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Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations. [ABSTRACT FROM AUTHOR]

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]

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]

On February 05 2016, a significant and unfortunate series of mine disasters occurred at the Lily gold mine in South Africa, where 79 mineworkers were trapped under a huge rock and soil mass. In this paper, two separate geographical information system based models are proposed; one for surface and one for subsurface mine disasters. The multi-temporal (2004–2017) high-resolution pre and post-disaster satellite images were analyzed to assess the magnitude and spatial extent of the surface disaster. The explicit modeling technique was applied for mine subsidence using the Mohr–Coulomb failure criterion under gravitational acceleration. For surface disaster, the weighted overlay model was applied by assigning weights to hydrological (rainfall, flow direction, accumulation, and density); geological (geology and lineaments) and geomorphological (slope, aspect, and curvature) causative factors. The mine subsidence modeling results showed a subsidence zone of 55 × 24 × 71 m due to a failure of a crown pillar, whereas, in reality, it was 60 × 30 × 80 m. The deformation results showed that the lamp room wherein the miners were trapped could have been displaced somewhere at or below level 5 but above level 6 towards the southwest direction. The output of surface disaster modeling was also satisfactory and reliable as the actual two slope failures, i.e. the western and southern landslides of the Lily gold mine, located in extreme risk zones as predicted by the model. The results of this study can be useful for future mine planning and the environmental improvements at the Lily gold mine. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is a vital tool for disaster management and planning development activities in mountainous terrains of tropical and subtropical environments. In this paper, the weights-of-evidence modelling was applied, within a geographical information system (GIS), to derive landslide susceptibility map of two small catchments of Shikoku, Japan. The objective of this paper is to evaluate the importance of weights-of-evidence modelling in the generation of landslide susceptibility maps in relatively small catchments having an area less than 4 sq km. For the study area in Moriyuki and Monnyu catchments, northeast Shikoku Island in west Japan, a data set was generated at scale 1:5,000. Relevant thematic maps representing various factors (e.g. slope, aspect, relief, flow accumulation, soil depth, soil type, land use and distance to road) that are related to landslide activity were generated using field data and GIS techniques. Both catchments have homogeneous geology and only consist of Cretaceous granitic rock. Thus, bedrock geology was not considered in data layering during GIS analysis. Success rates were also estimated to evaluate the accuracy of landslide susceptibility maps and the weights-of-evidence modelling was found useful in landslide susceptibility mapping of small catchments. [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 detailed landslide susceptibility map was produced in the Youfang catchment using logistic regression method with datasets developed for a geographic information system (GIS). Known as one of the most landslide-prone areas in China, the Youfang catchment of Longnan mountain region, which lies in the transitional area among Qinghai-Tibet Plateau, loess Plateau and Sichuan Basin, was selected as a representative case to evaluate the frequency and distribution of landslides. Statistical relationships for landslide susceptibility assessment were developed using landslide and landslide causative factor databases. Logistic regression (LR) was used to create the landslide susceptibility maps based on a series of available data sources: landslide inventory; distance to drainage systems, faults and roads; slope angle and aspect; topographic elevation and topographical wetness index, and land use. The quality of the landslide susceptibility map produced in this paper was validated and the result can be used for designing protective and mitigation measures against landslide hazards. The landslide susceptibility map is expected to provide a fundamental tool for landslide hazards assessment and risk management in the Youfang catchment. [ABSTRACT FROM AUTHOR]

Landslides cause heavy damage to property and infrastructure, in addition to being responsible for the loss of human lives in many parts of the Turkey. The paper presents GIS-based spatial data analysis for landslide susceptibility mapping in the regions of the Sultan Mountains, West of Akşehir, and central part of Turkey. Landslides occur frequently in the area and seriously affect local living conditions. Therefore, spatial analysis of landslide susceptibility in the Sultan Mountains is important. The relationships between landslide distributions with the 19 landslide affecting parameters were analysed using a Bayesian model. In the study area, 90 landslides were observed. The landslides were randomly subdivided into 80 training landslides and 10 test landslides. A landslide susceptibility map was produced by using the training landslides. The test landslides were used in the accuracy control of the produced landslide susceptibility map. Approximately 9% of the study area was classified as high susceptibility zone. Medium, low and very low susceptibility zones covered 8, 23 and 60% of the study area, respectively. Most of the locations of the observed landslides actually fall into moderate (17.78%) and high (77.78. %) susceptibility zones of the produced landslide susceptibility map. This validates the applicability of proposed methods, approaches and the classification scheme. The high susceptibility zone is along both sides of the Akşehir Fault and at the north-eastern slope of the Sultan Mountains. It was determined that the surface area of the Harlak and Deresenek formations, which have attained lithological characteristics of clayey limestone with a broken and separated base, and where area landslides occur, possesses an elevation of 1,100-1,600 m, a slope gradient of 25°-35° and a slope aspect of 22.5°-157.5° facing slopes. [ABSTRACT FROM AUTHOR]

Landslides and their assessments are of great importance since they damage properties, infrastructures, environment, lives and so on. Particularly, landslide inventory, susceptibility, and hazard or risk mapping have become important issues in the last few decades. Such maps provide useful information and can be produced by qualitative or quantitative methods. The work presented in this paper aimed to assess landslide susceptibility in a selected area, covering 570.625 km in the Western Black Sea region of Turkey, by two quantitative methods. For this purpose, in the first stage, a detailed landslide inventory map was prepared by extensive field studies. A total of 96 landslides were mapped during these studies. To perform landslide susceptibility analyses, six input parameters such as topographical elevation, lithology, land use, slope, aspect and distance to streams were considered. Two quantitative methods, logistic regression and fuzzy approach, were used to assess landslide susceptibility in the selected area. For the fuzzy approach, the fuzzy and, or, algebraic product, algebraic sum and gamma operators were considered. At the final stage, 18 landslide susceptibility maps were produced by the logistic regression and fuzzy operators in a GIS (Geographic Information System) environment. Two performance indicators such as ROC (relative operating characteristics) and cosine amplitude method ( r) were used to validate the final susceptibility maps. Based on the analyses, the landslide susceptibility map produced by the fuzzy gamma operator with a level of 0.975 showed the best performance. In addition, the maps produced by the logistic regression, fuzzy algebraic product and the higher levels of gamma operators showed more satisfactory results, while the fuzzy and, or, algebraic sum maps were not sufficient to provide reliable outputs. [ABSTRACT FROM AUTHOR]

This paper summarizes findings of landslide hazard analysis on Penang Island, Malaysia, using frequency ratio, logistic regression, and artificial neural network models with the aid of GIS tools and remote sensing data. Landslide locations were identified and an inventory map was constructed by trained geomorphologists using photo-interpretation from archived aerial photographs supported by field surveys. A SPOT 5 satellite pan sharpened image acquired in January 2005 was used for land-cover classification supported by a topographic map. The above digitally processed images were subsequently combined in a GIS with ancillary data, for example topographical (slope, aspect, curvature, drainage), geological (litho types and lineaments), soil types, and normalized difference vegetation index (NDVI) data, and used to construct a spatial database using GIS and image processing. Three landslide hazard maps were constructed on the basis of landslide inventories and thematic layers, using frequency ratio, logistic regression, and artificial neural network models. Further, each thematic layer’s weight was determined by the back-propagation training method and landslide hazard indices were calculated using the trained back-propagation weights. The results of the analysis were verified and compared using the landslide location data and the accuracy observed was 86.41, 89.59, and 83.55% for frequency ratio, logistic regression, and artificial neural network models, respectively. On the basis of the higher percentages of landslide bodies predicted in very highly hazardous and highly hazardous zones, the results obtained by use of the logistic regression model were slightly more accurate than those from the other models used for landslide hazard analysis. The results from the neural network model suggest the effect of topographic slope is the highest and most important factor with weightage value (1.0), which is more than twice that of the other factors, followed by the NDVI (0.52), and then precipitation (0.42). Further, the results revealed that distance from lineament has the lowest weightage, with a value of 0. This shows that in the study area, fault lines and structural features do not contribute much to landslide triggering. [ABSTRACT FROM AUTHOR]

Take account of the characters of road geological hazard and its supervision, it is very important to develop the Road Landslides Information Management and Forecasting System based on Geographic Information System (GIS). The paper presents the system objective, function, component modules and key techniques in the procedure of system development. The system, based on the spatial information and attribute information of road geological hazard, was developed and applied in Guizhou, a province of China where there are numerous and typical landslides. The manager of communication, using the system, can visually inquire all road landslides information based on regional road network or on the monitoring network of individual landslide. Furthermore, the system, integrated with mathematical prediction models and the GIS’s strongpoint on spatial analyzing, can assess and predict landslide developing procedure according to the field monitoring data. Thus, it can efficiently assists the road construction or management units in making decision to control the landslides and to reduce human vulnerability. [ABSTRACT FROM AUTHOR]

This effort is made to develop a landslide hazard map and perform the kinematic slope instability analysis of the critical sections at the Dasu dam site and its reservoir area. The study area is located at the Indus River in the Kohistan District within the highly elevated Himalayan terrain. The fragile geologic setup and high tectonic activity make this region favorable to produce frequent slope failures and landsliding events. First, a regional level landslide inventory map was created using GIS and remote-sensing tools. For this a hill shaded topographic map was created by overlapping a topographic map on to a hillshade image extracted from satellite based PALSAR Digital Elevation Model (DEM) with 12.5 m spatial resolution in GIS environment. Numerous past landsides were delineated using the expert driven knowledge of anomalous topographic expression like, crenulated contours, divergent contours, isolated topographic knobs, extended topographic ridges and arcuate headscarf evacuation areas. Total 120 landslides were marked, later a field visit was carried out for the validation of this exercise. The overall efficiency of this work was 85% which indicates that the GIS and remote sensing tools utilized for this study are reliable and effective in producing regional landslide inventory maps. Moreover, the scanline surveys were conducted at few selected locations to generate discontinuities data for the evaluation of slope instability issues by using kinematic analysis in DIPS 7.0 software. The study successfully highlights the usefulness of GIS and RS tools and in combination with kinematic analysis for landslides hazard assessment for mega projects. The outcomes of this research could serve as a guide in risk assessment and mitigation work in future to stabilize the critical slopes near Dasu dam site and along its reservoir area. [ABSTRACT FROM AUTHOR]

Landslide susceptibility maps are modelled either by knowledge-driven methods (qualitative) or data-driven methods (quantitative) depending on apriori expert knowledge or complete landslide inventory data, respectively, which hinders land regulatory authorities from taking quick decisions for land use planning at a regional scale. To overcome these limitations, a new modelling methodology is proposed which can perform equally with the existing established models and needs minimum knowledge of geo-environmental variables without any model testing data. In this method, common variances of the geo-environmental variables are extracted from initially equally weighted variables and are termed as equal weight method (EWM). The extracted common variances are used to weight the variables for the preparation of a landslide susceptibility map on a regional scale. The output of the model can be interpreted to understand the inter-relationship of different geo-environmental variables along with the identification and prioritization of variables on model performance by delineating the non-performing variables. The proposed modelling methodology is tested at the eastern Himalayan road corridor, the result of which is compared with the results of the analytical hierarchy process (qualitative) and frequency ratio (quantitative) modelling methods by area under curve (AUC) of receiver operating characteristic (ROC) curves and cost analysis. AUC of ROC curves for the analytical hierarchy process, frequency ratio method and equal weight method models are 0.755, 0.809 and 0.774, respectively, indicating little performance differences. Cost analysis of all models shows a maximum bounding cost of 0.4 indicating that all models are cost-effective. The study concludes that the equal weight method is quick, robust and cost-effective at par with other well tested knowledge-driven and data-driven methods. [ABSTRACT FROM AUTHOR]

In the context of circular high steep slopes within a 500-m aperture spherical radio telescope (FAST) site in Pingtang, Guizhou Province in southwest China, a pertinent small-area karst landslide risk assessment is carried out based on geographic information system and fuzzy analytical hierarchy process. Results show the following: (a) The AUC value of hazard assessment is 0.826, which means a good adaptability and a relatively higher accuracy, and the AUC value of risk assessment is 0.803, which means the accuracy of landslide risk assessment is within acceptable level. (b) The slopes of FAST area are generally at the medium or low-hazard level, with high-hazard region accounting for only 3.17% of the total area. High-hazard regions are primarily found in two locations, one is located in nearby the Guangmingding slope where the dangerous rock masses were basically cleared away, the other is located in nearby feed tower 5H where potentially unstable rock masses were reinforced to improve the stability of slope. (c) High-vulnerability area of FAST accounts for approximately 43.62%. As a direct result, the high-risk area of FAST site accounts for 34.36% based on the overlap of vulnerability and hazard. The high risk is concentrated in the surrounding areas such as the feed tower, support pillar, and telescope mirror, and the high-risk area overlaps most of the fault fracture zone in the study area. Our findings provide meaningful references for landslide prevention and monitoring of FAST areas and are instructive in theory and practice. [ABSTRACT FROM AUTHOR]

Rwanda as a landlocked country has been recurrently facing tremendous and devastating landslides having serious impacts on the environment and socioeconomic development. Analyzing landslide susceptibility and its influencing factors could ease landslide disaster control and, hence, contributes to environmental sustainability. Unfortunately, detailed information on influencing factors per respective classes and their level of correlation to the probability of landslide occurrence have not been fully analyzed in Rwanda. The purpose of this study is to reveal the spatial correlation between landslide occurrence and different classes of influencing factors using the frequency ratio (FR) approach with geographical information system (GIS) and remote sensing techniques. Initially, a landslide inventory map was prepared using 423 landslide locations that were randomly split into 75% of training datasets (318 points) and 25% (105 points) to validate the model. A multicollinearity analysis was performed among ten influencing factors using the tolerance and variance inflation factor method. These factors include elevation, slope, aspect, distance to roads, distance to rivers, the normalized difference vegetation index, land use land cover, stream power index, rainfall and soil texture. The analysis revealed no multicollinearity among these factors, and therefore, all of them were suitable for modeling process. The FR model evaluated the relationship between landslide incidence and influencing factors in their respective classes and then generated the landslide susceptibility map using GIS which revealed the western, northern and some parts of southern province as the most susceptible areas owing to the slope, LCLU, rainfall and elevation as the main influencing factors. The generated susceptibility map was validated using the area under curve which portrayed 81.2% and 84.6% for success and prediction rate, respectively. To conclude, the results of this study are essential for future initiatives regarding landslide risk reduction toward sustainability in Rwanda. However, these can be broad and may reflect the most landslide influencing factors projected in other central-east African regions. [ABSTRACT FROM AUTHOR]

Even small number of built-up areas experienced landslide problems; however, the loss could rise when the built-up areas develop in and on the unstable slopes. Landslide hazard information serves one of the many components in an integrated development planning study. A study on spatial vulnerability assessment is carried out for the part of Nilgiris district in the Western Ghats of India with the help of remote sensing and geographical information system to make use of landslide planning scenario. A spatial database is created for past landslide histories in the study area. The critical facilities, transportation and lifeline utilities, which include tourist spots, banks, ATMs, educational institutions, hospitals, bus stops, railway stations, that are potential threat under the very high to severe landslide hazard areas were identified. These are the elements at risk during landslides. From the study, it has been found that the eastern part of the study area is more prone to landslides. The past landslide inventories very clearly reveal that most of the landslides are triggered by the intense rainfall, human and cultural activities. Out of 36 utilities in the study area, about 65 % critical facilities and 36 % of transportation and lifeline utilities are vulnerable to severe landslide threat. The output of the present study can be used as first-hand information for the decision makers and planners and also helpful for the further developmental activities in the landslide-prone areas of the Nilgiris district of India. [ABSTRACT FROM AUTHOR]

Bedding planes are a known factor that controls the type, abundance and pattern of landslides. Where layered rocks crop out, the geometrical relationships between the attitude of the bedding and the geometry of the terrain is crucial to understand landslide phenomena. Obtaining information on bedding attitude for large areas through field surveys is time-consuming, and resource intensive, hampering the possibility of quantitative investigations on the control of bedding planes on landslides. We propose a GIS-based method to extract information on bedding planes from the analysis of information captured through the visual interpretation of stereoscopic aerial photographs and a digital representation of the terrain. We tested the method in the Collazone study area, Umbria, Central Italy, where we used spatially distributed information on beddings and terrain information obtained from a 10 × 10-m DEM to determine morpho-structural domains. We exploited the morpho-structural terrain zonation, in combination with landslide information for the same area, to investigate the role of beddings in controlling the distribution and abundance of landslides in the study area. We found that beddings condition the location and abundance of relict and deep-seated landslides, most abundant in cataclinal slopes, and do not condition significantly the shallow landslides. We expect the method to facilitate the production of maps of morpho-structural domains in layered geological environments. This will contribute to a better understanding of landslide phenomena and to foster the preparation of advanced landslide susceptibility and hazard models. [ABSTRACT FROM AUTHOR]

Identification of landslides and production of landslide susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning. Accuracy of the landslide susceptibility maps is important for reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is essential for land-use activities and management decision making in hilly or mountainous regions. The existing approaches to landslide susceptibility zoning and mapping require many different types of data. In this study, we propose a fractal method to map landslide susceptibility using historical landslide inventories only. The spatial distribution of landslides is generally not uniform, but instead clustered at many different scales. In the method, we measure the degree of spatial clustering of existing landslides in a region using a box-counting method and apply the derived fractal clustering relation to produce a landslide susceptibility map by means of GIS-supported spatial analysis. The method is illustrated by two examples at different regional scales using the landslides inventory data from Zhejiang Province, China, where the landslides are mainly triggered by rainfall. In the illustrative examples, the landslides from the inventory are divided into two time periods: The landslides in the first period are used to produce a landslide susceptibility map, and those in the late period are taken as validation samples for examining the predictive capability of the landslide susceptibility maps. These examples demonstrate that the landslide susceptibility map created by the proposed technique is reliable. [ABSTRACT FROM AUTHOR]

Landslide prediction is complex and involves many factors, such as geotechnical, geological, topographical, and even meteorological. This work presents a methodology by using a Data Mining approach in order to predict landslide occurrences induced by rainfall in Rio de Janeiro city. Landslide and rain data records from 1998 to 2001 were obtained from field technical reports and 30 automatic rain gauges, respectively. It was also collected data regarding soil parameters, including urban areas, forest, vulnerability, among others, and totalizing 46 soil variables. All the information was inserted into a Geographic Information Systems. Clustering ( Dendrogram and k- means) and Statistical ( Principal Component Analysis and Correlation) techniques were used to regionalize the rain data and select the rain gauges to be input on Artificial Neural Networks , which were used to replace the missing rain values. The landslide volume variable also presented missing values and it was completed by the k- Nearest Neighbor method. After data preparation, some models were built to predict landslide and rainfall using Data Mining techniques. The obtained model's performance is also analyzed. [ABSTRACT FROM AUTHOR]

On 19 February 2007, a landslide occurred on the Alaardiç Slope, located 1.6 km south of the town of Yaka (Gelendost, Turkey.) Subsequently, the displaced materials transformed into a mud flow in E¿lence Creek and continued 750 m downstream towards the town of Yaka. The mass poised for motion in the Yaka Landslide source area and its vicinity, which would be triggered to a kinetic state by trigger factors such as heavy or sustained rainfall and/or snowmelt, poise a danger in the form of loss of life and property to Yaka with its population of 3,000. This study was undertaken to construct a susceptibility mapping of the vicinity of the Yaka Landslide¿s source area and to relate it to movement of the landslide mass with the goal of prevention or mitigation of loss of life and property. The landslide susceptibility map was formulated by designating the relationship of the effecting factors that cause landslides such as lithology, gradient, slope aspect, elevation, topographical moisture index, and stream power index to the landslide map, as determined by analysis of the terrain, through the implementation of the conditional probability method. It was determined that the surface area of the Goksogut formation, which has attained lithological characteristics of clayey limestone with a broken and separated base and where area landslides occur, possesses an elevation of 1,100¿1,300 m, a slope gradient of 15°¿35° and a slope aspect between 0°¿67.5° and 157°¿247°. Loss of life and property may be avoided by the construction of structures to check the debris mass in E¿lence Creek, the cleaning of the canal which passes through Yaka, the broadening of the canal¿s base area, elevating the protective edges along the canal and the establishment of a protective zone at least 10-m wide on each side of the canal to deter against damage from probable landslide... [ABSTRACT FROM AUTHOR]

A spatially distributed physically based slope stability model combined with a hydrological model is presented and applied to a 350-km2 area located in Dhading district, Nepal. Land slide safety factor maps are generated for five cases, including three steady state conditions assuming either completely dry soils, half saturated soils, or fully saturated soils, and two quasi-dynamic conditions, i.e. soil wetness resulting from storm events with, respectively a 2 or 25-year return period. For the quasi-dynamic cases, two methods are used, one based on accumulation of groundwater flow from upstream areas, and the other on accumulation of soil water from direct infiltration. The methodology delineates areas most prone to shallow land sliding in function of readily available data as topography, land-use and soil types. For the study area only 29% of the soils are unconditionally stable, while 25% of the soils are found to be unstable under fully saturated conditions. The comparison between the methods based on contributing area or on infiltration for quasi-dynamic conditions show that the approach based on infiltration is more reliable for the study area. The proposed methodology for predicting landslide susceptibility on a regional scale, based on basic data in GIS form, may be useful for other remote regions where detailed information is not available. [ABSTRACT FROM AUTHOR]

On July 20, 2003, following a short duration of heavy rainfall, a debris-flow disaster occurred in the Minamata–Hougawachi area, Kumamoto Prefecture, Japan. This disaster was triggered by a landslide. In order to assess the landslide and debris-flow hazard potential of this mountainous region, the study of historic landslides is critical. The objective of the study is to couple 3D slope-stability analysis models and 2D numerical simulation of debris flow within a geographical information systems in order to identity the potential landslide-hazard area. Based on field observations, the failure mechanism of the past landslide is analyzed and the mechanical parameters for 3D slope-stability analysis are calculated from the historic landslide. Then, to locate potential new landslides, the studied area is divided into slope units. Based on 3D slope-stability analysis models and on Monte Carlo simulation, the spots of potential landslides are identified. Finally, we propose a depth-averaged 2D numerical model, in which the debris and water mixture is assumed to be a uniform continuous, incompressible, unsteady Newtonian fluid. The method accurately models the historic debris flow. According to the 2D numerical simulation, the results of the debris-flow model, including the potentially inundated areas, are analyzed, and potentially affected houses, river and road are mapped. [ABSTRACT FROM AUTHOR]