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Landslide susceptibility analysis based on the strong ability of data mining of Geographic Information System (GIS) has become a hot topic in international landslide research. This paper used optimized decision tree and GIS databases to analyze the sensitivity in the northwest mountain areas of Yunnan province of China, and then discussed the formation mechanism of the landslide happened in the area. The translational landslide located in the area with an average gradient less than or equal to 28.7° was reclassified as a higher level 3 sensitive area than before according to the normalized different fault index (NDFI). The results showed that the data mining based on GIS 3D space–time information database can help to find the unique topography, geology hydrology and the other typical spatial information of some special typed of landslides such as translational landslides, thus it can illustrate the relationship between the landslides and their sensitivity factors. The improved landslide susceptibility analysis will provide a new method for identifying the genetic mechanism of landslide, and play an important role in the government regional planning and disaster prevention measures. [ABSTRACT FROM AUTHOR]

Estimating the likelihood of landslides has proven to be critical for development and protection of infrastructure (e.g. pipelines, roads) and urban settlements. Currently, for regional studies of landslide susceptibility only qualitative or statistical evaluations are possible due to the large spatial variability of geological properties, topography, rainfall patterns, etc. In this paper, we explore an alternative to these approaches using data-driven methodologies to determine landslide susceptibility. We give special attention to the use of geographical information systems, machine learning and statistical techniques to build landslide susceptibility maps. These methods have input as fourteen key causative factors that might influence landslides occurrence. Additionally, feature extraction and feature selection are performed to evaluate if dimensionality reduction increases the prediction accuracy of the machine learning models. The models were compared using a case study in the Eastern Cordillera of Colombia, where the best performing model achieved a predictive performance of 93.07 % . [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]

Landslides are a serious natural hazard in the world. A map of landslide susceptibility can help to effectively reduce losses. In this paper, a hybrid ensemble technique based on random subspace (RS) and a J48 decision tree named RS–J48T was proposed for landslide susceptibility mapping. This model could enhance the effect of a single classifier significantly and solve the problem of overfitting. Qingchuan County, Sichuan (China) was taken as a study area. A geospatial database which consisted of 640 landslide locations and 12 factors was constructed for this study. The J48 decision tree, artificial neural network (ANN), and other ensemble techniques like AdaBoost and Bagging, were selected for comparison. Receiver operating curves and some statistical indices were used for model validation. The results showed that the RS–J48T model had the better fitting capability (AUC = 0.875), and the best prediction capability (AUC = 0.769) compared to other models. Overall, the novel hybrid model could be a promising way for generating landslide susceptibility maps for other prone areas. [ABSTRACT FROM AUTHOR]

Geomorphological analysis of landslide processes in mountainous terrains with difficult access has benefited from virtual representation of topography through the use of high-resolution digital elevation models (DEMs) generated by use of light detecting and ranging. Generic models of overlay and interpolation take advantage of the high-resolution DEMs for volume calculation. However, the advantages and limitations of the models at regional scale have received scant attention. These challenges are magnified in landslide hazard zonation mapping projects at state or national level, where the models need to be implemented for large datasets. To address the above deficiency, this paper presents a means to estimate landslide volume production and distribution by taking full advantage of LiDAR and to standardize landslide volume calculation in a geographic information system (GIS). We implemented two generic landslide volume models by using Python scripts; this is a systematic methodology for modeling volume of shallow and deep-seated landslides. The models were tested in real and theoretical conditions to highlight advantages and limitations. At the same time, we explored how the interpolation model is affected by local altimetric variation. The results show that one of the models can be used to make first-order interpretations regarding volume of eroded debris for landslide deposits at a local or national scale, while the other can help to assess the sequence of landslide activity. Theoretical evaluations show that local altimetric variation of < 1 m could lead to errors of almost 17%. The approach is explored with examples from Sumas Mountain in Whatcom County, Washington, USA. [ABSTRACT FROM AUTHOR]

The displacement of a large slow-moving landslide is accompanied by slope damage, such as fractures, tension cracks, and slope bulging. Studies of these features provide insight into the mechanisms responsible for the deformation. In this paper, we investigate slope damage at the Downie Slide, a very large landslide in British Columbia, Canada, that is slowly moving along two shear zones subparallel to the ground surface. Structural geology, and particularly the morphology of the lower shear zone, strongly controls the deformation and, in turn, the observed internal and surficial slope damage features. We use aerial and underground adit laser scanning and photogrammetry to characterize the geometry of the landslide. We subdivide the slide area into slope damage domains based on the distribution, size, and orientation of the slope damage features. We reconstruct the shape of the two failure surfaces by interpolating borehole inclinometer monitoring data and processing the 3D surfaces in a GIS environment to create aspect and slope maps. We observe a strong correlation between surficial slope damage features and changes in the dip and dip direction of the lower sliding surface. We further infer the presence of a multi-planar failure surface geometry and previously unrecognized structurally controlled damage zones. [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]

Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors (LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production (GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve (AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps (LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages. [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]

Development of hilly areas requiring deforestation and increased human activities has raised many environmental issues in the world. This paper presents the results of investigation to assess the parameters that contribute to the risk to environment by reviewing available reports from Penang Island and surrounding areas. Nine parameters were identified that mostly contribute to the environmental risk. These are casualties of people, soil properties, earth coverage, soil grading characteristics, land use suitability, factor of safety, blasting area, distance between proposed structure to slope and geometry of slope. From these parameters, the rated risk approaches were proposed based on the prediction of occurrences of environmental issues. The rated risks were categorised from very high (5) to very low (1) with high, medium and low in between. The assessments were reviewed and evaluated based on rated risk for the chosen parameters. A map was developed identifying areas of environmental risk. The map can be used to quantify risk to the environment based on the parameters due to the proposed development. The overall result shows that areas PD3 of Tanjung Bungah and PD3, PD8 and PD9 of Paya Terubong have high-rated risk. No development area is classified as very high risk and very low-risk areas. These rating approaches can be used to develop potential risk zoning of proposed development areas. From this zoning guideline or standard, the viability of any proposed development can be measured. [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]

The Lushan earthquake with magnitude M 7.0 ( M 6.6, USGS) in Sichuan Province, China, triggered a large number of landslides, which seriously aggravated the earthquake's destructive consequences. This paper mainly focuses on the methodology of the urgent landslide susceptibility assessment right after the earthquake. The detailed landslide inventory (including 5,688 landslides) is prepared by means of urgent post-earthquake landslide field survey, landslide remote sensing interpretation of multi-source remote sensing images including high-resolution unmanned aerial vehicle images and historical landslide archives. Ten remarkable causative factors for landslide occurrence have been selected to conduct the landslide susceptibility assessment, including earthquake intensity, landslide density and slope gradient. An integration assessment approach is developed to facilitate the effective urgent post-earthquake landslide susceptibility assessment using three methods: factor sensitivity analysis, analytical hierarchy process and factor-weighted overlay. Such integration can effectively reduce the subjectivity and uncertainty resulting from using single method. The validation evaluation using the area under curve suggests the landslide susceptibility assessment results have satisfactory accuracy, and the suggested methodology is effective for the urgent post-earthquake landslide susceptibility assessment. The study results reveal that earthquake intensity and slope gradient are the two most important causative factors for post-earthquake landslide occurrence in the Lushan earthquake-impacted area. The dominant slope gradient and slope aspect with relatively higher landslide frequency are 45°-50° and south-east direction, respectively. The intense earthquake impact increased the dominant slope gradient of landslide spatial distribution, and the thrust campaign of seismogenic fault with strike NE-SW made south-east direction as the dominant slope aspect of the landslide spatial distribution. The locations with very high and high landslide susceptibility are mainly distributed in the regions with higher earthquake intensity and adverse terrain conditions, such as Shuangshi town and Longmen town of Lushan county and Muping town of Baoxing county. The study results are expected to provide a beneficial reference for the landslide prevention and infrastructure reconstruction after the Lushan earthquake. [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]

The goal of this paper is to assess the landslide susceptibility of a hilly area in the Subcarpathian sector of the Prahova Valley, using the weight of evidence statistical method. This method aims to reduce the multitude of landslide-related conditions to a pattern of a few discrete predictive variables. The method is based on the decision of which state is more likely to occur grounded on the presence or absence of a predictive variable and the occurrence of an event (e.g., landslide) within a pixel. Based on the chi-square test and the Pearson correlation applied on the data, the selected conditionally independent variables in this study were as follows: slope gradient, slope aspect, and land use. Weights calculated individually for the three themes were added to produce a probability estimate of the area. The predictive power of the map was tested on the basis of a split sample of landslides that were not used in the modeling process. The fact that a great percent of the declivitous surfaces are susceptible to landslides shows the dominant manner of the evolution of the Subcarpathian slopes, the acceleration or deceleration of the process being influenced by the land use. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for developing a landslide hazard zonation map by integration of global positioning system (GPS), geographic information system (GIS), and remote sensing (RS) for Western Himalayan Kaghan Valley of Pakistan. The landslides in the study area have been located and mapped by using GPS. Eleven causative factors such as landuse, elevation, geology, rainfall intensity, slope inclination, soil, slope aspect, distances from main road, distances from secondary roads, and distances from main river and those from trunk streams were analyzed for occurrence of landslides. These factors were used with a modified form of pixel-based information value model to obtain landslide hazard zones. The matrix analysis was performed in remote sensing to produce a landslide hazard zonation map. The causative factors with the highest effect of landslide occurrence were landuse, rainfall intensity, distances from main road, distances from secondary roads, and distances from main river and those from trunk streams. In conclusion, we found that landslide occurrence was only in moderate, high, or very high hazard zones, and no landslides were in low or very low hazard zones showing 100% accuracy of our results. The landslide hazard zonation map showed that the current main road of the valley was in the zones of high or very high hazard. Two new safe road routes were suggested by using the GIS technology. [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]

A landslide susceptibility analysis has been made in the Río Aguas catchment (Almeria, Southeast Spain), using two statistical models, Maximum Entropy (MaxEnt) and Geographically Weighted Logistic Regression (GWLR). For this purpose, a previous landslide inventory has been used and re-elaborated, reaching a total incidence of 2.58% of the whole area. Different types of movements have been distinguished, being rock falls, slides and complex movements the predominant. From the inventory, the centroid of the rupture zone has been extracted to represent the landslides introduced in the models. A previous factor analysis has been made, using 12 predictors related to morphometry, hydrography, geology and land cover, with 5 m grid spacing, allowing the selection of factors to be used in the analysis and discarding those showing correlation between them. Then, MaxEnt and GWLR models are applied using different distributions of training and testing samples from the landslide inventory. For the validation, the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) has been used but additionally, the degree of fit (DF) has allowed to validate the rupture zones themselves, not only the centroids. Results show an excellent prediction with both metrics in all the methods and samples, but the better results are obtained in the GWLR method for AUC and in the MaxEnt for the degree of fit. Therefore, a consensus model of both methods has been obtained, that improves even more the results reaching an AUC value of 0.99 and a degree of fit of 90%. [ABSTRACT FROM AUTHOR]

This study aimed to examine the influence of the random selection of landslide training and testing sets on the predictive performance of the shallow landslide susceptibility modelling at regional scale. The performance of frequency ratio (FR), information value (IV), logistic regression (LR), and maximum entropy (ME) methods were tested and compared for modeling shallow landslide susceptibility in Calabria region, (South Italy). A landslide database of 22,028 shallow landslides, randomly split into training (70%) and testing (30%) sets, was combined with 15 predisposing factors (lithology, soil texture, soil bulk density, soil erodibility, drainage density, land use, elevation, local relief, slope gradient, slope aspect, plan curvature, topographic wetness index, stream power index, topographic ruggedness index, and topographic position index) to calibrate and validate the models. The robustness of the models in response to changes in the landslide dataset was explored through ten training and test sets replicates. The performance of these models was evaluated using several statistical indices and the ROC curve method. The results showed that all the four methods applied achieve promising performance on the prediction of shallow landslide susceptibility at regional scale. The comparison between four methods displayed that the ME is the best performing (AUC = 0.866), followed by the LR (AUC = 0.845), FR (AUC = 0.813), and finally IV (AUC = 0.800). In addition, the findings showed that the accuracy of the four methods for modeling shallow landslide susceptibility was quite robust when the training and testing sets were changed (i.e. a very low sensitivity to varying training/testing sets). [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]

Landslides are one of the most destructive natural phenomena in the world, which occur mostly in mountainous areas and cause damage to the economic sectors, agricultural lands, residential areas and infrastructures of any country, and also threaten the lives and property of human beings. Therefore, landslide susceptibility mapping (LSM) can play a critical role in identifying prone areas and reducing the damage caused by landslides in each area. In the present study, deep learning algorithms including convolutional neural network (CNN) and long short-term memory (LSTM) were used to identify landslide prone areas in Ardabil province, Iran. Then 312 landslide locations were identified and randomly divided into train and test datasets, and according to previous studies and environmental conditions in the study area, twelve factors affecting the occurrence of landslides were selected. The ratio of the importance of each influential factor in landslide occurrence was obtained through information gain ranking filter method and it was found that land-use and profile curvature had the highest and lowest impacts, respectively. Afterwards, LSMs were generated using CNN and LSTM algorithms. In the next step, the performance of the models was evaluated based on the area under curve (AUC) value of receiver operating characteristics curve and the root mean square error (RMSE) method. The AUC values for CNN and LSTM models were 0.821 and 0.832, respectively. Furthermore, the RMSE values in the CNN model for each of the training and testing dataset were 0.121 and 0.132, respectively. The RMSE values in the LSTM model for each of the training and testing dataset were 0.185 and 0.188, respectively. Therefore, it can be concluded that LSTM performance is slightly better than CNN; but in general, both models have close performance and the accuracy of both models is acceptable. [ABSTRACT FROM AUTHOR]

In Nefas-Mewcha to Weldiya road corridor (study area), landslide incidence resulted in the death of people, devastation of infrastructure, properties, crops, and agricultural lands. To reduce damages due to landslide incidences, a complete landslide susceptibility mapping was carried out using GIS-based index of entropy (IOE) and frequency ratio (FR) models. Detailed fieldwork and google earth imagery analysis were used to identify 712 landslides. These landslides were divided into two categories: 498 (70%) for modeling and 214 (30%) for model validation. The spatial relationship between pre-existing landslides and 12 landslide factors was performed. Using a raster calculator, the weighted landslide factors were combined to provide a landslide susceptibility index (LSI). The natural break classification method was used to divide the LSI into five categories: very low, low, moderate, high, and very high susceptibility zones. The area under the curve (AUC) and the receiver operating characteristic (ROC) curves were used to assess the models' performance and accuracy. The results showed that the IOE model (AUC = 70%) performed somewhat better than the FR model (AUC = 66.41%) in terms of prediction. The IOE method also showed slightly high model performance compared to FR with the success rate of AUC values (71.3% for IOE and 69% for FR). In the IOE model which was produced after selecting the landslide factors, the success rate showed an increment from 71.3% to 74.5%. Similarly, the FR model also showed significant change in a success rate of 78.1% and a predictive rate of 73.5%. According to this finding, the performance and predictability of landslide susceptibility mapping methods are influenced by landslide factors. Therefore, landslide factor optimization is a critical task before landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Mapping the susceptibility of earthquake-induced soil liquefaction at the continental scale is a challenge. Susceptibility of soils to liquefaction is the tendency of certain geomaterials to undergo a severe stiffness degradation and loss of shear strength. The latter could be induced by cyclic loading induced by seismic events. The liquefaction surface evidence is a local phenomenon, and detailed geotechnical field investigations are not available for regional studies, not to mention at continental scales. The literature review shows earthquake-induced soil liquefaction evidence in several European countries, yet, a comprehensive picture of the susceptibility at the European scale is not available. This work aims to develop a methodology to assess the earthquake-induced soil liquefaction susceptibility in Europe using geospatial parameters weighted via the Analytic Hierarchy Process (AHP). The major outcome of the study is a novel Liquefaction Susceptibility map of Europe (LSE), based on the building of ten different European macro-units for earthquake-induced soil liquefaction. These European macro-units have been delineated in this work in order to be homogenous areas from the geological, physiographical, and geomorphological points of view. The adopted input parameters are the depositional environment of the sediments, the distance from water bodies (coast and rivers), and the compound topographic index (as a proxy of the soil saturation). The resolution of the LSE map is 900 × 900 m. The results have been cross-compared with soil liquefaction susceptibility maps available for a region of Greece (i.e. Thrace), Portugal, Bulgaria, and sites where liquefaction manifestations occurred across Europe. The LSE can be adopted to identify at large scale the areas susceptible to liquefaction including also the territories across Europe characterized by low seismicity and potentially affected by anthropogenic seismicity. [ABSTRACT FROM AUTHOR]

Landslide susceptibility consists of an essential component in the day-to-day activity of human beings. Landslide incidents are typically happening at a low rate of recurrence when compared and in contrast to other events. This might be generated into main natural catastrophes relating to widespread and undesirable sound effects. Landslide hotspot area identification and mapping are used for the regional community to secure from this disaster. Therefore, this research aims to identify the hotspot areas of landslide and to generate maps using GIS, AHP, and multi-criteria decision analysis (MCDA). MCDA techniques are applied under such circumstances to categorize and class decisions for successive comprehensive estimation or else to state possible from impossible potentiality with various landslides. Analytical hierarchy process (AHP) constructively applies for conveying influence to different criteria within multi-criteria decision analysis. The causative landslide identifying factors utilized in this research were elevation, slope, aspect, soil type, lithology, distance to stream, land use/land cover, rainfall, and drainage density achieved from various sources. Subsequently, to explain the significance of each constraint into landslide susceptibility, all factors were found using the AHP technique. Generally, landslide susceptibility map factors were multiplied by their weights to acquire with the AHP technique. The result showed that the AHP methods are comparatively good quality estimators of landslide susceptibility identification in the Chemoga watershed. As the result, the Chemoga watershed landslide susceptibility map classes were classified as 46.52%, 13.83%.18.71%, 15.39%, and 5.55% of the occurred landslide fall to very low, low, moderate, high, and very high susceptibility zones, respectively. Performance and accuracy of modeled maps have been established using GPS field data and Google earth data landslide map and area under curve (AUC) of the receiver operating characteristic curve (ROC). As the result, validation depends on the ROC specifies the accuracy of the map formed with the AHP merged through weighted overly method illustrated very good accuracy of AUC value 81.45%. In general, the research outcomes inveterate the very good test consistency of the generated maps. [ABSTRACT FROM AUTHOR]

Landslides are serious natural disasters frequently occurring in the mountainous areas along the Sichuan–Tibet railway. Landslides are often induced by rainfall and greatly threaten people's lives and property. Via the use of GIS technology, a probabilistic analysis model was proposed for rainfall-induced shallow landslides along the above route. The process of the proposed probabilistic analysis model could be divided into three parts: (i) an infiltration hydrogeological model, (ii) a slope stability model, and (iii) probabilistic analysis. In this model, soil cohesion and internal friction angle were regarded as random parameters. The Green–Ampt model was used to dynamically describe the soil infiltration process during rainfall. Combined with the infinite slope stability model, the failure probability in the study area was analyzed. The model was applied to simulate the Bayi catchment landslide event of August 18, 2010, to evaluate its reliability. Through comparison of the simulation results to landslide occurrence locations, it was determined that the model achieves a satisfactory prediction performance. In addition, compared to deterministic analysis methods and the r.slope model, the proposed probabilistic analysis model achieved a satisfactory evaluation performance. The presented physics-based probabilistic analysis model could provide important theoretical support for hazard prevention in regard to rainfall-induced shallow landslides in mountainous areas. [ABSTRACT FROM AUTHOR]

It is well known that geological structure is one of the main predisposing factors that control landslide occurrence at regional scale. Along time, investigation of the role of structural and lithologic controls has raised the interest of specialists from various earth and planetary sciences who proposed different approaches that range from descriptive analysis to automatic detection. The aim of this study is to investigate the role of structural and lithological controls on landslide occurrence at regional scale. To achieve this, a collection of tools for calculating the TOBIA index and estimating the relationship between morphostructural and lithological conditions and landslide occurrences was developed using Python programming language. The relationship between geological structure and landslide occurrences was analyzed in three distinct landslide-prone sites in Romania, located in different geological and geomorphological contexts. The spatial distribution of the geological structure is modelled based on the TOBIA index (TOpographic/Bedding-plane Intersection Angle). The TOBIA index uses topographic slope, slope aspect, dip angle, and dip direction to divide the terrain into three categories named cataclinal, anaclinal, and orthoclinal, with each category having several subcategories. Additionally, by implementing the bivariate statistical analyses (Weight of Evidence), we investigated the relationship of landslide occurrences to the resulted morphostructural slopes, on the one hand, and to the lithological units, on the other hand. The output contrast values show an evident influence of the cataclinal slopes over landslide occurrences in homoclinal areas, with most of the scarps occurring at the contact between cataclinal and orthoclinal slopes. Fewer influences are found in areas where clay outcrops and where the geological structure has a much-diminished control over the presence of the landslides. These results contribute to a better understanding of the role of structural and lithologic controls on landslide occurrence at regional scale and furthermore could serve to a better assessment of landslide susceptibility. The results obtained on different geologic and geomorphic contexts in Romania suggest that this methodology can be applied successfully in other areas prone to landslides. For reproducibility purposes, the GIS set of tools was made available under the MIT license. [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]

Landslides have been a great threat to the economy as well as human lives in the north-eastern border state, Manipur, India. Highways serve as the main lifelines of the state where hundreds of goods carrying heavy vehicles are transporting every day. However, during the rainy seasons, these highways are often collapsed due to landslides. This study aims to analyze the effect of multi-resolution Digital Elevation Models (DEMs) on landslide susceptibility mapping along a major highway in Manipur, India. Nine-landslide conditioning factors including Land Use Land Cover (LULC), slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), distance to roads, distance to streams, and distance to faults were selected. The topographic factors were extracted from three different DEM sources-CARTOSAT 10 m, ASTER 30 m and SRTM 90 m. The Analytical Hierarchy Process approach is employed to assign appropriate weights to the conditioning factors, and the weighted factors are integrated to produce the landslide susceptibility maps. The assessment of the model accuracies showed that LSM1 (10 m) procured a success accuracy of 83.78% and prediction accuracy of 81.45%, while LSM2 (30 m) resolution procured a success accuracy of 72.8% and prediction accuracy of 70.45%, and LSM3 (90 m) resolution procured a success accuracy of only 58% and prediction accuracy of 55.87%, respectively. In this particular study, the main aim is to analyse the effect of resolution in Landslide Susceptibility mapping along a major highway in Manipur, India, which has been witnessing landslides every year. The study also aims to select the optimal DEM resolution. The selection for optimal DEM resolution is dependent on scale and the topographic setting of the region under consideration. The susceptibility mapping results of this study indicates that CARTOSAT 10 m resolution DEM produced better accuracy as compared to other DEMs for this particular site. Policy makers can use the resulting map in planning purposes and risk management. [ABSTRACT FROM AUTHOR]

Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of hyperparameters is of great importance to achieve better accuracy in a landslide hazard assessment model. In this study, a novel approach is proposed for landslide hazard assessment with support vector machine (SVM) as the primary model and Bayesian optimization (BO) algorithm as the parameter tuning method. This study describes 1711 historical landslide disaster points in Nanping City, and a total of 12 landslide conditioning factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected. The multicollinearity diagnosis was performed on the factors using the Spearman correlation coefficient. For model validation, 1711 landslides and 1711 non-landslides were collected as the dataset and divided into a training dataset (50 %) and a testing dataset (50 %). The performance of the model was evaluated by the confusion matrix and receiver operating characteristic (ROC) curve. The results of the confusion matrix accuracy and the area under the ROC curve showed that the BO-SVM model (89.53 %, 0.97) performed better than the SVM model (84.91 %, 0.93). In addition, the landslide hazard maps generated by the BO-SVM model had better overall results than that by the SVM model. [ABSTRACT FROM AUTHOR]

Fires have increased in northeastern Iran as its semi-arid climate landscape is desiccated by human activities. To combat fire outbreaks in any region, fire susceptibility must be mapped using accurate and efficient models. This research mapped fire susceptibility in the forests and rangelands of Golestan Province in northeastern Iran using new data-mining models. Fire effective factors, including elevation, slope angle, annual mean rainfall, annual mean temperature, wind effect, topographic wetness index (TWI), plan curvature, distance to river, distance to road, and distance to village were obtained from several sources. The relative importance of each variable was determined using a random-forest algorithm. Fire-susceptibility maps were produced in R 3.0.2 software using GAM, MARS, SVM algorithms, and a new ensemble of the three models: GAM-MARS-SVM. The four fire-susceptibility maps were validated using the area under the curve. The results show that the distance to the village, annual mean rainfall, and elevation were of greatest importance in predicting fire susceptibility. The new GAM-MARS-SVM ensemble model achieved the highest precision of fire-susceptibility mapping. The fire-susceptibility map produced using the GAM-MARS-SVM ensemble model best detected the high fire risk areas in Golestan Province. The fire-susceptibility map produced by the ensemble model can be very useful for creating and enhancing management strategies for preventing fires, particularly in the higher-risk portions of Golestan Province. [ABSTRACT FROM AUTHOR]

Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the lives and economy of the communities residing in that mountainous topography. This study aims to investigate whether the landscape change has any impact on landslide occurrences in the Kalsi-Chakrata road corridor by detailed investigation through correlation of the landslide susceptibility zones and the landscape change, and finally to demarcate the hotspot villages where influence of landscape on landslide occurrence may be more in future. The rational of this work is to delineate the areas with higher landslide susceptibility using the ensemble model of GIS-based multi-criteria decision making through fuzzy landslide numerical risk factor model along the Kalsi-Chakrata road corridor of Uttarakhand where no previous detailed investigation was carried out applying any contemporary statistical techniques. The approach includes the correlation of the landslide conditioning factors in the study area with the changes in land use and land cover (LULC) over the past decade to understand whether frequent landslides have any link with the physical and hydro-meteorological or, infrastructure, and socioeconomic activities. It was performed through LULC change detection and landslide susceptibility mapping (LSM), and spatial overlay analysis to establish statistical correlation between the said parameters. The LULC change detection was performed using the object-oriented classification of satellite images acquired in 2010 and 2019. The inventory of the past landslides was formed by visual interpretation of high-resolution satellite images supported by an intensive field survey of each landslide area. To assess the landslide susceptibility zones for 2010 and 2019 scenarios, the geo-environmental or conditioning factors such as slope, rainfall, lithology, normalized differential vegetation index (NDVI), proximity to road and land use and land cover (LULC) were considered, and the fuzzy LNRF technique was applied. The results indicated that the LULC in the study area was primarily transformed from forest cover and sparse vegetation to open areas and arable land, which is increased by 6.7% in a decade. The increase in built-up areas and agricultural land by 2.3% indicates increasing human interference that is continuously transforming the natural landscape. The landslide susceptibility map of 2019 shows that about 25% of the total area falls under high and very high susceptibility classes. The result shows that 80% of the high landslide susceptible class is contained by LULC classes of open areas, scrubland, and sparse vegetation, which point out the profound impact of landscape change that aggravate landslide occurrence in that area. The result acclaims that specific LULC classes, such as open areas, barren-rocky lands, are more prone to landslides in this Lesser Himalayan road corridor, and the LULC-LSM correlation can be instrumental for landslide probability assessment concerning the changing landscape. The fuzzy LNRF model applied has 89.6% prediction accuracy at 95% confidence level which is highly satisfactory. The present study of the connection of LULC change with the landslide probability and identification of the most fragile landscape at the village level has been instrumental in delineation of landslide susceptible areas, and such studies may help the decision-makers adopt appropriate mitigation measures in those villages where the landscape changes have mainly resulted in increased landslide occurrences and formulate strategic plans to promote ecologically sustainable development of the mountainous communities in India's Lesser Himalayas. [ABSTRACT FROM AUTHOR]

The primary objective is to propose and verify an ensemble approach based on fuzzy system and bivariate statistics for landslide susceptibility assessment (LSA) at Azarshahr Chay Basin (Iran). In this regard, various integrations of fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with index of entropy (IOE) were investigated. Aerial photograph interpretations and substantial field checking were used to identify the landslide locations. Out of 75 identified landslides, 52 (≈70%) locations were utilized for the training of the models, whereas the remaining 23 (≈30%) cases were employed for the validation of the models. Fourteen landslide conditioning factors including altitude, slope aspect, slope degree, lithology, distance to fault, curvature, land use, distance to river, topographic position index (TPI), topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), distance to road, and rainfall were prepared and utilized during the analysis. The FMV _ IOE , FR _ IOE , and IV _ IOE models were designed utilizing the dataset for training. Finally, to validate as well as to compare the model's predictive abilities, the statistical measures of receiver operating characteristic (ROC), including sensitivity, accuracy, and specificity, were employed. The accuracy of 92.7, 92.5, and 91.8% of the models such as FMV _ IOE , FR _ IOE , and IV _ IOE ensembles, respectively, was by the area under the receiver operating characteristic (AUROC) values developed from the ROC curve. For the validation dataset, the FMV _ IOE model had the maximum sensitivity, accuracy, and specificity values of 95.7, 91.3, and 87.0%, respectively. Thus, the ensemble of FMV_IOE was introduced as a promising and premier approach that could be used for LSA in the study area. Also, IOE results indicated that altitude, lithology, and slope degree were main drivers of landslide occurrence. The results of the present research can be employed as a platform for appropriate basined management practices in order to plan the highly susceptible zones to landslide and hence minimize the expected losses. [ABSTRACT FROM AUTHOR]

Landslides cause loss of lives and serious material damage almost every year around the world. In Turkey, risks related to landslides increase due to the combined effects of climate change, deforestation, uncontrolled urbanization and improper land use. Artvin is among the cities where landslides occur most frequently due to its topographical and geological characteristics. In the present study, landslide susceptibility of the Central district of Artvin was evaluated using the frequency ratio (FR) and modified information value (MIV) models. In the study, ten parameters affecting the occurrence of landslides were considered. 70% of the landslide inventories were utilized to create susceptibility maps, and the remaining 30% were utilized for validation. The success and prediction capabilities of the models were assessed using the receiver operating characteristics curve and area under the curve. The success rates of the MIV and FR models were calculated as 88% and 84.9%, respectively, and the prediction rates were computed as 82.7% and 81.9%, correspondingly. The MIV model showed a slightly better performance compared to the FR model in terms of prediction and success rates. It was also determined that most of the village built-up areas, as well as the majority of the planned areas of the Artvin Municipality and majority of the public and private buildings within the municipal boundaries, were located in landslide susceptible zones. Therefore, the outcomes of the present study may assist local administrators, planners, and engineers in reducing the damage caused by landslides and planning the optimal land use. [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]

Landslide is a complex natural hazard that sometimes causes disaster resulting in loss of life, assets and infrastructure, especially in the Himalayas. Recent studies suggest that for effective mitigation and resilience through proper planning and policymaking, it is equally important to justify and select a suitable scientific technique that most appropriately addresses the salient causes of a landslide in any area. The principal objective of this study is to carry out a comparative assessment between two contemporary statistical techniques, i.e., the statistical information value (SIV) and index of entropy (IOE), to find out the effectiveness of the two said methods in landslide susceptibility mapping in Bhanupali-Beri region. During the analysis, the higher-resolution satellite images, i.e., World view-2 image of 2017 and Landsat-8 OLI image of 2018, have been used for delineation of various triggering parameters used for landslide susceptibility. The contemporary GIS technique integrated with the remote sensing applications was distinct in preparing the prominent landslide conditioning factor layers such as slope, slope aspect, thrust and fault proximity, geomorphology, landuse–landcover, stream power index, topographic wetness index, geology, roads proximity, lineament density and past landslide inventory. The final assessment was performed using GIS software through raster re-sampling, and the values derived for each conditioning factors were combined using defined SIV and IOE equations. The study area was categorized into five distinct landslide susceptible zones (very low, low, moderate, high and very high) using the Jenk's Natural Breaks algorithm. Index of entropy model has given better results compared to SIV. The utmost vital factors triggering landslide (estimated for entropy values) in the area are landuse–landcover with barren land and sparse vegetation followed by TWI, lineament density, geomorphology, and slope. [ABSTRACT FROM AUTHOR]

Kabi-Gebro locality of Gundomeskel area is located within the Abay Basin at Dera District of North Shewa Zone in the Central highland of Ethiopia and it is about 320Km from Addis Ababa. This is characterized by undulating topography, intense rainfall, active erosion and highly cultivated area. Geologically, it comprises weathered sedimentary and volcanic rocks. Active landslides damaged the gravel road, houses and agricultural land. The main objective of this research is to prepare the landslide susceptibility map using GIS-based Weights of Evidence model. Based on detailed field assessment and Google Earth image interpretation, 514 landslides were identified and classified randomly into training landslides (80%) and validation landslides (20%). The most common types of landslides in the study area include earth slide (rotational and translational slide), debris slide, debris flow, rock fall, topple, rock slide, creep and complex. Nine landslide causative factors such as lithology, slope, aspect, curvature, land use/land cover, distance to stream, distance to lineament, distance to spring and rainfall were used to prepare a landslide susceptibility map of the study area by adding the weights of contrast values of these causative factors using a rater calculator of the spatial analyst tool in ArcGIS. The final landslide susceptibility map was reclassified as very low, low, moderate, high and very high susceptibility classes. This susceptibility map was validated using landslide density index and area under the curve (AUC). The result from this model validation showed a success rate and a validation rate accuracy of 82.4% and 83.4% respectively. Finally, implementing afforestation strategies on bare land, constructing surface drainage channels & ditches, providing engineering reinforcements such as gabion walls, retaining walls, anchors and bolts whenever necessary and prohibiting hazardous zones can be recommended in order to lessen the impact of landslides in this area. [ABSTRACT FROM AUTHOR]

Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the high potential of landslides in the region. The aim of the study was to combine adaptive neuro-fuzzy inference system (ANFIS) with grey wolf optimizer (GWO) and particle swarm optimizer (PSO) algorithms using the outputs of qualitative stepwise weight assessment ratio analysis (SWARA) and quantitative certainty factor (CF) models. To this end, 264 landslide positions and twelve conditioning factors including slope, aspect, altitude, distance to faults, distance to rivers, distance to roads, land use, lithology, rainfall, plan and profile curvature and TWI were then extracted considering regional characteristics, literature review and available data. In the next step, the multi-criteria SWARA decision-making model and CF probability model were used to evaluate a correlation between landslide distribution and conditioning factors. Ultimately, landslide susceptibility maps were generated by ANFIS-GWO and ANFIS-PSO hybrid models and the accuracy of models was assessed by ROC curve. According to the results, the area under the curve (AUC) for the hybrid models ANFIS - GWO SWARA , ANFIS - PSO SWARA , ANFIS - GWO CF and ANFIS - PSO CF was 0.789, 0.838, 0.850 and 0.879, respectively. The hybrid models ANFIS - PSO CF and ANFIS - GWO SWARA showed the highest and lowest prediction rate, respectively. Moreover, CF outperformed the SWARA method in terms of evaluating correlation between conditioning factors and landslides. The map produced in this study can be used by regional authorities to manage landslide risk. [ABSTRACT FROM AUTHOR]

A complete landslide dam hazard management incorporates two assessment phases: the damming probability and the breach hazard. A prompt evaluation of the dam stability is crucial during the emergency to mitigate its consequences, but a reliable risk assessment can be realized only after the event has occurred, when the available time is very short. Therefore, it is necessary to develop tools able to help in mapping the spatial probability of damming over large areas for land-use planning, in order to better constrain consequence analysis and risk scenarios for setting up mitigation measures. In this work, a semi-automated GIS-based mapping methodology, based on a statistical correlation of morphometric parameters described by a morphological index, is proposed to spatially assess the likelihood of a river obstruction by landslide damming through two main mechanisms: the reactivation of existing landslides and the formation of new landslides. The two mapping methods (damming predisposition and damming probability) were used on a test area, the Arno River basin in Italy. The Eastern part of the basin resulted as the most susceptible to damming events in the whole basin. These are the highest mountain ridges in the basin (about 1600 m a.s.l.), characterized by calcareous, arenaceous, and marl lithology. The results are confirmed by the high concentration of the known historical landslide dams in the area according to existing inventories. [ABSTRACT FROM AUTHOR]

Landslides cause damages to land and infrastructure and pose serious threat to human survival. To prepare a landslide susceptibility map of the region in Longnan, City Gansu Province, with a 330-kV transmission line, 10 parameters were selected by correlation analysis and sensitivity analysis from initial 18 and five different methods were used, including analytical hierarchy process (AHP), information value (IV), fractal theory (FT), back propagation neural network (BPNN), support vector machine (SVM). The susceptibility maps were validated through receiver operating characteristic (ROC) and cumulative landslides percentage curves based on 77 existing landslide events. The results indicate that BPNN and SVM model are most accurate, time-saving and easily implemented. All of the five methods accurately predict the spatial distribution of landslides and can be well applied to landslide susceptibility mapping. What needs to be emphasized is that the machine learning methods have the advantages of high efficiency, accurate prediction, time-saving, convenient implementation, which are relatively new and better evaluation models of susceptibility. [ABSTRACT FROM AUTHOR]

Landslides are one of the most damaging disastrous phenomena that frequently lead to serious problems in hilly areas. The Namchi region of South Sikkim district as a part of Eastern Himalayas is not an exception to it. In the present study, multi-criteria analysis technique is used for landslide hazard zonation mapping. Various thematic layers, namely slope, rainfall distribution map, lineament density, drainage density, slope aspect, geology, land use/land cover and soil map, were integrated in a GIS platform (ArcGIS 10.1) to delineate landslide hazard zone. Analytic hierarchy process was used to determine the weight values of different factors. Relative rating values are assigned for the subclasses of each thematic layer based on their corresponding impact on the landslide triggers, and within a thematic layer, each class was assigned an ordinal rating from 0 to 9. The landslide hazard zonation map of Namchi region was produced based on weighted overly techniques. The landslide hazard map of Namchi region is divided into five vulnerable zones, namely very low-, low-, moderate-, high- and very high-hazard zones. Resulted landslide hazard zonation map was further validated with field study and geospatial technology-based analysis. The findings demonstrate high-landslide-hazard zones are associated with areas of active erosive processes (steep slopes/cut slopes/lineaments). The results indicate the villages Bomtar, Jorethang, Kopchey, Donok, Namthang, Sumbuk, Longchok, Mamring, Turung, Mikkhola, etc. are highly prone to landslides. The final landslide hazard zonation map can be used for the landslide hazard prevention, proper planning of future infrastructure and geoenvironmental development in Namchi region. [ABSTRACT FROM AUTHOR]

A landslide susceptibility map is a crucial tool for land-use spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze and characterize recent landslide events in the Larji-Kullu Tectonic Window (LKTW), and to establish a relationship between the tectonic and lithologic characters of the terrain and the landslides activity. Using multispectral satellite image analysis with selected field investigation, a landslide occurrence database has been generated for the period between 1984 and 2015. To decipher the accelerated occurrences of landslides in the region, an integrated study is undertaken in the Kullu (also known as Kulu) valley of Beas River basin within the LKTW complex, to analyze the litho-structural and terrain slope interactions using morpho-tectonic parameters such as Topographic/Bedding Plane Interaction Angle (TOBIA) index, terrain surface roughness index and lithological competency analysis. A prominent clustering of landslides is observed in the north of Sainj River, contained within the tectonic window. Major sites of landslides are found to be located in the intensely fractured Manikaran Quartzite occurring within the core of the LKTW. The landslides are mostly associated with southern and southwestern-facing slopes and activations are pronounced in the ‘Orthoclinal’ slope class with gradient of 37°-48°. Thematic maps, e.g., geological, structural, geomorphological, slope and slope-aspect maps are generated and considered together to understand the morpho-tectonic scenario of the tectonic window. Observations from the above-stated thematic maps along with the occurrences of moderate magnitude earthquake epicenters helped to infer neotectonic movements along the Sainj River fault. Tectonic upliftment of the northern bank of the Sainj River along with increased precipitation through decades has resulted in recurrent landslides within the LKTW. [ABSTRACT FROM AUTHOR]

The study area located in southern Kyrgyzstan is affected by high and ongoing landslide activity. To characterize this activity, a multi-temporal landslide inventory containing over 2800 landslide polygons was generated from multiple data sources. The latter include the results of automated landslide detection from multi-temporal satellite imagery. The polygonal representation of the landslides allows for characterization of the landslide geometry and determination of further landslide attributes in a way that accounts for the diversity of conditions within the landslide, e.g., at the landslide main scarp opposed to its toe. To perform such analyses, a methodology for efficient geographic information system (GIS)-based attribute derivation was developed, which includes both standard and customized GIS tools. We derived a number of landslide attributes, including area, length, compactness, slope, aspect, distance to stream and geology. The distributions of these attributes were analyzed to obtain a better understanding of landslide properties in the study area as a preliminary step for probabilistic landslide hazard assessment. The obtained spatial and temporal attribute variations were linked to differences in the environmental characteristics within the study area, in which the geological setting proved to be the most important differentiating factor. Moreover, a significant influence of the different data sources on the distribution of the landslide attribute values was found, indicating the importance of a critical evaluation of the landslide data to be used in landslide hazard assessments. [ABSTRACT FROM AUTHOR]