Numerical investigation of friction-heating-pressurization and its control parameters in the shear band of high-speed landslides

High-speed sliding often leads to catastrophic landslides,many of which,in the initial sliding phase before disintegration,experience a friction-induced thermal pressurization effect in the bottom shear band,accelerating the movement of the overlying sliding mass.To quantitatively investigate this complex multiphysical phenomenon,we established a set of equations that describe the variations in temperature and excess pore pressure within the shear band,as well as the conservation of momentum equation for the overlying sliding mass.With a simplified landslide model,we investigated the variations of temperature and excess pore pressure within the shear band and their impacts on the velocity of the overlying sliding mass.On this basis,we studied the impact of seven key parameters on the maximum temperature and excess pore pressure in the shear band,as well as the impact on the velocity of the overlying sliding mass.The simulation results of the standard model show that the temperature and excess pore pressure in the shear band are significantly higher than those in the adjacent areas,and reach the maximum values in the center.Within a few seconds after the start,the maximum excess pore pressure in the shear zone is close to the initial stress,and the shear strength loss rate exceeds 90%.The thermal pressurization mechanism significantly increases the velocity of the overlying sliding mass.The results of parameter sensitivity analysis show that the thermal expansion coefficient has the most significant impact on the temperature and excess pore pressure in the shear band,and the sliding surface dip angle has the most significant impact on the velocity of the overlying sliding mass.The results of this study are of great significance for clarifying the mechanism of thermal pressurization-induced high-speed sliding.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

High-resolution remote sensing image-based extensive deformation-induced landslide displacement field monitoring method

Landslide is one of the multitudinous serious geological hazards. The key to its control and reduction lies on dynamic monitoring and early warning. The article points out the insufficiency of traditional measuring means applied for large-scale landslide monitoring and proposes the method for extensive landslide displacement field monitoring using high- resolution remote images. Matching of cognominal points is realized by using the invariant features of SIFT algorithm in image translation, rotation, zooming, and affine transformation, and through recognition and comparison of characteristics of high-resolution images in different landsliding periods. Following that, landslide displacement vector field can be made known by measuring the distances and directions between cognominal points. As evidenced by field application of the method for landslide monitoring at West Open Mine in Fushun city of China, the method has the attraction of being able to make areal measurement through satellite observation and capable of obtaining at the same time the information of large- area intensive displacement field, for facilitating automatic delimitation of extent of landslide displacement vector field and sliding mass. This can serve as a basis for making analysis of laws governing occurrence of landslide and adoption of countermeasures.

Empirical-Statistical Models Based on Remote Sensing for Estimating the Volume of Landslides Induced by the Wenchuan Earthquake

The Wenchuan Ms 8.0 earthquake on May 12, 2008 induced a huge number of landslides. The distribution and volume of the landslides are very important for assessing risks and understanding the landslide - debris flow - barrier lake - bursts flood disaster chain. The number and the area of landslides in a wide region can be easily obtained by remote sensing technique, while the volume is relatively difficult to obtain because it requires some detailed geometric information of slope failure surface and sub-surface. Different empirical models for estimating landslide volume were discussed based on the data of 107 landslides in the earthquake-stricken area. The volume data of these landslides were collected by field survey. Their areas were obtained by interpreting remote sensing images while their apparent friction coefficients and height were extracted from the images unifying DEM (digital elevation model). By analyzing the relationships between the volume and the area, apparent friction coefficients, and the height, two models were established, one for the adaptation of a magnitude scale landslide events in a wide range of region, another for the adaptation in a small scope. The correlation coefficients (R2) are 0.7977 and 0.8913, respectively. The results estimated by the two models agree well with the measurement data.

Investigation and Assessment of Landslides and Debris Flows in Sichuan Province of China by Remote Sensing Technique

Taking TM images, ETM images, SPOT images, aerial photos and other remote sensing data as fundamental sources, this research makes a thorough investigation on landslides and debris flows in Sichuan Province, China, using the method of manual interpretation and taking topography maps as references after the processes of terrain correction, spectral matching, and image mosaic. And then, the spatial characteristics of landslides and debris flows in the year of 2005 are assessed and made into figures. The environmental factors which induce landslides and debris flows such as slope, vegetation coverage, lithology, rainfall and so on are obtained by GIS spatial analysis method. Finally, the rela- tionships of landslides or debris flows with some environmental factors are analyzed based on the grade of each envi- ronmental factor. The results indicate: 1) The landslides and debris flows are mainly in the eastern and southern area of Sichuan Province, however, there are few landslides and debris flows in the western particularly the northwestern Si- chuan. 2) The landslides and debris flows of Sichuan Province are mostly located in the regions with small slope degree. The occurring rate of debris flow reduces with the increase of the vegetation coverage degree, but the vegetation cov- erage degree has little to do with the occurrence of landslide. The more rainfall a place has, the easier the landslides and debris flows take place.

Remote Sense and GIS-Based Division of Landslide Hazard Degree in Wanzhou District of the Three Gorges Reservoir Area

An evaluation model divided landslide hazard degrees in Wanzhou District of Three Gorges Reservoir Area. The model was established by GIS techniques and took land use/cover, stratum characters, slope aspect, slope gradient, elevation difference and slope shape as evaluation factors. The data of land use/cover were obtained by remote sensing, and the weights of the factors mentioned above were established by the analytic hierarchy process （AHP）. The results indicate, low danger areas in the studied area account for 66.51%, and high danger areas and very high danger areas occupy 1/3 of the total area. The regions of high and very high danger are mainly located around the urban area of Wanzhou District and on the banks of the Yangtze River with a relatively large area, where collapse and landslide directly threats densely populated areas and Three Gorges Reservoir. Slope destabilization, if occurs, will bring huge loss to social economy. All research results are consistent with the actual conditions; therefore, they can be regarded as a useful basis for planning and constructing of the reservoir area.

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.

Characterization and spatial analysis of coseismic landslides triggered by the Luding Ms 6.8 earthquake in the Xianshuihe fault zone, Southwest China

On September 5, 2022, a magnitude Ms 6.8 earthquake occurred along the Moxi fault in the southern part of the Xianshuihe fault zone located in the southeastern margin of the Tibetan Plateau,resulting in severe damage and substantial economic loss. In this study, we established a coseismic landslide database triggered by Luding Ms 6.8 earthquake, which includes 4794 landslides with a total area of 46.79 km^(2). The coseismic landslides primarily consisted of medium and small-sized landslides, characterized by shallow surface sliding. Some exhibited characteristics of high-position initiation resulted in the obstruction or partial obstruction of rivers, leading to the formation of dammed lakes. Our research found that the coseismic landslides were predominantly observed on slopes ranging from 30° to 50°, occurring at between 1000 m and 2500 m, with slope aspects varying from 90° to 180°. Landslides were also highly developed in granitic bodies that had experienced structural fracturing and strong-tomoderate weathering. Coseismic landslides concentrated within a 6 km range on both sides of the Xianshuihe and Daduhe fault zones. The area and number of coseismic landslides exhibited a negative correlation with the distance to fault lines, road networks, and river systems, as they were influenced by fault activity, road excavation, and river erosion. The coseismic landslides were mainly distributed in the southeastern region of the epicenter, exhibiting relatively concentrated patterns within the IX-degree zones such as Moxi Town, Wandong River basin, Detuo Town to Wanggangping Township. Our research findings provide important data on the coseismic landslides triggered by the Luding Ms 6.8 earthquake and reveal the spatial distribution patterns of these landslides. These findings can serve as important references for risk mitigation, reconstruction planning, and regional earthquake disaster research in the earthquake-affected area.

Automatic recognition of landslides based on YOLOv7 and attention mechanism

Landslide disasters comprise the majority of geological incidents on slopes,posing severe threats to the safety of human lives and property while exerting a significant impact on the geological environment.The rapid identification of landslides is important for disaster prevention and control;however,currently,landslide identification relies mainly on the manual interpretation of remote sensing images.Manual interpretation and feature recognition methods are time-consuming,labor-intensive,and challenging when confronted with complex scenarios.Consequently,automatic landslide recognition has emerged as a pivotal avenue for future development.In this study,a dataset comprising 2000 landslide images was constructed using open-source remote sensing images and datasets.The YOLOv7 model was enhanced using data augmentation algorithms and attention mechanisms.Three optimization models were formulated to realize automatic landslide recognition.The findings demonstrate the commendable performance of the optimized model in automatic landslide recognition,achieving a peak accuracy of 95.92%.Subsequently,the optimized model was applied to regional landslide identification,co-seismic landslide identification,and landslide recognition at various scales,all of which showed robust recognition capabilities.Nevertheless,the model exhibits limitations in detecting small targets,indicating areas for refining the deep-learning algorithms.The results of this research offer valuable technical support for the swift identification,prevention,and mitigation of landslide disasters.

Spatial structural characteristics of the Deda ancient landslide in the eastern Tibetan Plateau:Insights from Audio-frequency Magnetotellurics and the Microtremor Survey Method

It is of crucial importance to investigate the spatial structures of ancient landslides in the eastern Tibetan Plateau’s alpine canyons as they could provide valuable insights into the evolutionary history of the landslides and indicate the potential for future reactivation.This study examines the Deda ancient landslide,situated in the Chalong-ranbu fault zone,where creep deformation suggests a complex underground structure.By integrating remote sensing,field surveys,Audio-frequency Magnetotellurics(AMT),and Microtremor Survey Method(MSM)techniques,along with engineering geological drilling for validation,to uncover the landslide’s spatial feature s.The research indicates that a fault is developed in the upper part of the Deda ancient landslide,and the gully divides it into Deda landslide accumulation zoneⅠand Deda landslide accumulation zoneⅡin space.The distinctive geological characteristics detectable by MSM in the shallow subsurface and by AMT in deeper layers.The findings include the identification of two sliding zones in the Deda I landslide,the shallow sliding zone(DD-I-S1)depth is approximately 20 m,and the deep sliding zone(DD-I-S2)depth is 36.2-49.9 m.The sliding zone(DD-Ⅱ-S1)depth of the DedaⅡlandslide is 37.6-43.1 m.A novel MSM-based method for sliding zone identification is proposed,achieving less than 5%discrepancy in depth determination when compared with drilling data.These results provide a valuable reference for the spatial structural analysis of large-deepseated landslides in geologically complex regions like the eastern Tibetan Plateau.

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.

Evaluating effectiveness of frequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag district, India

Rudraprayag in Garhwal Himalayan division is one of the most vulnerable districts to landslides in India. Heavy rainfall, steep slope and developmental activities are important factors for the occurrence of landslides in the district. Therefore, specific assessment of landslide susceptibility and its accuracy at regional level is essential for disaster management and proper land use planning. The article evaluates effectiveness of frequency ratio, fuzzy logic and logistic regression models for assessing landslide susceptibility in Rudraprayag district of Uttarakhand state, India. A landslide inventory map was prepared and verified by field data. Fourteen landslide parameters and generated inventory map were utilized to prepare landslide susceptibility maps through frequency ratio, fuzzy logic and logistic regression models. Landslide susceptibility maps generated through these models were classified into very high, high, medium, low and very low categories using natural breaks classification. Receiver operating characteristics(ROC) curve, spatially agreed area approach and seed cell area index(SCAI) method were used to validate the landslide models. Validation results revealed that fuzzy logic model was found to be more effective in assessing landslide susceptibility in the study area. The landslide susceptibility map generated through fuzzy logic model can be best utilized for landslide disaster management and effective land use planning.

Hazard assessment of landslide disaster using information value method and analytical hierarchy process in highly tectonic Chamba region in bosom of Himalaya

The present study is focused on a comparative evaluation of landslide disaster using analytical hierarchy process and information value method for hazard assessment in highly tectonic Chamba region in bosom of Himalaya. During study, the information about the causative factors was generated and the landslide hazard zonation maps were delineated using Information Value Method(IV) and Analytical Hierarchy Process(AHP) using Arc GIS(ESRI). For this purpose, the study area was selected in a part of Ravi river catchment along one of the landslide prone Chamba to Bharmour road corridor of National Highway(NH^(-1)54 A) in Himachal Pradesh, India. A numeral landslide triggering geoenvironmental factors i.e. slope, aspect, relative relief, soil, curvature, land use and land cover(LULC), lithology, drainage density, and lineament density were selected for landslide hazard mapping based on landslide inventory. Landslide hazard zonation map was categorized namely "very high hazard, high hazard, medium hazard, low hazard, and very low hazard". The results from these two methods were validated using Area Under Curve(AUC) plots. It is found that hazard zonation map prepared using information value method and analytical hierarchy process methods possess the prediction rate of 78.87% and 75.42%, respectively. Hence, landslide hazardzonation map obtained using information value method is proposed to be more useful for the study area. These final hazard zonation maps can be used by various stakeholders like engineers and administrators for proper maintenance and smooth traffic flow between Chamba and Bharmour cities, which is the only route connecting these tourist places.

Stability and remote real-time monitoring of the slope slide body in the Luoshan mining area

Landslides have occurred frequently in the Luoshan mining area because of disordered mining.This paper discusses the landforms and physiognomy,hydro-meteorology,formation lithology,and geologic structure of the Luoshan mining area.It also describes the factors influencing the slope stability of landslide No.Ⅲ,determines the general parameters and typical section plane,analyzes the stress-strain state of the No.Ⅲ slope,and calculates its safety factors with FLAC3 D under saturated and natural conditions.Based on a stability analysis,a remote real-time monitoring system was applied to the No.Ⅲ slope,and these monitoring data were collected and analyzed.

Coseismic landslide sediment increased by the“9.5”Luding earthquake,Sichuan,China

The"9.5"Luding earthquake(Ms 6.8),which occurred on September 5,2022,has triggered thousands of landslides,and caused coseismic landslide sediment in the mountain basin to increase significantly.After the Luding earthquake,landslide sediment may continue to divert to channels,and increase the activity of debris flows.Importantly,the formation of debris flows can pose a major threat to infrastructure,lives and property.To better understand the landslide sediment that increased by the"9.5"Luding earthquake and its impact on the activity of debris flows,we mapped the coseismic landslide database using satellite images.A total of9142 landslides with an area of 49.51 km^(2),covering4.81%of the whole basin,were triggered by the Luding earthquake.The coseismic landslides induced by this earthquake are dominated by shallow landslides and are densely distributed in the combined zone of the Xianshuihe fault and the Daduhe fault.Approximately 333.31×10^(6)m^(3)(error:111.43×10^(6)m^(3)/-70.73×10^(6)m^(3))of coseismic landslide sediments were induced by the earthquake in the epicenter,and the landslide materials were concentrated downstream of the basins.In addition.more than 13986.45×10^(4)m^(3)(error:4675.67×10^(4)m^(3)/-2967.92×10^(4)m^(3))of landslide sediment may supply for debris flow occurrence.Simultaneously,the small basins that are distributed near Moxi,Detuo and the junction of the Xianshuihe fault and Daduhe fault are more susceptible to debris flows when rainstorms hit these regions.Therefore,prevention and mitigation measures,early warning,and land use planning should be adopted in advance in these regions.However,from the perspectives of landslide scale and the degree of landslide-channel coupling,the activity or active intensity of debris flows in the Luding earthquake area may be lower than that in the epicenter area of the 2008 Wenchuan earthquake.

Formation and reactivation mechanisms of large-scale ancient landslides in the Longwu River basin in the northeast Tibetan Plateau, China

The northeastern Tibetan Plateau exhibits steep topography and strong internal or external dynamic geological effect and is frequently subjected to strong earthquakes and heavy rainfall. The geological evolution has resulted in a wide distribution of ancient landslides, which has become a hotspot for studying ancient landslide formation and reactivation. In recent decades, several ancient landslides on both banks of the Longwu River, Qinghai Province, China were reactivated, causing serious economic losses and casualties. This study conducted remote sensing interpretation and ground surveys on these ancient landslides. Totally 59 ancient landslides were identified, and the formation mechanism, evolution process, and resurrection mechanism of the Longwu Xishan No.2 ancient landslide were analyzed by means of a detailed field geological survey, drilling, and series of experimental tests such as the particle size distribution test, the Xray diffraction test and the mechanical properties test. The results show that the formation of these ancient landslides is closely associated with the uplift of the Tibetan Plateau and the erosion of the Longwu River. Firstly, the intermittent uplift of the Tibetan Plateau lead to the diversion and downcutting of the Longwu River basin, which forms the alternate slope topography with steep and slow slopes, thereby providing favourable topography and slope structure conditions for the formation of landslides. Secondly, 34.5% clay-mineral content in the Neoproterozoic mudstone with 32.7% particle size less than 0.005 mm, and the corrosion and softening effects of the Neogene mudstone with high clay mineral content under the erosion of water provides favourable material conditions for the formation of landslides. Thirdly, rainfall and human activities are the primary triggering factors for the revival of this ancient landslide group. It is revealed that the evolution process of the ancient landslides on both banks of the Longwu River can be divided into five stages namely tectonic rapid uplift slope formation, river erosion creep-sliding deformation, slope instability critical status, landslide failure-movement-accumulation, and slope reactivation under rainfall erosion and engineering excavation.

GIS Based Landslide Susceptibility Mapping with Application of Analytical Hierarchy Process in District Ghizer, Gilgit Baltistan Pakistan

District Ghizer is a rugged mountainous territory which experiences several landslides each year. There are 16 major landslide areas and 53 villages that are at high risk to hazards. Keeping in view the severity of natural hazards, the present study was designed to generate landslide susceptibility map based on twelve causative factors viz., slope, aspect, elevation, drainage network, Stream Power Index (SPI), Topographic Wetness Index (TWI), lithological units, fault lines, rainfall, road network, land cover and soil texture. Soil texture was determined by particle size analysis and data for other factors were acquired from freely available sources. Analytical Hierarchy Process (AHP) was employed to identify major landslide causative factors in the district Ghizer. Further, a temporal assessment from 1999 till 2015 was generated to assess the impact of land cover change on landslides. It indicated that the barren soil/ exposed rocks and glaciers have reduced while the vegetation and water classes have shown increment. The total area that lies in moderate to very high landslide susceptible zones was 74.38%, while slope is the main landslide causative factor in the district Ghizer. Validation of the susceptibility map showed 88.1% of the landslides in the study area had occurred in the moderate to very high susceptible zones.

Numerical simulations of kinetic formation mechanism of Tangjiashan landslide

Tangjiashan landslide is a typical high-speed landslide hosted on consequent bedding rock. The landslide was induced by Wenchuan earthquake at a medium-steep hill slope. The occurrence of Tangjiashan landslide was basically controlled by the tectonic structure, topography, stratum lithology, slope structure, seismic waves, and strike of river. Among various factors, the seismic loading with great intensity and long duration was dominant. The landslide initiation exhibited the local amplification effect of seismic waves at the rear of the slope, the dislocation effect on the fault, and the shear failure differentiating effect on the regions between the soft and the hard layers. Based on field investigations and with the employment of the distinct element numerical simulation program UDEC （universal distinct element code）, the whole kinetic sliding process of Tan iashan landslide was represented and the formation mechanism of the consequent rock landslide under seismic loading was studied. The results are helpful for understanding seismic dynamic responses of consequent bedding rock slopes, where the slope stability could be governed by earthquakes.

Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multicriteria

Rainfall induced landslides are a common threat to the communities living on dangerous hillslopes in Chittagong Metropolitan Area, Bangladesh. Extreme population pressure, indiscriminate hill cutting, increased precipitation events due to global warming and associated unplanned urbanization in the hills are exaggerating landslide events. The aim of this article is to prepare a scientifically accurate landslide susceptibility map by combining landslide initiation and runout maps. Land cover, slope, soil permeability, surface geology, precipitation, aspect, and distance to hill cut, road cut, drainage and stream network factor maps were selected by conditional independence test. The locations of 56 landslides were collected by field surveying. A weight of evidence(Wo E) method was applied to calculate the positive(presence of landslides) and negative(absence of landslides) factor weights. A combination of analytical hierarchical process(AHP) and fuzzymembership standardization(weighs from 0 to 1) was applied for performing a spatial multi-criteria evaluation. Expert opinion guided the decision rule for AHP. The Flow-R tool that allows modeling landslide runout from the initiation sources was applied. The flow direction was calculated using the modified Holmgren's algorithm. The AHP landslide initiation and runout susceptibility maps were used to prepare a combined landslide susceptibility map. The relative operating characteristic curve was used for model validation purpose. The accuracy of Wo E, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.

Geospatial susceptibility mapping of earthquake-induced landslides in Nuweiba area, Gulf of Aqaba, Egypt

Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.