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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]

Mountain areas in the southern western corner of the Kingdom of Saudi Arabia frequently suffer from various types of landslides due to rain storms and anthropogenic activities. To resolve the problem related to landslides, landslide susceptibility map is important as a quick and safe mitigation measure and to help making strategic planning by identifying the most vulnerable areas. This paper summarizes findings of landslide susceptibility analysis at Al-Hasher area, Jizan, KSA, using two statistical models: frequency ratio and index of entropy models with the aid of GIS tools and remote sensing data. The landslide locations (inventory map) were identified in the study area using historical records, interpretation of high-resolution satellite images that include Geo-Eye in 2.5 m and Quickbird in 0.6m resolution, topographic maps of 1:10,000 scale, and multiple field investigations. A total of 207 landslides (80% out of 257 detected landslides) were randomly selected for model training, and the remaining 50 landslides (19%) were used for the model validation. Ten landslide conditioning factors including slope angle, slope-aspect, altitude, curvature, lithology, distance to lineaments, normalized difference vegetation index (NDVI), distance to roads, precipitation, and distance to streams, were extracted from spatial database. Using these conditioning factors and landslide locations, landslide susceptibility and weights of each factor were analyzed by using frequency ratio and index of entropy models. Our findings showed that the existing landslides of high and very high susceptibility classes cover nearly 80.4% and 79.1% of the susceptibility maps produced by frequency ratio and index of entropy models respectively. For verification, receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) were calculated for success and prediction rates. For success rate the results revealed that for the index of entropy model (AUC = 77.9%) is slightly lower than frequency ratio model (AUC = 78.8%). For the prediction rate, it was found that the index of entropy model (AUC = 74.9%) is slightly lower than the frequency ratio model (AUC = 76.7%). The landslide susceptibility maps produced from this study could help decision makers, planners, engineers, and urban areas developers to make suitable decisions. [ABSTRACT FROM AUTHOR]

The aim of this study was to apply and to verify the use of artificial neural network (ANN) and weight of evidence (WoE) models to landslide susceptibility mapping in the Gongliu county, China, using a geographic information system (GIS). For this aim, in this study, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field surveys. A total of 163 landslides (70% out of 233 detected landslides) were randomly selected for model training, and the remaining 70 landslides (30%) were used for the model validation. Then, a total number of twelve landslide conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance to rivers, distance to roads, lithology, rainfall, normalized difference vegetation index (NDVI), and sediment transport index (STI), were used in the analysis. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors by ANN and WoE models. Finally the output maps were validated using the area under the curve (AUC) method. The validation results showed that the ANN model with a success rate of 82.51% and predictive accuracy of 77.31% performs better than WoE (success rate, 79.82%; predictive accuracy, 74.59%) model. Overall, both models showed almost similar results. Therefore, the two landslide susceptibility maps obtained were successful and can be useful for preliminary general land use planning and hazard mitigation purpose. [ABSTRACT FROM AUTHOR]

Landslides are one of the most dangerous natural hazards in the world which have significant negative impact on so many lives and properties. Interferometric Synthetic Aperture Radar (InSAR) is an imaging technique for measuring changes in the detailed characteristics of the surface which is timely and cost effective. This research aimed to detect the landslide that occurred in Gunung pass area, Malaysia using InSAR generated from ALOSPALSAR repeat pass data. The signals information was converted into amplitude and phase for both scenes where the phases were used to construct the InSAR. Goldstein filter was used to reduce the phase noise and the results were used as an input for phase unwrapping. Using the unwrapped phase, the vertical displacement was measured and landslide was recognized. Results showed the efficiency of InSAR in detecting the movement of landslide in Gunung pass without the differential having to generate DInSAR. The results were validated using the observed reference point of the landslides and the root mean square error (RMSE) was 0.19. Furthermore, advance 3D processing was performed for measuring the volume of the landslides. The achievements of current research represented that PALSAR data yield excellent performance to generate the interferometric and landslide could be detected very precisely in highly vegetated tropical forest. [ABSTRACT FROM AUTHOR]

The rheology of marine sediment-laden flows in a submarine environment is examined. The viscosity and yield stress of marine sediments are key parameters controlling landslide mobility: the former is related to the peak velocity and the latter is related to the final deposition of debris flow. One of the simplest rheological models is the ideal plastic Bingham model, which is often applied to diverse materials. For marine sediments, the flow behavior can be approximated by modified Bingham models, such as the bi-viscosity and Papanastasiou models. The flow of viscoplastic materials can then be described by unyielded (local shear stress τ is lower than a yield stress τ), yield surface ( τ = τ), and post-yield ( τ > τ) characteristics. It is concluded that modified Bingham models with a proper choice of flow parameters can be used to predict the unyielded, yielding and yielded zones for marine sediments. It is also demonstrated that in some cases the yield surface can be effectively obtained using the relationship between viscosity and shear rate. [ABSTRACT FROM AUTHOR]

The aim of this study is to apply and cross-validate a spatial logistic multiple-regression model at Boeun, Korea using a Geographic Information System (GIS). For this landslide locations in the Boeun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology and land cover were constructed from a spatial database. The 13 factors that influence landslide occurrence were calculated and extracted from the spatial database. Using the 13 factors, landslide susceptibility was analyzed by logistic multiple-regression methods. For validation and cross-validation, the result of the landslide susceptibility analysis obtained from Boeun area was applied to Yongin area in Korea. The validation and cross-validation results showed 75.0% and 85.3% prediction accuracy between the susceptibility map and the existing landslide locations. The GIS was used to analyze the vast amount of data efficiently and statistical programs were used to maintain specificity and accuracy. [ABSTRACT FROM AUTHOR]

The authors have evaluated the effect of spatial resolution on the accuracy of landslide susceptibility mapping. For this purpose, landslide locations were identified from the interpretation of aerial photographs and field surveys in the Boun region of Korea. Topography, soil, forest, geological, lineament and landuse data were collected, processed, and constructed into a spatial database using GIS and remote sensing data. The 15 factors that influenced landslide occurrence were extracted and calculated from the spatial database at 5, 10, 30, 100 and 200 m spatial resolution. Hazardous landslide areas were analyzed and mapped using the landslide-occurrence factors by employing a probability models frequency ratio for the five spatial resolutions. The results of the analysis were verified using the landslide location data and area under success rate curve. The spatial resolutions of 5, 10 and 30 m showed similar results (the normalized area values 0.97, 1.00 and 0.92, respectively), but the 100 and 200 m spatial resolutions showed less well-verified data (the normalized area values 0.48, and 0.00, respectively). Because the scale of the input data was 1∶5,000–1∶50,000, the 5, 10 and 30 m spatial resolutions had a similar accuracy, but the 100 and 200 m spatial resolutions had a lower accuracy. From this, we conclude that spatial resolution has an effect on the accuracy of landslide susceptibility, as it is dependent on the input map. At least, less than 30 m resolution is need for landslide analysis in Korea where most of map scale is in the range 1∶5,000–1∶50,000. [ABSTRACT FROM AUTHOR]

Techniques for geological hazard management, assessment and prediction must be developed for the prevention and mitigation of geological hazards. To enable this, data sets related to geological hazard prevention related must be constructed, analyzed and distributed to customers. For this, a spatial database (SDB) including geological hazards, basic maps, damageable objects, satellite imagery, meteorological data and terrain analysis data was constructed using GIS, and to manage the SDB, a geological hazard information system (GHIS) was developed. The SDB is covering most area of South Korea and was formed at national, regional and medium scales separately in the form of index and administrative district unit. Using the GHIS, the SDB can be selected according to scale, locality and different types of data, and can be edited and manipulated. For the application of the constructed SDB, landslide hazard was analyzed for the Janghung area, Korea. Landslide susceptibility was analyzed using the landslide-occurrence factors by frequency ratio model. For the verification, the result of the analysis was applied to study areas. The verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations. [ABSTRACT FROM AUTHOR]