damage to their dynamic runout processes. Understanding the mechanics of the runout process is essential for landslide risk assessment and mitigation design. This paper investigates the runout process of the 1.16 × 109 m3 giant Daguangbao landslide triggered by the 2008 Ms 8.0 Wenchuan earthquake. The Daguangbao landslide is a typical bedding-plane slide and the properties of basal sliding surface play a key role in the runout behaviors of the landslide. The material point method (MPM) is adopted as a numerical tool of the simulations. A thermal-based velocity-weakening friction law is used to simulate the contact between the sliding body and the basal sliding surface. A strain-softening constitutive model is used to evaluate the damages inside the landslide body and their effects on the runout behaviors of the landslide. Numerical results reveal that landslide mass slides along the bedding-plane as a whole body initially and then disintegrates during the runout process. The calculated duration of sliding is 64 s and the maximum velocity reaches 60 m/s. The friction coefficient of the slip surface decreases sharply as the landslide body starts to move, and a steady-state friction coefficient μ ≈ 0.06 is reached when the velocity exceeds approximately 20 m/s. Friction degradation of the slip surface shows a sensible influence on the final runout distance and the depth of the deposit zone. The dynamic fragmentation of the landslide body, the final runout distance, and deposition topography are also significantly affected by material softening. [ABSTRACT FROM AUTHOR]

The great M ~ 8 1920 Haiyuan earthquake (HYEQ) was one of the largest and most deadly earthquakes in China in the last century, with ~ 234,000 deaths. The earthquake occurred within the Loess Plateau of northern China, where Quaternary loess deposits form a distinctive blanket across the landscape. Large regions of this loess cover experienced co-seismic landslides. Based on an analysis of the original disaster reports, field surveys, and satellite image interpretation, we have compiled the shaking effects of the earthquake, including the distribution of landslides, fatalities, and structural damage. Landslides triggered by the HYEQ (n > 7,000) are concentrated south of the Haiyuan fault, in a region that has both thick loess cover and long-term relief generated by the drainage network. This distribution is spatially separated from landslides triggered by other earthquakes. We find that in contrast to previous studies, the most important factor in the severe death toll of the HYEQ was the collapse of housing by ground shaking, including collapse of loess house-caves. Landslides were a secondary factor; although up to 32,000 deaths occurred in areas with intense landsliding. Based on the revised distribution pattern of landslides and damage (e.g., house collapses), we suggest that the isoseismal intensity IX line extends south of previous locations. We have also identified 126 dammed lakes created by co-seismic landslides, which form major modifications of this semi-arid landscape. The research methods in this paper, combining historical review, satellite image interpretation, and field validation of landslides, can be used as a reference for studies of other areas affected by historical earthquakes and co-seismic landslides, elsewhere in the Loess Plateau and beyond. [ABSTRACT FROM AUTHOR]

Landslide susceptibility map (LSM) provide useful tool for decision makers in hazard mitigation concerns. In the present paper, a novel hybrid block-based neural network model (HBNN) for the purpose of producing high-resolution LSM was developed. This hybrid approach was found through the mixture of expert modular structures and divide-and-conquer strategy incorporated with genetic algorithm (GA). The introduced HBNN then was applied on southern part of Guilan province (north of Iran) using 14 causative factors covering topographic and geomorphologic features, and geological and tectonical factors as well as hydrology, land data, and climate conditions. The landslide inventory map was provided using a synergy work from monitored events, interpretation of aerial photographs, and carried out geotechnical investigations in the area as well as field surveys. To insight, the predictability of proposed HBNN was compared with two developed models using multilayer perceptrons (MLPs) and generalized feed forward neural network (GFFN). The generated LSM was validated using receiver operating characteristic (ROC) curves, statistical error indices, and sensitivity and weight analyses as well as monitored landslides. Based on the compared metrics, HBNN with 86.52% and 90.15% in prediction and success rate as well as 89.36% for precision-recall curve demonstrated more consistent tool for future landslide susceptibility zonation. This implies on capability of developed HBNN in producing higher resolution and more reliable LSM for urban and land-use planners. [ABSTRACT FROM AUTHOR]

In a strong earthquake, not only a large number of coseismic landslides are triggered, but relaxation and cracks in the rocks and soils are induced, which make these rocks and soils vulnerable to instability during subsequent rainfall; thus, strong earthquakes always have long-term effects on landslides. The geo-hazards along the Zhaotong-Qiaojia road in the 2014 Ludian earthquake (Ms 6.5) of Yunnan Province, China, were investigated, and the Ganjiazhai giant landslide was chosen as a case study. First, using the limit equilibrium analysis and Newmark method, the critical seismic intensity of the landslide before the earthquake was evaluated. Secondly, the dynamic failure process of the landslide under the measured ground motion was simulated with the discontinuous deformation analysis method. Lastly, based on stress–seepage coupling analysis and precipitation data from Ludian meteorological station, the stability of the landslide during subsequent rainfall after the earthquake was predicted. The results show that the critical seismic intensity was within degrees VIII–IX, which is consistent with the results of the earthquake damage investigation. The dynamic failure process can be divided into four stages, and four scarps formed; the potential sliding zones during the subsequent rainfall were at the first scarp and the fourth scarp, and their critical rainfall amounts were 35–40 mm and 55–60 mm, respectively. In this paper, failure process simulation and stability prediction of the landslide before, during, and after the strong earthquake are presented, which provide analysis methods for the dynamic stability of landslides in the meizoseismal area. [ABSTRACT FROM AUTHOR]

This paper focuses on the evaluation of temporal and spatial propagations of impulse waves using 3D numerical simulation-based models for a potential landslide area in the Tersun Dam reservoir, Northeast Turkey. The topographical model was created using Stereo Lithography files, and a drift-flux model was used to simulate the landslides. The wave generation was simulated using the Reynolds-averaged Navier–Stokes equations. The Drift-Flux approach was selected, and a renormalization group-based k-ε turbulence model was used to create a fluid–solid coupled model. The results show that a 16.5-m impulse wave is created as a result of sliding material hitting the water at a velocity of 16.4 m/s. The wave will reach the opposite shore in 27.4 s, and the run-up height will reach up to 48.8 m. As the wave, with an average velocity of 11.6 m/s in the reservoir, propagates toward the dam body, its height will decrease, and a 3.7-m-high wave will hit the dam in 155 s. A validation analysis performed using empirical equations and laboratory model tests indicates small differences in the results. The main reason for these differences is that the evaluation of wave properties is independent of the type of mass movement in empirical methods. A proper stability analysis is required to have a better estimation of the volume and the velocity of the sliding mass. [ABSTRACT FROM AUTHOR]

Multi-platform remote sensing using space-, airborne and ground-based sensors has become essential tools for landslide assessment and disaster-risk prevention. Over the last 30 years, the multiplicity of Earth Observation satellites mission ensures uninterrupted optical and radar imagery archives. With the popularization of Unmanned Aerial Vehicles, free optical and radar imagery with high revisiting time, ground and aerial possibilities to perform high-resolution 3D point clouds and derived digital elevation models, it can make it difficult to choose the appropriate method for risk assessment. The aim of this paper is to review the mainstream remote-sensing methods commonly employed for landslide assessment, as well as processing. The purpose is to understand how remote-sensing techniques can be useful for landslide hazard detection and monitoring taking into consideration several constraints such as field location or costs of surveys. First we focus on the suitability of terrestrial, aerial and spaceborne systems that have been widely used for landslide assessment to underline their benefits and drawbacks for data acquisition, processing and interpretation. Several examples of application are presented such as Interferometry Synthetic Aperture Radar (InSAR), lasergrammetry, Terrestrial Optical Photogrammetry. Some of these techniques are unsuitable for slow moving landslides, others limited to large areas and others to local investigations. It can be complicated to select the most appropriate system. Today, the key for understanding landslides is the complementarity of methods and the automation of the data processing. All the mentioned approaches can be coupled (from field monitoring to satellite images analysis) to improve risk management, and the real challenge is to improve automatic solution for landslide recognition and monitoring for the implementation of near real-time emergency systems. [ABSTRACT FROM AUTHOR]

A large catastrophic landslide occurred on July 23, 2019, after a continuous heavy rainfall at Jichang (N 26°15′22.99″, E 104°40′6.33″), Shuicheng county, Guizhou province, China. This landslide had a maximum runout distance of 1295 m, a maximum falling height of 477 m between the highest point of the source area and the leading edge, and a maximum speed of 45.5 m/s. The 1.4 million cubic meters of debris instantly buried twenty-one buildings, destroyed six buildings, and resulted in 42 fatalities and 9 people missing. This paper analyzes the extent and mechanisms of initiation and runout distance of the landslide. The landslide occurred in the eluvial and deluvial accumulation of basalt, and its initiation mechanisms were different from the bedrock landslides that are typical of the interbedded soft and hard rocks in Guizhou and basalt landslides in Southwest China. The prolonged heavy rainfall, lush vegetation, and road construction played the roles in landslide initiation. Subsequently, the landslide can be divided into three sliding regions caused by the valley on both sides and the stepped terrain in the middle, and the valley topography enhanced the disaster intensity and reach. [ABSTRACT FROM AUTHOR]

Landslide is a disaster which may cause huge losses of human life and block the traffic on hilly area. In this paper, we present a new physically based model to simulate the dynamic flow of landslides, under a modified MPM (material point method) framework. To realistically simulate the characteristics of fracture and flow of soil medium in landslide, we introduce the modified Cambridge clay model (MCCM) from soil dynamics into the yield surface criterion to model the dynamic process of landslides. The interaction between soil and rock in the landslide is simulated by a level-set-based two-way fluid–solid coupling algorithm. Meanwhile, we propose a GPU-based optimization to calculate the signed distance function in level set to improve the efficiency of collision detection. We also simplify the hardening and softening parameter calculation algorithm of MCCM to reduce the calculation involved in landslide simulation. By choosing different values of the material yield surface parameters, various kinds of landslide disaster scenes with different cover area are successfully generated, including rocks rolling from hill, soil and rock collapsing, landslide flowing, and covering the road and cars. Experimental results demonstrate the potential of our method. [ABSTRACT FROM AUTHOR]

Natural disaster is one of the most important research topics worldwide. In this paper, a data driven cyber-physical system is introduced to detect landslides. This system is composed of Wi-Sun acceleration sensors, which can detect the acceleration of the nearby environment in 3D domain, and the sensors are linked with the router (act as 'sink' node) via Wi-Sun transmission (i.e. IEEE802.15.4g). The details of the detection system are explained and the landslide detection mechanism with low computational complexity is proposed. A traffic reduction method is proposed thereafter to help reduce the data needed for transmission by exploring the intra-correlations of the sensor data. This method can save the energy consumption without degrading the detection performance. Field test is conducted and the results show that the landslide can be detected and amount of data to be transmitted can be reduced, which verifies the system's effectiveness. [ABSTRACT FROM AUTHOR]

Chalk exposures in coastal cliff faces extend from Yorkshire to Devon in England and from Boulonnais to Normandy in France. The failure of chalk cliffs is a long-recognised hazard along parts of these coastlines; however, variation in the chalk's lithology, stratigraphy and physical properties coupled with changing topographic relief, means that the nature of the hazard is variable. This paper is concerned with cliff failures that may lead to chalk flow resulting in catastrophic flow slide or sturzstrom-like behaviour. The paper discusses the nature of chalk flows and how they relate to other types of flow-like sturzstroms. Recently published and unpublished work is used to unify simple mechanical index properties between the French and English coastal chalks. Some well-documented studies are then re-examined, using these results and in the light of new findings on sturzstroms to show how runout is controlled by chalk porosity. Finally, a series of physical model tests are described in which some of the controls on runout of the chalk are examined. [ABSTRACT FROM AUTHOR]

Adjacent debris flows may interact in many ways: two or more concurrent debris flows may merge; one debris flow can run out over an existing debris flow fan. Such interactions may cause debris flow properties to change in the mixing process as well as more severe adverse effects than those caused by a single debris flow. This paper aims to investigate the interactions among channelized debris flows originated from adjacent catchments. Both concurrent and successive debris flows are considered. If several debris flows originate from different locations concurrently and merge, the volumetric sediment concentration (i.e., the ratio of the volume of solid material to the total volume of debris flow), C , is a good index to capture the mixing process of these debris flows. The change in C reflects where mixing occurs and the mixing degree. The debris flow properties (e.g., yield stress and dynamic viscosity) evolve in the mixing process and can be captured by the change in C . The debris flow with a larger volume dominates the mixing process, and the properties of the mixed debris flow are more similar to those of the larger debris flow. The inundated areas and runout distances of successive debris flows are smaller than those of concurrent debris flows of the same total volume due to the smaller scales of the individual events and blockage by the earlier debris flows. However, the deposit depth in the interacting part of the debris flow fans of successive debris flows can be much larger than that of concurrent debris flows, leading to more destructive cascading hazards (e.g., the formation of debris barrier lakes). The sequence of successive debris flows not only significantly influences the runout characteristics of the debris flows but also substantially affects the cascading hazards. [ABSTRACT FROM AUTHOR]

The spatial resolution of digital elevation models (DEMs) is an important factor for reliable landslide studies. Multi-interferometric techniques such as persistent scatterer interferometric synthetic aperture radar (PSInSAR) are used to evaluate the landslide state of activity and its ground deformation velocity, which is commonly measured along the satellite line of sight (LOS). In order to compare velocities measured by different satellites in different periods, their values can be projected along the steepest slope direction, which is the most probable direction of real movement. In order to achieve this result, DEM-derived products are needed. In this paper, the effectiveness of different DEM resolutions was evaluated in order to project ground deformation velocities measured by means of PSInSAR technique in two different case studies in the Messina Province (Sicily, southern Italy): San Fratello and Giampilieri. Three DEMs were used: (i) a 20-m resolution DEM of the Italian Military Geographic Institute (IGM), (ii) a 2-m resolution DEM derived from airborne laser scanning (ALS) light detection and ranging (LiDAR) data for the San Fratello 2010 landslide, and (iii) a 1-m resolution DEM derived from ALS LiDAR data for the area of Giampilieri. The evaluation of the applied method effectiveness was performed by comparing the DEMs elevation with those of each single permanent scatterer (PS) and projecting the measured velocities along the steepest slope direction. Results highlight that the higher DEM resolution is more suitable for this type of analysis; in particular, the PS located nearby the watershed divides is affected by geometrical problems when their velocities are projected along the steepest slope. [ABSTRACT FROM AUTHOR]

This paper presents rainfall-induced landslide thresholds and predicts landslide hazard in Kuala Lumpur metropolitan city and surrounding areas. Landslide events from 2000 to 2012 were collected. The long and short antecedent rainfall days were prepared for landslide and non-landslide days simultaneously. First, threshold analysis was conducted by using data obtained from rainfall stations located in highly urbanized areas of Kuala Lumpur metropolis. Six rainfall gauges were selected, and the study area was divided into six zones according to rainfall gauges: Taman Desa Station (TD-KL), Genting Klang (GK-KL), LDG Edinburgh Station (LDGE-KL), SG Raya Hulu Langat Station (SRHL-Slg), Puchong Drop Station (PD-Slg), and Bukit Antarabangsa (BTA-Slg). After the threshold analysis was conducted for different periods (10, 15, and 30 days) in each station, reliability index test was conducted to optimize the best threshold that limits the predicted events along the study period for each region. Second, the threshold analysis results were used as input in the Poisson probability model to estimate landslide temporal probability ( P). Third, the spatial probability ( P) analysis was prepared by using the evidential belief function multiplied by the P to obtain the hazard maps for 1-, 3-, and 5-year scenarios. Finally, a validation process was conducted to test the prediction performance of the resultant hazard map for a 1- and 2-year prediction by using the landslide inventory of 2012 to early 2014, which was not included in the modeling of the hazard map. Results showed a valid correlation between the high and moderate hazardous areas for the six zones. The predicted hazard maps indicated a quantitative assessment of the prone areas and proved to be a valid disaster management tool. The produced hazard maps may play a vital role as input component in risk analysis. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of two bivariate statistical approaches, frequency ratio (FR) and weights-of-evidence (WoE) for landslide susceptibility mapping (LSM) at south-western part of Saudi Arabia, Jizan region. Landslide locations were identified and mapped from interpretation of high resolution satellite images (GeoEye 0.5 m and QuikeBird 0.6m), topographic maps (scale of 1:10,000), historical records, and extensive field surveys. A total of 106 landslide locations were mapped using Arc-GIS software and divided into two groups, 75% and 25% of landslide locations were used for training and validation of models, respectively. Eleven landslide conditioning factors such as elevation, slope, curvature, aspect, lithology, topographic wetness index (TWI), normalized difference vegetation index (NDVI), proximity to lineament, roads and rivers were considered in this evaluation. The effects of these factors on landslide occurrence were assessed using aforementioned bivariate statistical approaches. For validation, the models results were compared with landslide locations which were not used during the models building. Subsequently, the receiver operating characteristic (ROC) curves were established and area under the curves (AUC) was calculated for the landslide susceptibility maps using the success (training data) and prediction (validation data) rates. The results showed that the area under the curve for success rates are 0.861 (86.1%) and 0.839 (83.9%) and for prediction rates are 0.796 (79.6%) and 0.791 (79.1%), respectively for frequency ratio and weight-of-evidence models. The resulting landslide susceptibility maps showed five classes of susceptibility such as very high, high, moderate, low, and very low. The percentage of existing training and validating landslides data in high and very high zones of the susceptibility maps were calculated to be 90.02% and 76.03% for frequency ratio model and 88.33% and 79.3% for weight-of-evidence model, respectively. The results revealed that the frequency ratio and weights-of-evidence models produced reasonable accuracy. The resultant maps would be useful and can also help planners for general choosing favorable locations for development schemes, such as infrastructural, buildings, road constructions, and environmental protection. [ABSTRACT FROM AUTHOR]

A landslide inventory map, which shows the location of landslide phenomena and contains information about movement type, activity, etc., is a basic element for landslide susceptibility and risk assessment. For this reason, the evaluation of the quality, in terms of accuracy and completeness, of landslide inventory maps is an important issue. In this paper, two landslide inventory maps are compared, in order to determine the corresponding quality, through a direct comparison, aimed to evaluate the degree of cartographic matching between the maps, and the determination of the statistical properties of landslide areas and the comparison between the frequency-area statistics of landslides contained in the two inventories. The two landslide inventory maps at 1:25,000 scale, used for these analyses, have been produced for Daunia region (Apulia, Southern Italy), by the Apulia River Basin Authority; the first, 'archive inventory', by unifying the existing archive inventories; the second, 'surveyed inventory', through aerial-photo interpretation and field investigations. [ABSTRACT FROM AUTHOR]

An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. CHAID as a multivariate method has an automatic classification capacity to analyze large numbers of landslide conditioning factors. Moreover, it results two or more nodes for each independent variable, where every node contains numbers of presence or absence of landslides (dependent variable). Other DT methods such as Quick, Unbiased, Efficient Statistic Tree (QUEST) and Classification and Regression Trees (CRT) are not able to produce multi branches based tree. Thus, the main objective of this paper is to use CHAID method to perform the best classification fit for each conditioning factors, then, combined it with logistic regression (LR) to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. In the first step, a landslide inventory map with 296 landslide locations were extracted from various sources over the Pohang-Kyeong Joo catchment (South Korea). Then, the inventory was randomly split into two datasets, 70 % was used for training the models, and the remaining 30 % was used for validation purpose. Thirteen landslide conditioning factors were used for the susceptibility modeling. Then, CHAID was applied and revealed that some conditioning factors such as altitude, soil drain, soil texture and TWI, as terminal nodes and reflected the best classification fit. Then, a proposed ensemble technique was applied and the interpretations of the coefficients showed that the relationship between the decision tree branch nodes distance from drain, soil drain, and TWI, respectively, leads to better consequences assessment of landslides in the current study area. The validation results showed that both success and prediction rates, 75 and 79 %, respectively. This study proved the efficiency and reliability of ensemble DT and LR model in landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Landslide susceptibility (LS) assessment by indirect approaches presents some limitations due to (1) the tendency to simplify the environmental factors (i.e., variables) and (2) the assumptions that landslides occur under the same combination of variables for a study site. Recently, some authors have discussed the interest to introduce expert knowledge in the indirect approaches in order to improve the quality of indirect LS maps. However, if the results are reliable, the procedures used seem fastidious and a very good knowledge of the study site is essential. The objectives of this paper are to discuss a methodology to introduce the expert knowledge in the indirect mapping process. After the definition of the expert rules associated to three landslide types, several indirect LS maps are produced by two indirect exploratory approaches, based on fuzzy set theory and on a modification of a bivariate method called expert weight of evidence. Then, the indirect LS maps are confronted to a landslide inventory and a LS map produced by a direct approach. The analyses indicate that the methodology used to introduce the expert rules in the mapping process increases the predictive power of indirect LS map. Finally, some indications about advantages and drawbacks of each approach are given to help the geoscientist to introduce his expert knowledge in the landslide susceptibility mapping process. [ABSTRACT FROM AUTHOR]

There is a clear need for integrated research on landslide disaster risk. Landslide disasters have major impacts in developing countries due to the increasing social vulnerability of both rural and urban communities. In recent decades, landslide disasters in Latin America triggered by both precipitation and earthquakes have also increased considerably. Thus, scientific contributions based on integrated risk research are quite urgent for improving the knowledge base for reducing the vulnerability of exposed communities to landslides. Thus, there is a parallel necessity to promote capacity building for young scientists in Latin America by considering the shift of disaster paradigm to recognize the 'unnaturalness' of disasters. Under such a framework, there are particular goals to be pursued including: (1) Development of landslide regional networks with a commitment for understanding risk as a socially constructed process; (2) Engagement of young scientists in integrated landslide risk research; (3) Inducing a scientific multi- and transdisciplinary approach for integrated landslide risk research; (4) Development and implementation of capacity building; (5) Contributing to the dissemination and application of common methodologies on landslide disasters investigations; and (6) Strengthening collaboration on integrated landslide disaster risk research in Latin America. In this paper, we present one of the main activities of the ICL Latin-American network in terms of capacity building carried out in 2013; to that end, the first international workshop on forensic investigations of disasters associated with landslides was held in the University of Sciences and Arts of Chiapas in the city of Tuxtla Gutierrez, Chiapas, Mexico, from June 26th to July 4th, 2013. [ABSTRACT FROM AUTHOR]

Landslide hazard is a complex nonlinear dynamical system with uncertainty. The evolution of landslide is influenced by many factors such as tectonic, rainfall and reservoir level fluctuation. Using a time series model, total accumulative displacement of landslide can be divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes in landslide displacement and inducing factors. In this paper, a novel neural network technique called ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. Grey relational analysis is used to sieve out the more influential inducing factors as the inputs in E-ELM. Trend component displacement and periodic component displacement are forecasted, respectively; then, total predictive displacement is obtained by adding the calculated predictive displacement value of each sub. Performances of our model are evaluated by using real data from Baishuihe landslide in the Three Gorges Reservoir of China, and it provides a good representation of the measured slide displacement behavior. [ABSTRACT FROM AUTHOR]

Landslides cause extensive loss of life and property in the Nepal Himalaya. Since the late 1980s, different mathematical models have been developed and applied for landslide susceptibility mapping and hazard assessment in Nepal. The main goal of this paper is to apply fuzzy logic to landslide susceptibility mapping in the Ghurmi-Dhad Khola area, Eastern Nepal. Seven causative factors are considered: slope angle, slope aspect, distance from drainage, land use, geology, distance from faults and folds, soil and rock type. Likelihood ratios are obtained for each class of causative factors by comparison with past landslide occurrences. The ratios are normalized between zero and one to obtain fuzzy membership values. Further, different fuzzy operators are applied to generate landslide susceptibility maps. Comparison with the landslide inventory map reveals that the fuzzy gamma operator with a γ-value of 0.60 yields the best prediction accuracy. Consequently, this operator is used to produce the final landslide susceptibility zonation map. [ABSTRACT FROM AUTHOR]

This paper provides a detailed account of the landslide that took place between 09:00 and 10:00 am on April 25, 2009, near the village of Rampac Grande in northern Peru (department of Ancash). Lives were lost and property destroyed during the event. Archive information, remotely sensed data, and detailed field investigations including sampling were applied to describe the events. Historically, landslides have been a common problem in this region with recorded events dating as far back as the 1800s. The landslide is considered as a deep-seated composite rotational-translational earth slide-earth flow. The local community suspected that a mining company had triggered the landslide as it prospected for precious metals. No evidence was found to suggest that anthropogenic activity caused the landslide. Instead, it was likely caused by the considerable amount of cumulative precipitation during the annual rainy season. Several possible sources of future landslide reactivation have been identified. Finally, a poster has been produced for the local community providing basic information about the event and how to manage the potential future landslide hazard. It is hoped that this simple method of knowledge dissemination will provide a fundamental bridge between the efforts of research scientists and the needs of the local community. [ABSTRACT FROM AUTHOR]

This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/ geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network. [ABSTRACT FROM AUTHOR]