Landslide susceptibility mapping by frequency ratio and logistic regression methods: an example from Niksar–Resadiye (Tokat,Turkey)

Turkey confronts loss of life and large economic losses due to natural disasters caused by its morphologic structure, geographical placement, and climate characteristics. The Kuzulu (Koyulhisar) landslide, which caused loss of life and property on 17th March 2005, occurred in an area near the country’s most important active fault, the North Anatolian Fault Zone. To mitigate and prevent landslide damages, prediction of landslide susceptibility areas based on probabilistic methods has a great importance. The purpose of this study was to produce a landslide susceptibility map by the logistic regression and frequency ratio methodologies for a 733-km² area near the North Anatolian Fault Zone from the southeast of Niksar to Resadiye in Tokat province. Conditioning parameters, such as elevation, slope gradient, slope aspect, distance to streams, roads, and faults, drainage density, and fault density, were used in the analysis. Before susceptibility analysis, the landslides observed in the area were separated into two groups for use in analysis and verification, respectively. The susceptibility maps produced had five different susceptibility classes such as very low, low, moderate, high, and very high. To test the performance of the susceptibility maps, area under curve (AUC) approach was used. For the logistic regression method, the AUC value was 0.708; while for the frequency rate method, this value was 0.744. According to these AUC values, it could be concluded that the two landslide susceptibility maps obtained were successful.     

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.     

A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models

The purpose of this study is to produce landslide susceptibility map of a landslide-prone area (Daguan County, China) by evidential belief function (EBF) model and weights of evidence (WoE) model to compare the results obtained. For this purpose, a landslide inventory map was constructed mainly based on earlier reports and aerial photographs, as well as, by carrying out field surveys. A total of 194 landslides were mapped. Then, the landslide inventory was randomly split into a training dataset; 70% (136 landslides) for training the models and the remaining 30% (58 landslides) was used for validation purpose. Then, a total number of 14 conditioning factors, such as slope angle, slope aspect, general curvature, plan curvature, profile curvature, altitude, distance from rivers, distance from roads, distance from faults, lithology, normalized difference vegetation index (NDVI), sediment transport index (STI), stream power index (SPI), and topographic wetness index (TWI) were used in the analysis. Subsequently, landslide susceptibility maps were produced using the EBF and WoE models. Finally, the validation of landslide susceptibility map was accomplished with the area under the curve (AUC) method. The success rate curve showed that the area under the curve for EBF and WoE models were of 80.19% and 80.75% accuracy, respectively. Similarly, the validation result showed that the susceptibility map using EBF model has the prediction accuracy of 80.09%, while for WoE model, it was 79.79%. The results of this study showed that both landslide susceptibility maps obtained were successful and would be useful for regional spatial planning as well as for land cover planning.     

Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji,China

The main objective of this study was to apply a statistical (information value) model using geographic information system (GIS) to the Chencang District of Baoji, China. Landslide locations within the study area were identified using reports and aerial photographs, and a field survey. A total of 120 landslides were mapped, of which 84 (70 %) were randomly selected for building the landslide susceptibility model. The remaining 36 (30 %) were used for model validation. We considered a total of 10 potential factors that predispose an area to a landslide for the landslide susceptibility mapping. These included slope degree, altitude, slope aspect, plan curvature, geomorphology, distance from faults, lithology, land use, mean annual rainfall, and peak ground acceleration. Following an analysis of these factors, a landslide susceptibility map was produced using the information value model with GIS. The resulting landslide susceptibility index was divided into five classes (very high, high, moderate, low, and very low) using the natural breaks method. The corresponding distribution area percentages were 29.22, 25.14, 15.66, 15.60, and 14.38 %, respectively. Finally, landslide locations were used to validate the results of the landslide susceptibility map using areas under the curve (AUC). The AUC plot showed that the susceptibility map had a success rate of 81.79 % and a prediction accuracy of 82.95 %. Based on the results of the AUC evaluation, the landslide susceptibility map produced using the information value model exhibited good performance.     

The 17 March 2005 Kuzulu landslide (Sivas, Turkey) and landslide-susceptibility map of its near vicinity

Landslides are common natural hazards in the seismically active North Anatolian Fault Zone of Turkey. Although seismic activity, heavy rainfall, channel incisions, and anthropogenic effects are commonly the main triggers of landslides, on March 17, 2005, a catastrophic large landslide in Sivas, northeastern of Turkey, the Kuzulu landslide, was triggered by snowmelt without any other precursor. The initial failure of the Kuzulu landslide was rotational. Following the rotational failure, the earth material in the zone of accumulation exhibited an extremely rapid flow caused by steep gradient and high water content. The Agnus Creek valley, where Kuzulu village is located, was filled by the earth-flow material and a landslide dam was formed on the upper part of Agnus Creek. The distance from the toe of the rotational failure down to the toe of the earth flow measured more than 1800 m, with about 12.5 million m³ of displaced earth material. The velocity of the Kuzulu landslide was extremely fast, approximately 6 m/s. The main purposes of this study are to describe the mechanism and the factors conditioning the Kuzulu landslide, to present its environmental impacts, and to produce landslide-susceptibility maps of the Kuzulu landslide area and its near vicinity. For this purpose, a detailed landslide inventory map was prepared and geology, slope, aspect, elevation, topographic-wetness index and stream-power index were considered as conditioning factors. During the susceptibility analyses, the conditional probability approach was used and a landslide-susceptibility map was produced. The landslide-susceptibility map will help decision makers in site selection and the site-planning process. The map may also be accepted as a basis for landslide risk-management studies to be applied in the study area.     

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.     

基于证据权模型的川西鲜水河断裂带滑坡易发性评价

鲜水河断裂带是发育于青藏高原东缘的一条大型左旋走滑断裂带,该区新构造活动强烈且历史强震频发,一系列大型-巨型滑坡沿断裂带密集分布。在资料收集的基础上,对鲜水河断裂带两侧10 km区域内进行遥感解译和野外地质调查,建立数据库并对滑坡主要影响因素进行分析。在滑坡区域发育分布规律分析的基础上,选取地形坡度、地形坡向、地面高程、平面曲率、地形湿度指数、活动断裂、工程地质岩组、年降雨量、河流、道路、植被覆盖指数等11个因素作为滑坡易发性评价因子,在ArcGIS软件平台上,采用证据权模型开展了滑坡易发性评价。根据成功率曲线对评价结果的检验,滑坡易发性评价结果具有较好的精度,并将研究区的滑坡易发程度划分为极高易发、高易发、中等易发、低易发和不易发5个级别。滑坡的易发性受鲜水河断裂带影响显著,极高易发区和高易发区主要分布在东谷到道孚县沿鲜水河断裂带两侧,以及康定县城和磨西镇附近;中等易发区主要分布在鲜水河支流两岸及省道沿线;滑坡低易发区和不易发区主要分布在人类工程活动少的高山地带以及地形相对平缓的区域。滑坡易发性评价结果很好地反映了鲜水河断裂带区域内滑坡发育分布现状,为该区重大工程规划建设和防灾减灾提供参考依据。     

Landslide susceptibility mapping for a landslide-prone area (Findikli,NE of Turkey) by likelihood-frequency ratio and weighted linear combination models

Landslides are very common natural problems in the Black Sea Region of Turkey due to the steep topography, improper use of land cover and adverse climatic conditions for landslides. In the western part of region, many studies have been carried out especially in the last decade for landslide susceptibility mapping using different evaluation methods such as deterministic approach, landslide distribution, qualitative, statistical and distribution-free analyses. The purpose of this study is to produce landslide susceptibility maps of a landslide-prone area (Findikli district, Rize) located at the eastern part of the Black Sea Region of Turkey by likelihood frequency ratio (LRM) model and weighted linear combination (WLC) model and to compare the results obtained. For this purpose, landslide inventory map of the area were prepared for the years of 1983 and 1995 by detailed field surveys and aerial-photography studies. Slope angle, slope aspect, lithology, distance from drainage lines, distance from roads and the land-cover of the study area are considered as the landslide-conditioning parameters. The differences between the susceptibility maps derived by the LRM and the WLC models are relatively minor when broad-based classifications are taken into account. However, the WLC map showed more details but the other map produced by LRM model produced weak results. The reason for this result is considered to be the fact that the majority of pixels in the LRM map have high values than the WLC-derived susceptibility map. In order to validate the two susceptibility maps, both of them were compared with the landslide inventory map. Although the landslides do not exist in the very high susceptibility class of the both maps, 79% of the landslides fall into the high and very high susceptibility zones of the WLC map while this is 49% for the LRM map. This shows that the WLC model exhibited higher performance than the LRM model.     

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.

     

Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand

The purpose of this study is to produce a landslide susceptibility map for the lower Mae Chaem watershed, northern Thailand using a Geographic Information System (GIS) and remotely sensed images. For this purpose, past landslide locations were identified from satellite images and aerial photographs accompanied by the field surveys to create a landslide inventory map. Ten landslide-inducing factors were used in the susceptibility analysis: elevation, slope angle, slope aspect, lithology, distance from lineament, distance from drainage, precipitation, soil texture, land use/land cover (LULC), and NDVI. The first eight factors were prepared from their associated database while LULC and NDVI maps were generated from Landsat-5 TM images. Landslide susceptibility was analyzed and mapped using the frequency ratio (FR) model that determines the level of correlation between locations of past landslides and the chosen factors and describes it in terms of frequency ratio index. Finally, the output map was validated using the area under the curve (AUC) method where the success rate of 80.06% and the prediction rate of 84.82% were achieved. The obtained map can be used to reduce landslide hazard and assist with proper planning of LULC in the future.     

Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models,Sarkhoun basin,Southwestern Iran

Landslides are natural disasters often activated by interaction of different controlling environmental factors, especially in mountainous terrains. In this research, the landslide susceptibility map was developed for the Sarkhoun catchment using Index of Entropy (IoE) and Dempster–Shafer (DS) models. For this purpose, 344 landslides were mapped in GIS environment. 241 (70%) out of the landslides were selected for the modeling and the remaining (30%) were employed for validation of the models. Afterward, 10 landslide conditioning factor layers were prepared including land use, distance to drainage, slope gradient, altitude, lithology, distance to roads, distance to faults, slope aspect, Topography Wetness Index, and Stream Power Index. The relationship between the landslide conditioning factors and landslide inventory maps was determined using the IoE and DS models. In order to verify the models, the results were compared with validation landslide data not employed in training process of the models. Accordingly, Receiver Operating Characteristic (ROC) curves were applied, and Area Under the Curve (AUC) was calculated for the obtained susceptibility maps using the success (training data) and prediction (validation data) rate curves. The land use was found to be the most important factor in the study area. The AUC are 0.82, and 0.81 for success rates of the IoE, and DS models, respectively, while the prediction rates are 0.76 and 0.75. Therefore, the results of the IoE model are more accurate than the DS model. Furthermore, a satisfactory agreement is observed between the generated susceptibility maps by the models and true location of the landslides.     

Structural and geomorphological aspects of the Kat landslides (Tokat—Turkey) and susceptibility mapping by means of GIS

Kat County, which is located in a slope of hilly region and constructed in the side of a mountain along the North Anatolian Fault Zone, is frequently subject to landslides. The slides occur during periods of heavy rainfall, and these events cause destruction to property, roads, agricultural lands and buildings. In the last few decades, a lot of houses and buildings have been damaged and destroyed. Settlement areas have remained evacuated for a long time. The slope instabilities in the study area are a complex landslide extending from north to south containing a lot of landslides. Field investigations, interpretation of aerial photography, analyses of geological data and laboratory tests suggest that some factors have acted together on the slopes to cause the sliding. In the wet season, the slopes became saturated. As the saturation of the earth material on the slope causesa rise in water pressure, the shear strength (resisting forces) decreases and the weight (driving forces) increases; thus, the net effect was to lower the safety factor. Previous failures have affected the rock mass, leading to the presence of already sheared surfaces at residual strengths. The relation between the joint planes and the instability of the slope in the study area was discussed and it was found that the potential slope instabilities are mainly in the directions of NW–SE, NE–SW and N–S. The landslide susceptibility map obtained by using the geographical information system showed that a large area is susceptible and prone to landslides in the northern part of the study area.An erratum to this article can be found at     

Landslide susceptibility mapping using Bayesian approach in the Sultan Mountains (Akşehir,Turkey)

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.     

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.     

基于随机森林-特征递归消除模型的可解释性缓丘岭谷地貌滑坡易发性评价

研究旨在基于随机森林-特征递归消除模型,通过SHAP算法(SHapley Additive exPlanation, SHAP)与部分依赖图(Partial Dependence Plot, PDP)对缓丘岭谷地貌区域进行滑坡易发性评价与内部机制解释,以期为地质灾害防治研究提供参考。利用优化随机森林算法对典型缓丘岭谷地区滑坡易发性进行研究,建立缓丘岭谷滑坡易发性评价模型;利用特征递归消除算法剔除噪声因子,选取地形地貌、地质构造、环境条件、人类活动5个类型16个因子构建重庆合川区滑坡致灾因子数据库;结合合川区754个历史滑坡点,利用随机森林算法对因子重要性进行排序,并根据专家经验法对研究区的滑坡易发性进行划分,将研究区的滑坡易发性分为极低、低、中、高、极高5个等级;应用部分依赖图对合川区滑坡发生影响大的因子进行解释和SHAP算法对个体滑坡进行局部解释。结果表明：与原模型相比,随机森林-特征递归消除模型测试集AUC值提高了0.019,证明了特征递归消除算法的有效性;训练集以及测试集的AUC值分别为0.769、0.755,具有较高的预测精度;缓丘缓坡地区在起伏较大地区滑坡密度较大,历史滑坡多集中于高易发地区;滑坡的空间分布具有不均匀性与复杂性,各致灾因子对滑坡发生的影响有着明显的区域特征与空间异质性,在缓坡丘陵地区多年平均降雨、高程、岩性3个因子对滑坡发生的影响最大;由SHAP算法对合川白塔坪上山公路滑坡事件进行解释,岩性与高程对滑坡起抑制作用,起伏度、坡度、归一化植被指数(NDVI)与POI核密度促进滑坡发生。综上所述,基于随机森林-特征递归消除模型在缓丘岭谷区滑坡易发性评价中具有较高的准确性,通过部分依赖图与SHAP算法对全局滑坡与个体滑坡发生的内在机理进行解释分析,有利于构建与完善不同地貌环境下滑坡易发性评价因子体系并探究滑坡内部决策机理,可为区域滑坡易发性评估与地质灾害防治提供参考。     

A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey)

This study aimed to investigate the parameter effects in preparing landslide susceptibility maps with a data-driven approach and to adapt this approach to analytical hierarchy process (AHP). For this purpose, at the first stage, landslide inventory of an area located in the Western Black Sea region of Turkey covering approximately 567?km² was prepared, and a total of 101 landslides were mapped. In order to assess the landslide susceptibility, a total of 13 parameters were considered as the input parameters: slope, aspect, plan curvature, topographical elevation, vegetation cover index, land use, distance to drainage, distance to roads, distance to structural elements, distance to ridges, stream power index, sediment transport capacity index, and wetness index. AHP was selected as the major assessment methodology since the adapted approach and AHP work in data pairs. Adapted to AHP, a similarity relation?Cbased approach, namely landslide relation indicator (LRI) for parameter selection method, was also proposed. AHP and parametric effect analyses were performed by the proposed approach, and seven landslide susceptibility maps were produced. Among these maps, the best performance was gathered from the landslide susceptibility map produced by 9 parameter combinations using area under curve (AUC) approach. For this map, the AUC value was calculated as 0.797, while the others ranged between 0.686 and 0.771. According to this map, 38.3?% of the study area was classified as having very low, 8.5?% as low, 15.0?% as moderate, 20.3?% as high, and 17.9?% as very high landslide susceptibility, respectively. Based on the overall assessments, the proposed approach in this study was concluded as objective and applicable and yielded reasonable results.     

Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran

Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.     

Landslide susceptibility assessment in the İzmir (West Anatolia,Turkey) city center and its near vicinity by the logistic regression method

A landslide susceptibility assessment for İzmir city (Western Turkey), which is the third biggest city of Turkey, was performed by a logistic regression method. A database of landslide characteristics was prepared using detailed field surveys. The major landslides in the study area are generally observed in the field, dominated by weathered volcanics, and 39.63% of the total landslide area is in this unit. The parameters of lithology, slope gradient, slope aspect, distance to drainage, distance to roads and distance to fault lines were used as variables in the logistic regression analysis. The effect of each parameter on landslide occurrence was assessed from the corresponding coefficients that appear in the logistic regression function. On the basis of the obtained coefficients, lithology plays the most important role in determining landslide occurrence and distribution. Slope gradient has a more significant effect than the other geomorphological parameters, such as slope aspect and distance to drainage. Using a predicted map of probability, the study area was classified into five categories of landslide susceptibility: very low, low, moderate, high and very high. Whereas 49.65% of the total study area has very low susceptibility, very high susceptibility zones make up 11.69% of the area.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.     

基于逻辑回归–信息量的川藏交通廊道滑坡易发性评价

川藏交通廊道位于青藏高原中东部,是世界上隆升和地貌演化最快的区域之一。在内外动力耦合作用下,区内滑坡灾害极其发育,严重制约着公路、铁路和水电工程的规划建设。在区域地质资料收集和整理的基础上,选取岩性、坡度、坡向、坡形、地形起伏度、地形粗糙度、断裂密度和河流距离8个因素为评价因子,结合传统信息量和逻辑回归模型的优势,采用逻辑回归–信息量模型对研究区滑坡进行易发性评价。通过对评价因子的多重共线性和显著性检验,得到评价因子不存在多重共线性且均对滑坡发生具有显著影响。采用ROC曲线对评价结果进行检验,其AUC值为0.81,表明评价模型能很好地预测滑坡的发生。易发性评价结果表明:研究区高易发区主要集中龙门山断裂带、金沙江断裂带、澜沧江断裂带、怒江断裂带、边坝–洛隆断裂带等大型活动断裂带控制区,以及区内坡度陡峭、地形起伏度大的大型河流深切河谷的两岸;中易发区在区内分布广泛,主要分布在岸坡较陡、地形起伏度中等的大型河流支流的两岸。研究结果有利于加深对川藏交通廊道滑坡发育分布的认识,也可为研究区的工程规划建设和防灾减灾提供科学依据。