Diagnosis of landslide risk for individual buildings: insights from Prahova Subcarpathians,Romania

The aim of this study is to quantify the landslide risk for individual buildings using spatial data in a GIS environment. A landslide-prone area from Prahova Rivers’ Subcarpathian Valley was chosen because of its associated landslide hazards and its impact upon human settlements and activities. The bivariate landslide susceptibility index (LSI) was applied to calculate the spatial probability of landslides occurrence. The Landslide Susceptibility Index map was produced by numerically adding the weighted thematic maps for slope gradient and aspect, water table, soil texture, lithology, built environment and land use. Validation curves were obtained using the random-split strategy for two combinations of variables: (a) all seven variables and (b) three variables which showed highest individual success rates with respect to landslides occurrences (slope gradient, water table and land use). The principal pre-disposing factors were found to be slope steepness and groundwater table. Vulnerability was established as the degree of loss to individual buildings resulting from a potential damaging landslide with a given return period in an area. Risk was calculated by multiplying the spatial probability of landslides by the vulnerability for each building and summing up the losses for the selected return period.     

Landslides in the North of Lisbon Region (Portugal): Conditioning and triggering factors

Detailed geomorphological mapping carried out in 5 sample areas in the North of Lisbon Region allowed us to collect a set of geological and geomorphological data and to correlate them with the spatial occurrence of landslide. A total of 597 slope movements were identified in a total area of 61.7 km², which represents about 10 landslides per km².The main landslide conditioning factors are: lithology and geological structure, slope angle and slope morphology, land use, presence of old landslides, and human activity.The highest landslide density occurs in Cretaceous marls and marly limestones, but the largest movements are in Jurassic clays, marls and limestones.The landslide density is higher on slopes with gradients above 20 °, but the largest unstable area is found on slopes of 10 ° to 15 °, thus reflecting the presence of the biggest slope movements. There is a correlation between landslides and topographical concavities, a fact that can be interpreted as reflecting the significance of the hydrological regime in slope instability.Concerning land use, the highest density of landslides is found on slopes covered with shrub and undergrowth vegetation.About 26% of the total number of landslides are reactivation events. The presence of old landslides is particularly important in the occurrence of translational slides and complex and composite slope movements.20% of the landslide events were conditioned by anthropomorphic activity. Human's intervention manifests itself in ill-consolidated fills, cuts in potentially unstable slopes and, in a few cases, in the changing of river channels.Most slope movements in the study area exhibit a clear climatic signal. The analysis of rainfall distribution in periods of recognised slope instability allows the distinction of three situations: 1) moderate intensity rainfall episodes, responsible for minor slope movements on the bank of rivers and shallow translational slides, particularly in artificial trenches; 2) high intensity rainfall episodes, originating flash floods and most landslides triggered by bank erosion; 3) long-lasting rainfall periods, responsible for the rise of the groundwater table and triggering of landslides with deeper slip surfaces.     

Relative effect-based landslide hazard zonation mapping in parts of Nilgiris, Tamil Nadu, South India

The Nilgiri massif, South India, is chronically prone to landslides due to deforestation and the resultant direct entry of rainwater and the final increases of pore pressure leading to landslides in the region. In order to understand such landslide causes, the relative effect method, a new technique, has been adopted for the study area. Among various methods, this is a statistical method developed within the framework of the Geographic Information System to map landslide hazard zones in a mountainous environment. To determine the relative effect (RE) of the factors influencing landslides, data layers of geology, land use/land cover, geomorphology, slope, lineament density, drainage density, and soil were analyzed by calculating the ratio of the unit portion in coverage and landslide, this function that is logarithmic. To quantify the magnitude of factors influencing each grid unit, REs were summed and classified into zones of low-, moderate-, and high-landslide hazard zones. It is also appropriate to follow suitable measures to prevent the landslides in the study area by involving all stockholders and with the active participation of local communities.     

Volume estimate of flow-type landslides along carbonatic and volcanic slopes in Campania (Southern Italy)

Recent studies on flow-type landslides in pyroclastic deposits have been performed to identify potential source areas and the main depositional mechanisms. Interesting methods for mapping landslide susceptibility have also been proposed. Since the potential volume of flow-type landslides is a measure of event magnitude, hence of considerable use in hazard assessment, we propose a method to estimate the potential volume for the morphometric analysis of 213 flow-like landslides occurred in Campania in recent centuries. First, our data show that the height, H, of the detachment and erosion-transport zones (i.e. the difference in height between the top of source area and a point, the first break at the foot of the slope, where the deposition stars to take place and the landslide loses velocity) and the area, A _f, of the same zones are linked by a mathematical function. Secondly, only part of the entire thickness of the pyroclastic material on the slope is involved. To define the potential volumes of the flow-type landslides, we analysed slopes, both in volcanic and carbonatic contexts, considering both channelled and unchannelled flow-type landslides. The most susceptible areas are identified by using a landslide-triggering susceptibility map, and then in each case the height H was estimated. This height is the difference in level between the point on the slope with highest susceptibility and the first break at the foot of the slope. Using the statistical correlation between H and A _f, both calculated for historical landslides, we evaluate the area of a potential landslide on a slope. Finally, potential volumes are calculated by using A _f and a constant thickness of the pyroclastic cover for the whole slope. This method could represent a useful tool to detect the main areas where risk mitigation works are required.     

Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi,Nepal

The 2015 Mw7.8 Gorkha earthquake triggered thousands of landslides of various types scattered over a large area. In the current study, we utilized pre- and post-earthquake high-resolution satellite imagery to compile two landslide inventories before and after earthquake and prepared three landslide susceptibility maps within 404 km² area using frequency ratio (FR) model. From the study, we could map about 519 landslides including 178 pre-earthquake slides and 341 coseismic slides were identified. This study investigated the relationship between landslide occurrence and landslide causative factors, i.e., slope, aspect, altitude, plan curvature, lithology, land use, distance from streams, distance from road, distance from faults, and peak ground acceleration. The analysis showed that the majority of landslides both pre-earthquake and coseismic occurred at slope >30°, preferably in S, SE, and SW directions and within altitude ranging from 1000 to 1500 m and 1500 to 3500 m. Scatter plots between number of landslides per km^?2 (LN) and percentage of landslide area (LA) and causative factors indicate that slope is the most influencing factor followed by lithology and PGA for the landslide formation. Higher landslide susceptibility before earthquake is observed along the road and rivers, whereas landslides after earthquake are triggered at steeper slopes and at higher altitudes. Combined susceptibility map indicates the effect of topography, geology, and land cover in the triggering of landslides in the entire basin. The resultant landslide susceptibility maps are verified through AUC showing success rates of 78, 81, and 77%, respectively. These susceptibility maps are helpful for engineers and planners for future development work in the landslide prone area.     

Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map

The main purpose of this study is to define the main variables that contribute to the occurrence of landslides in Kimi, Euboea, Greece, and to produce a landslide susceptibility map using the weight of evidence method. For the developed model, a sensitivity analysis is carried out in order to understand the model’s behavior when small changes are introduced in the weight value of the landslide-related variables. Landslide locations were identified from field surveys and interpretation of aerial photographs which resulted in the construction of an inventory map with 132 landslide events, while eight contributing variables were identified and exploited. All landslide-related variables were converted into a 5?×?5-m float-type raster file. These input-raster layers included a lithological unit layer, an elevation layer, a slope angle layer, a slope aspect layer, a distance from tectonic features layer, a distance from hydrographic network layer, a topographic wetness index layer, and a curvature layer. The validation of the developed model was achieved by using a subset of unprocessed landslide data, showing a satisfactory agreement between the expected and existing landslide susceptibility level, with the area under the predictive rate curve estimated to be 0.808. The area under the success rate curve was estimated to be 0.828 indicating a very high classification rate for existing landslide areas. According to the results of the sensitivity analysis, the lithological unit “yellowish gray to white marls” was the most sensitive as it had the highest change in the relative frequency of observed landslides. The overall outcomes of the performed analysis provide crucial knowledge in successful land use planning and management practice and also in risk reduction projects.     

Slope units-based flow susceptibility model: using validation tests to select controlling factors

A susceptibility map for an area, which is representative in terms of both geologic setting and slope instability phenomena of large sectors of the Sicilian Apennines, was produced using slope units and a multiparametric univariate model. The study area, extending for approximately 90 km², was partitioned into 774 slope units, whose expected landslide occurrence was estimated by averaging seven susceptibility values, determined for the selected controlling factors: lithology, mean slope gradient, stream power index at the foot, mean topographic wetness index and profile curvature, slope unit length, and altitude range. Each of the recognized 490 landslides was represented by its centroid point. On the basis of conditional analysis, the susceptibility function here adopted is the density of landslides, computed for each class. Univariate susceptibility models were prepared for each of the controlling factors, and their predictive performance was estimated by prediction rate curves and effectiveness ratio applied to the susceptibility classes. This procedure allowed us to discriminate between effective and non-effective factors, so that only the former was subsequently combined in a multiparametric model, which was used to produce the final susceptibility map. The validation of this map latter enabled us to verify the reliability and predictive performance of the model. Slope unit altitude range and length, lithology and, subordinately, stream power index at the foot of the slope unit demonstrated to be the main controlling factors of landslides, while mean slope gradient, profile curvature, and topographic wetness index gave unsatisfactory results.     

Controlling parameter analyses and hazard mapping for earthquake-triggered landslides: an example from a square region in Beichuan County, Sichuan Province, China

On May 12, 2008, at 1428 hours (Beijing time), a catastrophic earthquake, with a magnitude of Ms 8.0, struck the Sichuan Province, China. About 200,000 landslides, as a secondary geological hazard associated with the earthquake, were triggered over a broad area. These landslides were of almost all types such as shallow, disrupted landslides, rock falls, deep-seated landslides, and rock avalanches. Some of these landslides damaged and destroyed large part of some towns, blocked roads, dammed rivers, and caused other serious damages. The purpose of this study is to detect correlations between landslide occurrence and the surface rupture plane, ground shaking conditions (measured by peak ground acceleration, PGA), lithology, slope gradient, slope aspect, topographic position, and distance from drainages by using two indices, landslide area percentage (LAP) and the landslide number density (LND), based on geographic information system (GIS) technology and statistical analysis method in a square region (study area) of Beichuan County, Sichuan Province, China. There were 5,096 landslides related with the earthquake which were delineated by visual interpretation and selected field checking throughout the study area. The total area (horizontal projection) of the 5,096 landslides is about 41.103 km². The LAP, which is defined as the percentage of the plane area affected by landslides, was 10.276 %, and the LND, means the number of landslides per square kilometers, was 12.74 landslides/km². Statistical analysis results show that both LAP and LND have a positive correlation with slope gradient and a negative correlation with distance from the surface rupture. However, the correlation between the occurrence of landslides with PGA, topographic position, and distance from drainages are uncertain, or has just a little positive correlation. The correlation between landslide and slope aspect also shows the effect of the directivity of the seismic wave. The Zbq formation had the most concentrated landslide activity with the LND value of 21.78 landslides/km _, ² and the ∈₁ q Gr. geological units had the highest LAP value. Furthermore, weight index (W _i) model is performed with a GIS platform to derive landslide hazard index map. The success rate of the model was 71.615 % and, thus, it was valid. In addition, comparison of five landslide controlling parameters’ influence on landslide occurrences was also carried out.     

Application of an analytical hierarchical process approach for landslide susceptibility mapping in A Luoi district,Thua Thien Hue Province,Vietnam

A Luoi is a Vietnamese–Laotian border district situated in the western part of Thua Thien Hue province, central Vietnam, where landslides occur frequently and seriously affect local living conditions. This study focuses on the spatial analysis of landslide susceptibility in this 263-km² area. To analyze landslide manifestation in the study area, causative factor maps are derived of slope angle, weathering, land use, geomorphology, fault density, geology, drainage distance, elevation, and precipitation. The analytical hierarchical process approach is used to combine these maps for landslide susceptibility mapping. A landslide susceptibility zonation map with four landslide susceptibility classes, i.e. low, moderate, high, and very high susceptibility for landsliding, is derived based on the correspondence with an inventory of observed landslides. The final map indicates that about 37% of the area is very highly susceptible for landsliding and about 22% is highly susceptible, which means that more than half of the area should be considered prone to landsliding.     

A distributed hydrological–geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale

This paper describes the potential applicability of a hydrological–geotechnical modeling system using satellite-based rainfall estimates for a shallow landslide prediction system. The physically based distributed model has been developed by integrating a grid-based distributed kinematic wave rainfall-runoff model with an infinite slope stability approach. The model was forced by the satellite-based near real-time half-hourly CMORPH global rainfall product prepared by NOAA-CPC. The method combines the following two model outputs necessary for identifying where and when shallow landslides may potentially occur in the catchment: (1) the time-invariant spatial distribution of areas susceptible to slope instability map, for which the river catchment is divided into stability classes according to the critical relative soil saturation; this output is designed to portray the effect of quasi-static land surface variables and soil strength properties on slope instability and (2) a produced map linked with spatiotemporally varying hydrologic properties to provide a time-varying estimate of susceptibility to slope movement in response to rainfall. The proposed hydrological model predicts the dynamic of soil saturation in each grid element. The stored water in each grid element is then used for updating the relative soil saturation and analyzing the slope stability. A grid of slope is defined to be unstable when the relative soil saturation becomes higher than the critical level and is the basis for issuing a shallow landslide warning. The method was applied to past landslides in the upper Citarum River catchment (2,310 km²), Indonesia; the resulting time-invariant landslide susceptibility map shows good agreement with the spatial patterns of documented historical landslides (1985–2008). Application of the model to two recent shallow landslides shows that the model can successfully predict the effect of rainfall movement and intensity on the spatiotemporal dynamic of hydrological variables that trigger shallow landslides. Several hours before the landslides, the model predicted unstable conditions in some grids over and near the grids at which the actual shallow landslides occurred. Overall, the results demonstrate the potential applicability of the modeling system for shallow landslide disaster predictions and warnings.     

Landslides triggered by slipping-fault-generated earthquake on a plateau: an example of the 14 April 2010, Ms 7.1, Yushu, China earthquake

On 14 April 2010 at 07:49 (Beijing time), a catastrophic earthquake with Ms 7.1 struck Yushu County, Qinghai Province, China. A total of 2,036 landslides were interpreted from aerial photographs and satellite images, verified by selected field checking. These landslides cover about a total area of 1.194 km². The characteristics and failure mechanisms of these landslides are presented in this paper. The spatial distribution of the landslides is evidently strongly controlled by the locations of the main co-seismic surface fault ruptures. The landslides commonly occurred close together. Most of the landslides are small; there were only 275 individual landslide (13.5 % of the total number) surface areas larger than 1,000 m². The landslides are of various types. They are mainly shallow, disrupted landslides, but also include rock falls, deep-seated landslides, liquefaction-induced landslides, and compound landslides. Four types of factors are identified as contributing to failure along with the strong ground shaking: natural excavation of the toes of slopes, which mean erosion of the base of the slope, surface water infiltration into slopes, co-seismic fault slipping at landslide sites, and delayed occurrence of landslides due to snow melt or rainfall infiltration at sites where slopes were weakened by the co-seismic ground shaking. To analyze the spatial distribution of the landslides, the landslide area percentage (LAP) and landslide number density (LND) were compared with peak ground acceleration (PGA), distance from co-seismic main surface fault ruptures, elevation, slope gradient, slope aspect, and lithology. The results show landslide occurrence is strongly controlled by proximity to the main surface fault ruptures, with most landslides occurring within 2.5 km of such ruptures. There is no evident correlation between landslide occurrences and PGA. Both LAP and LND have strongly positive correlations with slope gradient, and additionally, sites at elevations between 3,800 and 4,000 m are relatively susceptible to landslide occurrence; as are slopes with northeast, east, and southeast slope aspects. Q₄ al-pl, N, and T₃ kn ¹ have more concentrated landslide activity than others. This paper provides a detailed inventory map of landslides triggered by the 2010 Yushu earthquake for future seismic landslide hazard analysis and also provides a study case of characteristics, failure mechanisms, and spatial distribution of landslides triggered by slipping-fault generated earthquake on a plateau.     

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.     

Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis

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.     

Evaluation and validation of landslide spatial susceptibility in the Western Ghats of Kerala, through GIS-based Weights of Evidence model and Area Under Curve technique

Landslide susceptibility mapping and spatial prediction have been carried out for the headwater region of Manimala river basin in the Western Ghats of Kerala, India, through geographic information technology and bayesian statistics, Weights of Evidence (WofE) model. The variables such as geomorphology, slope, relative relief, terrain curvature, slope length and steepness, soil type and land use/land cover are considered as factors that translate the terrain susceptible to landsliding. The quantitative relationship between landslides and the causative factors were statistically weighted using the ArcSDM extension of ArcGIS software. The posterior probability map, produced on the basis of predictive weights for each variable by combining the weighted layers in GIS, shows a high posterior probability value of 0.1 (highly possible) with a standard deviation of 0.0025. The discrete susceptibility classes in the reclassified posterior probability map reveals that the high and moderate landslide susceptibility classes cover 0.78 and 14.93% respectively of the total study area. The result was validated using the Area Under Curve (AUC) method with a separate set of landslide locations and the validation demonstrates high prediction accuracy with a prediction rate of 81.32%.     

基于确定性系数的降雨型滑坡影响因子敏感性分析

本文选择东南沿海地区具有典型降雨型滑坡的淳安县作为研究区,在完成全县地质灾害详细调查的基础上,选取高程、坡度、坡向、曲率、工程地质岩组、距断层距离、距道路距离、土地利用和植被等9个滑坡影响因子,利用GIS技术与确定性系数分析方法,对这9个影响因子开展敏感性分析。研究结果表明:(1) 寒武、震旦、石炭和白垩系是滑坡易发地层,侵入岩组、紫红色砂岩、碳酸盐岩夹碎屑岩、碳酸盐岩为主的岩组是滑坡高敏感性岩组;滑坡受断层影响总体上随着距离断层由近及远逐渐降低;(2) 坡度范围10°~35°是滑坡的易发坡度,30°~35°滑坡数量达到峰值;SE和S等朝南坡向是滑坡最易发坡向;高程范围为100~200m是滑坡最易发区间;凹坡最易发生滑坡,而凸坡则滑坡敏感性最差;非林地、茶叶、竹林和经济林等是滑坡高敏感植被类型;(3) 住宅用地、耕地、园地等与人类活动密切相关的用地类型是滑坡易发地类;距道路距离因子对滑坡敏感性低,相关性不明显。上述各滑坡影响因子最利于滑坡发生的数值区间确定,将为研究区进一步开展降雨型滑坡区域易发性评价及预测奠定基础。     

Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS

The purpose of this study is to assess the susceptibility of landslides in parts of Western Ghats, Kerala, India, using a geographical information system (GIS). Landslide inventory of the area was made by detailed field surveys and the analysis of the topographical maps. The landslide triggering factors are considered to be slope angle, slope aspect, slope curvature, slope length, distance from drainage, distance from lineaments, lithology, land use and geomorphology. ArcGIS version 8.3 was used to manipulate and analyse all the collected data. Probabilistic-likelihood ratio was used to create a landslide susceptibility map for the study area. The result was validated using the Area under Curve (AUC) method and temporal data of landslide occurrences. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations. As the result, the success rate of the model was (84.46%) and the prediction rate of the model was (82.38%) shows high prediction accuracy. In the reclassified final landslide susceptibility zone map, 5.68% of the total area is classified as critical in nature. The landslide susceptibility map thus produced can be used to reduce hazards associated with landslides and to land cover planning.     

Analysis of a spatial distribution of landslides triggered by the 2004 Chuetsu earthquakes of Niigata Prefecture, Japan

On October 23, 2004, a series of powerful earthquakes with a maximum M _w = 6.6 located near the western coast of northern Honshu struck parts of northern Japan, particularly Niigata Prefecture; these earthquakes were known as the Chuetsu event. Thousands of landslides, as a secondary geotechnical hazard associated with these earthquakes, were triggered over a broad area; these landslides were of almost all types. The purpose of this study was to detect correlations between landslide occurrence with geologic and geomorphologic conditions, slope geometry, and earthquake parameters using two indexes based on Geographic Information Systems (GIS). In the study area, the landslide–area ratio (LAR), which is defined as the percentage of the area affected by landslides, was 2.9%, and the landslide concentration (LC), the number of landslides per square kilometer, was 4.4 landslides/km², which is much more than other reported cases of seismic activity with the same magnitude. This was possibly due to heavy rainfall just before the Chuetsu earthquakes. Statistical analyses show that LAR has a positive correlation with slope steepness and distance from the epicenter, while LC is inversely correlated with distance from the epicenter. The Wanazu Formation had the most concentrated landslide activity, followed by the Kawaguchi, Ushigakubi, Shiroiwa and Oyama Formations, although the Wanazu Formation occupied only 4.5% of the total area of geological units. With 8.2% of the area affected by seismic landslides, the Kawaguchi Formation had the highest LAR. It was followed by the Shiroiwa, Ushigakubi and Wanazu Formations with LAR ranging from 4.6% to 6.0%. For lots of geological subunits, landslides are more frequent in a range of slope angles between 15° and 40°. The susceptibility to landsliding of each geologic unit was thus evaluated to correlate with slope steepness. It was also noted that the effects of the earthquakes were made far worse by antecedent rainfall conditions induced by a␣typhoon, and further research emphasizing the role of antecedent rainfall was discussed.     

Application of frequency ratio,statistical index,and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.     

Landslide Susceptibility Mapping Using GIS-based Fuzzy Logic and the Analytical Hierarchical Processes Approach: A Case Study in Constantine (North-East Algeria)

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.