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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]

Rainfall-induced landslide masses often change into disastrous debris flows and damage large areas in Nepal's mountainous region. The area covered by debris flow inundation or debris flow runout is a most essential component for landslide hazard assessments leading to development of land use plans. However, there is presently no tool that can allow to assess and predict debris flow runout from the initial landslide on the watershed scale in Nepal. In this paper, a method and simulation tool are developed for the assessment and prediction of debris flow runout in Kulekhani Watershed, Nepal. The Flow-R model with user-defined landslide-susceptible areas was chosen for debris flow runout analysis. The Flow-R model with various algorithms has the capability to analyze debris flow inundation with limited input information. Two recent debris flow events are taken as case studies to identify the appropriate algorithms for runout analysis of the study area. After comparison of the observed and simulated results for debris flow runout, the algorithms proposed by Holmgren (Hydrol Process 8:327–334, 1994) (modified) are found suitable for the study watershed. These algorithms are employed for debris flow inundation analysis in the study area with pre-defined landslide sources plus debris flow inundation map in GIS environment. The results obtained from this modeling for the debris flow area induced by 540 mm of rainfall in 24 h period was 2.68% of the watershed, which is comparable to previously observed debris flow area in the study watershed. The findings of this research and GIS simulation tool developed will enable the optimization of planning and investment for land development in the studied region. [ABSTRACT FROM AUTHOR]

Landslide Hazard Zonation (LSH) maps play a key role in landuse planning particularly in landslide prone areas. LSH mapping is globally accepted one for analyzing the area for landslide susceptibility. Different approaches were followed by many researchers in India to prepare landslide hazard zonation mapping depending upon their need and requirement. The Nilgiris district in Western Ghats of India is one of the severe to high landslide hazard prone areas of India. Many agencies have carried out research on LSH mapping for the Nilgiris district with different scales. A systematic study of inventory and zonation was 122carried out in 1980's by government agencies. However there is no proper updation or documentation on landslides after 1980's in the district The purpose of this paper is to review the existing landslide-related studies in the district of The Nilgiris and review the district's existing landslide hazard map with updated information. Landslide hazard maps in The Nilgiris were compiled in the GIS platform from various authenticated sources. Data on landslides from 1824 to 2014 were collected and a spatial database on landslides was created. A detailed inventory was analyzed and used for revision of the district's landslide hazard impact on the 2009 landslides.. Based on the landslide inventory and densely populated areas and repeated landslides at the same locations, the most landslide hazard areas were identified. [ABSTRACT FROM AUTHOR]

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

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

Landslide susceptibility 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]

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

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

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]

This paper presents the results of geographical information system (GIS)-based landslide susceptibility mapping in Ayvalık, western Turkey using multi-criteria decision analysis. The methodology followed in the study includes data production, standardization, and analysis stages. A landslide inventory of the study area was compiled from aerial photographs, satellite image interpretations, and detailed field surveys. In total, 45 landslides were recorded and mapped. The areal extent of the landslides is 1.75 km2. The identified landslides are mostly shallow-seated, and generally exhibit progressive character. They are mainly classified as rotational, planar, and toppling failures. In all, 51, 45, and 4% of the landslides mapped are rotational, planar, and toppling types, respectively. Morphological, geological, and land-use data were produced using existing topographical and relevant thematic maps in a GIS framework. The considered landslide-conditioning parameters were slope gradient, slope aspect, lithology, weathering state of the rocks, stream power index, topographical wetness index, distance from drainage, lineament density, and land-cover and vegetation density. These landslide parameters were standardized in a common data scale by fuzzy membership functions. Then, the degree to which each parameter contributed to landslides was determined using the analytical hierarchy process method, and the weight values of these parameters were calculated. The weight values obtained were assigned to the corresponding parameters, and then the weighted parameters were combined to produce a landslide susceptibility map. The results obtained from the susceptibility map were evaluated with the landslide location data to assess the reliability of the map. Based on the findings obtained in this study, it was found that 5.19% of the total area was prone to landsliding due to the existence of highly and completely weathered lithologic units and due to the adverse effects of topography and improper land use. [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]

The present work aimed to assess the feasibility of three methods, namely, analytical hierarchy process, frequency ratio and binary logistic regression, that are used for landslide susceptibility analysis. Twelve causal factors such as slope, relative relief, road buffer, fault/fold and thrust (FFT) buffer, topographic wetness index (TWI), aspect, stream power index (SPI), drainage buffer, profile curvature, land used/land cover (LULC), soil and geology as well as a landslide inventory were retrieved using multi-source spatial data. Correlation between landslides and causative factors was estimated using frequency ratio on a geospatial platform with 234 landslide locations, extracted using field visits and remote sensing imageries and found to be reasonably influencing landslides in the study region. These factors were then used in all three landslide susceptibility models to calculate and generate a landslide susceptibility map (LSM). LSM of the study region Mao-Maram (tehsil) of Manipur was generated with five classes: very low, low, medium, high, and very high. Cumulative percentage curve and receiver operating characteristic (ROC) approaches were used for validation of the model performance. Cumulative percentage curve area under curve (AUC) accuracy for frequency ratio, analytical hierarchy process and binary logistic regression were found to be 77.24%, 73.22%, and 80.32%, respectively. ROC curve under AUC for frequency ratio 88.7%, analytical hierarchy process 77.4%, and binary logistic regression 90%. A landslide susceptibility map derived from every three models can be helpful for city or village planners for planning, development, mitigation, and disaster prevention. [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]

The primary objective of the present research is to apply and compare the performance of evidential belief function (EBF)-based logistic model trees (LMTs) and multiclass alternate decision trees (LADTrees) in landslide susceptibility mapping in Xiaojin County, China. Firstly, 328 landslides were mapped in the study area. Then, 70% of landslide points were used as training samples randomly, and the remaining 30% were intended for validation samples. For the study area, 12 landslide-related conditioning factors were identified, for instance, plan curvature, profile curvature, elevation, slope angle, slope aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), land use, lithology, distance to river soil, and distance to roads. The following procedure was to map landslide susceptible regions through EBF, LADTree and LMT models. Finally, the receiver operating characteristic (ROC) curve was utilized to contrast and test the capacity of the three models. The success rates with the training dataset were 0.880, 0.877 and 0.886 for the EBF, LADTree and LMT models, respectively. In addition, their prediction rates with the validation dataset were 0.846, 0.861 and 0.865, respectively. The results could provide references for disaster management and land-use planning. [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]

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping. [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]

This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study. [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]

Landslides are among the most costly and damaging natural hazards in mountainous regions, triggered mainly under the influence of earthquakes and/or rainfall. In the present study, Landslide Hazard Zonation (LHZ) of Dikrong river basin of Arunachal Pradesh was carried out using Remote Sensing and Geographic Information System (GIS). Various thematic layers namely slope, photo-lineament buffer, thrust buffer, relative relief map, geology and land use / land cover map were generated using remote sensing data and GIS. The weighting-rating system based on the relative importance of various causative factors as derived from remotely sensed data and other thematic maps were used for the LHZ. The different classes of thematic layers were assigned the corresponding rating value as attribute information in the GIS and an “attribute map” was generated for each data layer. Each class within a thematic layer was assigned an ordinal rating from 0 to 9. Summation of these attribute maps were then multiplied by the corresponding weights to yield the Landslide Hazard Index (LHI) for each cell. Using trial and error method the weight-rating values have been re-adjusted. The LHI threshold values used were: 142, 165, 189 and 216. A LHZ map was prepared showing the five zones, namely “very low hazard”, “low hazard”, “moderate hazard”, “high hazard” and “very high hazard” by using the “slicing” operation. [ABSTRACT FROM AUTHOR]