GIS-based Frequency Ratio and Logistic Regression Modelling for Landslide Susceptibility Mapping of Debre Sina Area in Central Ethiopia

Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.     

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.     

Post-earthquake Rainfall-triggered Slope Stability Analysis in the Lushan Area

The "4.20" Lushan earthquake in Sichuan province, China has induced a large amount of geological hazards and produced abundant loose materials which are prone to post-earthquake rainfalltriggered landslides. A detailed landslide inventory was acquired through post-earthquake emergent field investigation and high resolution remote sensing interpretation. The rainfall analysis was conducted using historical rainfall records during the period from 1951 to 2010. Results indicate that the average annual rainfall distribution is heterogeneous and the largest average annual rainfall occurs in Yucheng district. The Stability Index MAPping(SINMAP)model was adopted to assess and analyze the postearthquake slope stability under different rainfall scenarios(light rainfall, moderate rainfall, heavy rainfall, and rainstorm). The model parameters were calibrated to reflect the significant influence of strong earthquakes on geological settings. The slope stability maps triggered by different rainfall scenarios wereproduced at a regional scale. The effect of different rainfall conditions on the slope stability is discussed.The expanding trend of the unstable area was quantitatively assessed with the different critical rainfall intensity. They provide a new insight into the spatial distribution and characteristics of postearthquake rainfall-triggered landslides in the Lushan seismic area. An increase of rainfall intensity results in a significant increase of unstable area. The heterogeneous distribution of slope instability is strongly correlated with the distribution of earthquake intensity in spite of different rainfall conditions. The results suggest that the both seismic intensity and rainfall are two crucial factors for postearthquake slope stability. This study provides important references for landslide prevention and mitigation in the Lushan area after earthquake.     

Landslide inventory and susceptibility modelling using geospatial tools,in Hunza-Nagar valley,northern Pakistan

A comprehensive landslide inventory and susceptibility maps are prerequisite for developing and implementing landslide mitigation strategies. Landslide susceptibility maps for the landslides prone regions in northern Pakistan are rarely available. The Hunza-Nagar valley in northern Pakistan is known for its frequent and devastating landslides. In this paper, we have developed a landslide inventory map for Hunza-Nagar valley by using the visual interpretation of the SPOT-5 satellite imagery and mapped a total of 172 landslides. The landslide inventory was subsequently divided into modelling and validation data sets. For the development of landslide susceptibility map seven discrete landslide causative factors were correlated with the landslide inventory map using weight of evidence and frequency ratio statistical models. Four different models of conditional independence were used for the selection of landslide causative factors. The produced landslides susceptibility maps were validated by the success rate and area under curves criteria. The prediction power of the models was also validated with the prediction rate curve. The validation results shows that the success rate curves of the weight of evidence and the frequency models are 82% and 79%, respectively. The prediction accuracy results obtained from this study are 84% for weight of evidence model and 80% for the frequency ratio model. Finally, the landslide susceptibility index maps were classified into five different varying susceptibility zones. The validation and prediction result indicates that the weight of evidence and frequency ratio model are reliable to produce an accurate landslide susceptibility map, which may be helpful for landslides management strategies.     

Landslide inventory and susceptibility assessment using multiple statistical approaches along the Karakoram highway,northern Pakistan

China-Pakistan Economic Corridor(CPEC)is a framework of regional connectivity,which will not only benefit China and Pakistan but will have positive impact on Iran,Afghanistan,India,Central Asian Republic,and the region.The surrounding area in CPEC is prone to frequent disruption by geological hazards mainly landslides in northern Pakistan.Comprehensive landslide inventory and susceptibility assessment are rarely available to utilize for landslide mitigation strategies.This study aims to utilize the high-resolution satellite images to develop a comprehensive landslide inventory and subsequently develop landslide susceptibility maps using multiple techniques.The very high-resolution(VHR)satellite images are utilized to develop a landslide inventory using the visual image classification techniques,historic records and field observations.A total of 1632 landslides are mapped in the area.Four statistical models i.e.,frequency ratio,artificial neural network,weights of evidence and logistic regression were used for landslide susceptibility modeling by comparing the landslide inventory with the topographic parameters,geological features,drainage and road network.The developed landslides susceptibility maps were verified using the area under curve(AUC)method.The prediction power of the model was assessed by the prediction rate curve.The success rate curves show 93%,92.8%,92.7%and 87.4%accuracy of susceptibility maps for frequency ratio,artificial neural network,weights of evidence and logistic regression,respectively.The developed landslide inventory and susceptibility maps can be used for land use planning and landslide mitigation strategies.     

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

Rainfall induced landslides are a common threat to the communities living on dangerous hill-slopes 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 (WoE) 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 fuzzy membership 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 WoE, AHP, and combined susceptibility map was calculated 96%, 97%, and 98%, respectively.     

Estimation of rainfall thresholds for shallow landslides in the Sierra Madre Oriental,northeastern Mexico

Landslides induced by prolonged rainfalls are frequent mass movements along the northeastern portion of the Sierra Madre Oriental in Mexico, causing significant damage to infrastructure. In this work, we have studied the connection between rainfall and landslides in the Santa Rosa Canyon, a catchment located in the northeastern Mexico. A landslide database triggered by major storms and hurricanes that have hit the region over the past 30 years was analyzed. A total of 92 rainfall events in the Santa Rosa Canyon were studied to determine the amount of precipitation needed to trigger shallow landslides. For each event the duration(D, in hours) and the cumulated rainfall event(E, in mm) were determined by using historical rainfall data from weather stations located near the study area. We have proposed an ED threshold for rainfall-induced landslides with durations 0.5 D 120 hours to address the conditions that trigger these events in the study area. On analyzing the obtained threshold, it has been established that almost 60 mm of a daily rainfall accumulation is required to trigger shallow landslides in the study area. This estimation is consistent with other calculations made for areas close to the Santa Rosa Canyon. Finally, we validated the predictive capability of the threshold with a different set of rainfall data that did not result in landslides performing a straightforward receiver operating characteristic analysis. A good approach was obtained, especially for rainfall events with daily measurements. Results could be used as input information in the design of a landslide early warning system for the northeastern Mexico, and replicated for other landslide prone areas in the region.     

Rapid susceptibility mapping of co-seismic landslides triggered by the 2013 Lushan Earthquake using the regression model developed for the 2008 Wenchuan Earthquake

The primary objective of landslide susceptibility mapping is the prediction of potential landslides in landslide-prone areas.The predictive power of a landslide susceptibility mapping model could be tested in an adjacent area of similar geoenvironmental conditions to find out the reliability.Both the 2008 Wenchuan Earthquake and the 2013 Lushan Earthquake occurred in the Longmen Mountain seismic zone,with similar topographical and geological conditions.The two earthquakes are both featured by thrust fault and similar seismic mechanism.This paper adopted the susceptibility mapping model of co-seismic landslides triggered by Wenchuan earthquake to predict the spatial distribution of landslides induced by Lushan earthquake.Six influencing parameters were taken into consideration: distance from the seismic fault,slope gradient,lithology,distance from drainage,elevation and Peak Ground Acceleration(PGA).The preliminary results suggested that the zones with high susceptibility of coseismic landslides were mainly distributed in the mountainous areas of Lushan,Baoxing and Tianquan counties.The co-seismic landslide susceptibility map was completed in two days after the quake and sent to the field investigators to provide guidance for rescue and relief work.The predictive power of the susceptibility map was validated by ROC curve analysis method using 2037 co-seismic landslides in the epicenter area.The AUC value of 0.710 indicated that the susceptibility model derived from Wenchuan Earthquake landslides showed good accuracy in predicting the landslides triggered by Lushan earthquake.     

Modeling regional landslide susceptibility using dynamic soil moisture profiles

A landslide susceptibility mapping study was performed using dynamic hillslope hydrology. The modified infinite slope stability model that directly includes vadose zone soil moisture (SM) was applied at Cleveland Corral, California, US and Krishnabhir, Dhading, Nepal. The variable infiltration capacity (VIC-3L) model simulated vadose zone soil moisture and the wetness index hydrologic model simulated groundwater (GW). The GW model predictions had a 75% NASH-Sutcliffe efficiency when compared to California’s in-situ GW measurements. The model performed best during the wet season. Using predicted GW and VIC-3L vadose zone SM, the developed landslide susceptibility maps showed very good agreement with mapped landslides at each study region. Previous quasi-dynamic model predictions of Nepal’s hazardous areas during extreme rainfall events were enhanced to improve the spatial characterization and provide the timing of hazardous conditions.     

Landslide hazard mapping during a large scale earthquake

This paper reports a method to make hazard maps of sediment disasters resulting from an earthquake and following heavy rainfall for the entire region of Gunma prefecture, Japan. Firstly, we identified the slopes in the study area, which are susceptible to large-scale landslides and land failures during an earthquake with a magnitude of seven on the Richter scale. To analyze the sheer volume of the data, we employed a statistical method to evaluate the susceptibility, mainly considering geomorphologic conditions. Secondly, we extracted mudflow and slope failure susceptible areas and potential flooding zones resulting from a damming at a river triggered by the earthquake and heavy rainfall, and we identified the settlements which would be isolated by the road disruption caused by the sediment disasters. As the result, 359 settlements were classified as potential isolation areas. Combining the above-mentioned susceptibility maps, we obtained two types of sediment disaster hazard maps of the study area, depicting the potential hazards which would occur during the earthquake and the disasters which would be caused by heavy rainfall following the quake, respectively. These hazard maps and the disaster information would be useful for the regional disaster prevention planning and countermeasures in the future.     

金沙江结合带巴塘段滑坡群InSAR探测识别与形变特征

金沙江结合带结构破碎,软弱岩层发育,流域性特大高位地质灾害频繁发生。针对该区域开展大范围滑坡调查与监测研究,对减灾防灾具有重要意义。以金沙江结合带巴塘段为试验区,采用堆叠InSAR技术分别利用升轨、降轨Sentinel-1A卫星数据对该区域滑坡隐患开展了调查研究。在此基础上,以中心绒乡滑坡群为重点研究区,利用多维小基线子集技术获取了区域二维形变速率(水平东西向和垂直向)及二维时间序列结果。通过对4处典型滑坡体的形变时间序列结果进行分析,发现在两年时间段内安里克米滑坡、仁娘村滑坡、贡伙村滑坡1和贡伙村滑坡2水平方向累积位移量分别达到44.3、-26.6、65.3和-77.1 mm,垂直向累积位移量分别达到-30.2、-88.3、-80.9和-56.9 mm,且这4处滑坡呈现缓慢蠕滑变形趋势。通过对贡伙村滑坡2的形变监测二维时间序列与降雨数据分析发现,强降雨对滑坡变形有一定短暂影响。由于滑坡群处于地质条件脆弱地区,构造活动强烈,在强降雨条件下极易导致滑坡失稳,建议对其进行持续监测,同时该研究成果对流域内其他区域的滑坡调查与监测研究具有参考意义。     

GIS based spatial data analysis for landslide susceptibility mapping

Landslide susceptibility map delineates the potential zones for landslides occurrence. The paper presents a statistical approach through spatial data analysis in GIS for landslide susceptibility mapping in parts of Sikkim Himalaya. Six important causative factors for landslide occurrences were selected and corresponding thematic data layers were prepared in GIS. Topographic maps,satellite image,field data and published maps constitute the input data for thematic layer preparation. Numerical weights for different categories of these factors were determined based on a statistical approach and the weighted thematic layers were integrated in GIS environment to generate the landslide susceptibility map of the area. The landslide susceptibility map classifies the area into five different landslide susceptible zones i.e.,very high,high,moderate,low and very low. This map was validated using the existing landslide distribution in the area.     

Landslide Susceptibility Mapping along Bhalubang–Shiwapur Area of Mid-Western Nepal Using Frequency Ratio and Conditional Probability Models

Roads constructed in fragile Siwaliks are prone to large number of instabilities. Bhalubang–Shiwapur section of Mahendra Highway lying in Western Nepal is one of them. To understand the landslide causative factor and to predict future occurrence of the landslides, landslide susceptibility mapping(LSM) of this region was carried out using frequency ratio(FR) and weights-of-evidence(W of E) models. These models are easy to apply and give good results. For this, landslide inventory map of the area was prepared based on the aerial photo interpretation, from previously published/unpublished reposts, and detailed field survey using GPS. About 332 landslides were identified and mapped, among which 226(70%) were randomly selected for model training and the remaining 106(30%) were used for validation purpose. A spatial database was constructed from topographic, geological, and land cover maps. The reclassified maps based on the weight values of frequency ratio and weights-of-evidence were applied to get final susceptibility maps. The resultant landslide susceptibility maps were verified andcompared with the training data, as well as with the validation data. From the analysis, it is seen that both the models were equally capable of predicting landslide susceptibility of the region(W of E model(success rate = 83.39%, prediction rate = 79.59%); FR model(success rate = 83.31%, prediction rate = 78.58%)). In addition, it was observed that the distance from highway and lithology, followed by distance from drainage, slope curvature, and slope gradient played major role in the formation of landsides. The landslide susceptibility maps thus produced can serve as basic tools for planners and engineers to carry out further development works in this landslide prone area.     

Mapping landslide susceptibility at the Three Gorges Reservoir,China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.     

Geometrical and Geotechnical Characteristics of Landslides in Korea under Various Geological Conditions

The goal of this study is to determine the geometrical and geotechnical characteristics of landslides under various geological conditions using detailed field surveys, laboratory soil tests and precipitation records. Three study areas are selected to consider different rocks, including gneiss in Jangheung, granite in Sangju and sedimentary rocks in Pohang, South Korea. Many landslides have occurred in these three areas during the rainy season.Precipitation records indicate that landslides occurring in the gneiss area of Jangheung and granite area of Sangju may be influenced by the hourly rainfall intensity rather than cumulative rainfall.However, landslides occurring in the sedimentary rock area of Pohang may be influenced by hourly rainfall intensity and cumulative rainfall. To investigate the factors that influence these types of landslides, a detailed landslide survey was performed and a series of laboratory soil tests were conducted.According to the detailed field survey, most landslides occurred on the flanks of mountain slopes, and the slope inclination where they occurred mostly ranged from 26 to 30 degrees, regardless of the geological conditions. The landslide in the gneiss area of Jangheung is larger than the landslides in the granite area of Sangju and sedimentary rock area of Pohang.Particularly, the landslide in the sedimentary rock area is shorter and shallower than the landslides in the gneiss and granite areas. Thus, the shape and size of the landslide are clearly related to the geological conditions. According to the integrated soil property and landslide occurrence analyses results, the average dry unit weight of the soils from the landslide sites is smaller than that of the soils obtained from the nonlandslide site. The average coefficient of permeability of soils obtained from the landslide sites is greater than that of soils obtained from the non-landslide sites with the same geology. These results indicate that the soils from the landslide sites are more poorly graded or looser than the soils from the non-landslide sites.     

基于修正Green-Ampt模型的降雨诱发区域浅层斜坡失稳灾害分析

由于具有类似的工程地质和水文地质条件, 在高度相关的降雨作用下, 同一个区域中的降雨诱发浅层斜坡失稳灾害常成群出现。在区域尺度预测浅层斜坡失稳灾害对滑坡灾害的防灾减灾工作具有重要的意义。为此, 提出了一种基于力学原理的降雨诱发浅层斜坡失稳灾害预测新模型RARIL。该模型采用修正Green-Ampt模型进行降雨入渗分析, 采用无限体边坡模型进行安全系数计算, 利用可靠度原理考虑区域斜坡稳定性分析中的参数不确定性。该模型具有可考虑降雨诱发浅层斜坡的失稳力学机理、可考虑区域内斜坡土体参数不确定性, 以及计算效率高、易于在GIS平台上实现等优点。案例分析表明, RARIL模型较为准确地预测了2010年8月12日11∶00至2010年8月14日9∶00期间强降雨在四川省汶川县映秀镇附近的303省道K0-K20段沿线区域引发的滑坡灾害, 研究结果证明RARIL模型在预测降雨诱发区域斜坡失稳灾害方面有很好的应用前景。      

Landslide and basin self-organized criticality in the Lushan Hot Spring area

Defining a basin under a critical state(or a self-organized criticality) that has the potential to initiate landslides,debris flows,and subsequent sediment disasters,is a key issue for disaster prevention.The Lushan Hot Spring area in Nantou County,Taiwan,suffered serious sediment disasters after typhoons Sinlaku and Jangmi in 2008,and following Typhoon Morakot in 2009.The basin’s internal slope instability after the typhoons brought rain was examined using the landslide frequency-area distribution.The critical state indices attributed to landslide frequency-area distribution are discussed and the marginally unstable characteristics of the study area indicated.The landslides were interpreted from Spot 5 images before and after disastrous events.The results of the analysis show that the power-law landslide frequency-area curves in the basin for different rainfall-induced events tend to coincide with a single line.The temporal trend of the rainfallinduced landslide frequency-area distribution shows 1/f noise and scale invariance.A trend exists for landslide frequency-area distribution in log-log space for larger landslides controlled by the historical maximum accumulated rainfall brought by typhoons.The unstable state of the basin,including landslides,breached dams,and debris flows,are parts of the basin’s self-organizing processes.The critical state of landslide frequency-area distribution could be estimated by a critical exponent of 1.0.The distribution could be used for future estimation of the potential landslide magnitude for disaster mitigation and to identify the current state of a basin for management.     

Relationships between landslide types and topographic attributes in a loess catchment, China

Topographic attributes have been identified as the most important factor in controlling the initiation and distribution of shallow landslides triggered by rainfall.As a result,these landslides influence the evolution of local surface topography.In this research,an area of 2.6 km 2 loess catchment in the Huachi County was selected as the study area locating in the Chinese Loess Plateau.The landslides inventory and landslide types were mapped using global position system(GPS) and field mapping.The landslide inventory shows that these shallow landslides involve different movement types including slide,creep and fall.Meanwhile,main topographic attributes were generated based on a high resolution digital terrain model(5 m × 5 m),including aspect,slope shape,elevation,slope angle and contributing area.These maps were overlaid with the spatial distributions of total landslides and each type of landslides in a geographic information system(GIS),respectively,to assess their spatial frequency distributions and relative failure potentials related to these selected topographic attributes.The spatial analysis results revealed that there is a close relation between the topographic attributes of the postlandsliding local surface and the types of landslide movement.Meanwhile,the types of landslide movement have some obvious differences in local topographic attributes,which can influence the relative failure potential of different types of landslides.These results have practical significance to mitigate natural hazard and understandgeomorphologic process in thick loess area.     

霍西煤矿区地表滑坡灾害敏感性数值建模与等级划分

本文以山西省霍西煤矿区为研究区,利用遥感和GIS方法对滑坡灾害的敏感性进行了数值建模与定量评价。利用交叉检验方法构建了径向基核函数支持向量机滑坡敏感性评价模型,并基于拟合精度对模型进行了定量评价;对各评价因子在模型中的重要性进行对比分析;基于空间分辨率为30m的评价因子,通过径向基核函数支持向量机模型获得了霍西煤矿区滑坡敏感性指数值,并利用分位数法将霍西煤矿区的滑坡敏感性分为极高、高、中和低4个等级。结果表明：拟合精度建模阶段和验证阶段分别为87.22%和70.12%;与滑坡敏感性关系最密切的5个评价因子依次是岩性、距道路距离、坡向、高程和土地利用类型;极高和高敏感区域分布了93.49%的滑坡点,面积占总面积的50.99%,是比较合理的分级方案。本研究不仅可以为研究区人工边坡调查和煤矿资源合理开采提供借鉴,对相似矿区的相关工作也具有参考价值。     

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.