GIS-based landslide susceptibility evaluation using fuzzy-AHP multi-criteria decision-making techniques in the Abha Watershed,Saudi Arabia

Landslides are natural geological disasters causing massive destructions and loss of lives, as well as severe damage to natural resources, so it is essential to delineate the area that probably will be affected by landslides. Landslide susceptibility mapping (LSM) is making increasing implications for GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. It is considered to be an effective tool to understand natural disasters related to mass movements and carry out an appropriate risk assessment. This study is based on an integrated approach of GIS and statistical modelling including fuzzy analytical hierarchy process (FAHP), weighted linear combination and MCE models. In the modelling process, eleven causative factors include slope aspect, slope, rainfall, geology, geomorphology, distance from lineament, distance from drainage networks, distance from the road, land use/land cover, soil erodibility and vegetation proportion were identified for landslide susceptibility mapping. These factors were identified based on the (1) literature review, (2) the expert knowledge, (3) field observation, (4) geophysical investigation, and (5) multivariate techniques. Initially, analytical hierarchy process linked with the fuzzy set theory is used in pairwise comparisons of LSM criteria for ranking purposes. Thereafter, fuzzy membership functions were carried out to determine the criteria weights used in the development of a landslide susceptibility map. These selected thematic maps were integrated using a weighted linear combination method to create the final landslide susceptibility map. Finally, a validation of the results was carried out using a sensitivity analysis based on receiver operator curves and an overlay method using the landslide inventory map. The study results show that the weighted overlay analysis method using the FAHP and eigenvector method is a reliable technique to map landslide susceptibility areas. The landslide susceptibility areas were classified into five categories, viz. very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility, and very high susceptibility. The very high and high susceptibility zones account for 15.11% area coverage. The results are useful to get an impression of the sustainability of the watershed in terms of landsliding and therefore may help decision makers in future planning and mitigation of landslide impacts.     

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.     

A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses

Bivariate and multivariate statistical analyses were used to predict the spatial distribution of landslides in the Cuyahoga River watershed, northeastern Ohio, U.S.A. The relationship between landslides and various instability factors contributing to their occurrence was evaluated using a Geographic Information System (GIS) based investigation. A landslide inventory map was prepared using landslide locations identified from aerial photographs, field checks, and existing literature. Instability factors such as slope angle, soil type, soil erodibility, soil liquidity index, landcover pattern, precipitation, and proximity to stream, responsible for the occurrence of landslides, were imported as raster data layers in ArcGIS, and ranked using a numerical scale corresponding to the physical conditions of the region. In order to investigate the role of each instability factor in controlling the spatial distribution of landslides, both bivariate and multivariate models were used to analyze the digital dataset. The logistic regression approach was used in the multivariate model analysis. Both models helped produce landslide susceptibility maps and the suitability of each model was evaluated by the area under the curve method, and by comparing the maps with the known landslide locations. The multivariate logistic regression model was found to be the better model in predicting landslide susceptibility of this area. The logistic regression model produced a landslide susceptibility map at a scale of 1:24,000 that classified susceptibility into four categories: low, moderate, high, and very high. The results also indicated that slope angle, proximity to stream, soil erodibility, and soil type were statistically significant in controlling the slope movement.     

Application of logistic regression model for slope instability prediction in Cuyahoga River Watershed,Ohio, USA

High incidences of slope movement are observed throughout Cuyahoga River watershed in northeast Ohio, USA. The major type of slope failure involves rotational movement in steep stream walls where erosion of the banks creates over-steepened slopes. The occurrence of landslides in the area depends on a complex interaction of natural as well as human induced factors, including: rock and soil strength, slope geometry, permeability, precipitation, presence of old landslides, proximity to streams and flood-prone areas, land use patterns, excavation of lower slopes and/or increasing the load on upper slopes, alteration of surface and subsurface drainage. These factors were used to evaluate the landslide-induced hazard in Cuyahoga River watershed using logistic regression analysis, and a landslide susceptibility map was produced in ArcGIS. The map classified land into four categories of landslide susceptibility: low, moderate, high, and very high. The susceptibility map was validated using known landslide locations within the watershed area. The landslide susceptibility map produced by the logistic regression model can be efficiently used to monitor potential landslide-related problems, and, in turn, can help to reduce hazards associated with landslides.     

Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment

The objective of this paper is to evaluate the importance of geomorphological expert knowledge in the generation of landslide susceptibility maps, using GIS supported indirect bivariate statistical analysis. For a test area in the Alpago region in Italy a dataset was generated at scale 1:5,000. Detailed geomorphological maps were generated, with legends at different levels of complexity. Other factor maps, that were considered relevant for the assessment of landslide susceptibility, were also collected, such as lithology, structural geology, surficial materials, slope classes, land use, distance from streams, roads and houses. The weights of evidence method was used to generate statistically derived weights for all classes of the factor maps. On the basis of these weights, the most relevant maps were selected for the combination into landslide susceptibility maps. Six different combinations of factor maps were evaluated, with varying geomorphological input. Success rates were used to classify the weight maps into three qualitative landslide susceptibility classes. The resulting six maps were compared with a direct susceptibility map, which was made by direct assignment of susceptibility classes in the field. The analysis indicated that the use of detailed geomorphological information in the bivariate statistical analysis raised the overall accuracy of the final susceptibility map considerably. However, even with the use of a detailed geomorphological factor map, the difference with the separately prepared direct susceptibility map is still significant, due to the generalisations that are inherent to the bivariate statistical analysis technique.     

Regional landslide susceptibility following the Mid NIIGATA prefecture earthquake in 2004 with NEWMARK’S sliding block analysis

This study proposes a calculation method for regional earthquake-induced landslide susceptibility that applies the permanent seismic displacement calculated using Newmark’s sliding block analysis with estimated vertical and horizontal seismic motions. The proposed method takes into account the direction of slope failure based on the specified slope azimuth. The study results reveal the importance of predominant slope failure direction using a simple infinite slope model subjected to earthquakes. The target area for the earthquake-induced landslide susceptibility analysis constituted a region of more than 2000 km² surrounding the epicenter of the Mid Niigata prefecture earthquake in 2004. An earthquake-induced landslide susceptibility map was created based on the proposed method with a specific combination of friction angle and cohesion, and the resulting data were compared to the landslide inventory map produced from aerial photographs following the Mid Niigata prefecture earthquake in 2004. To create the susceptibility map, geomaterial cohesion values for the slope were back-calculated to satisfy the minimum safety factor in the static state. This study also proposes a calculation method for the prediction rate and determines the back-calculated strength parameters of geomaterials. The proposed regional landslide susceptibility map will be useful for understanding potential slope failure locations and magnitude of damage, as well as for planning field investigation and preventing secondary disasters immediately after earthquakes.     

GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline,Hendek (Turkey)

A segment of natural gas pipeline was damaged due to landsliding near Hendek. Re-routing of the pipeline is planned, but it requires the preparation of a landslide susceptibility map. In this study, the statistical index (Wi) and weighting factor (Wf) methods have been used with GIS to prepare a landslide susceptibility map of the problematic segment of the pipeline. For this purpose, thematic layers including landslide inventory, lithology, slope, aspect, elevation, land use/land cover, distance to stream, and drainage density were used. In the study area, landslides occur in the unconsolidated to semi-consolidated clayey unit and regolith. The Wf method gives better results than the Wi method. Lithology is found to be the most important aspect in the study area. Based on the findings obtained in this study, the unconsolidated to semi-consolidated clayey unit and alluvium should be avoided during re-routing. Agricultural activities should not be allowed in the close vicinity of the pipeline.     

Integration of statistical and heuristic approaches for landslide risk analysis: a case of volcanic mountains in West Java Province,Indonesia

Owing to fragile geo-morphology, extreme climatic conditions, and densely populated settlements and rapid development activities, West Java Province is the most landslide hazardous area in Indonesia. So, a landslide risk map for this province bears a great importance such as for land-use planning. It is however widely accepted that landslide risk analysis is often difficult because of the difficulties involved in landslide hazard assessment and estimation of consequences of future landslide events. For instance, lack of multi-temporal inventory map or records of triggering events is often a major problem in landslide hazard mapping. In this study, we propose a simple technique for converting a landslide susceptibility map into a landslide hazard map, which we have employed for landslide risk analysis in one ideally hazardous part of volcanic mountains in West Java Province. The susceptibility analysis was carried out through correlation between past landslides and eight spatial parameters related to instability, i.e. slope, aspect, relative relief, distance to river, geological units, soil type, land use and distance to road. The obtained susceptibility map was validated using cross-time technique, and was collaborated with the frequency-area statistics to respond to ‘when landslide will occur’ and ‘how large it will be’. As for the judgment of the consequences of future landslides, expert opinion was used considering available literature and characteristic of the study area. We have only considered economic loss in terms of physical damage of buildings, roads and agricultural lands for the landslide risk analysis. From this study, we understand the following: (1) the hazard map obtained from conversion of the susceptibility map gives spatial probability and the area of an expected landslide will be greater than 500m² in the next 2 years, (2) the landslide risk map shows that 24% of the total area is in high risk; 30% in moderate risk; 45% in low risk and no risk covers only 1% of the total area, and (3) the loss will be high in agricultural lands, while it will be low in the road structures and buildings.     

Rainfall event-based landslide susceptibility zonation mapping

Landslide susceptibility assessment is a major research topic in geo-disaster management. In recent days, various landslide susceptibility and landslide hazard assessment methodologies have been introduced with diverse thoughts of assessment and validation method. Fundamentally, in landslide susceptibility zonation mapping, the susceptibility predictions are generally made in terms of likelihoods and probabilities. An overview of landslide susceptibility zoning practices in the last few years reveals that susceptibility maps have been prepared to have different accuracies and reliabilities. To address this issue, the work in this paper focuses on extreme event-based landslide susceptibility zonation mapping and its evaluation. An ideal terrain of northern Shikoku, Japan, was selected in this study for modeling and event-based landslide susceptibility mapping. Both bivariate and multivariate approaches were considered for the zonation mapping. Two event-based landslide databases were used for the susceptibility analysis, while a relatively new third event landslide database was used in validation. Different event-based susceptibility zonation maps were merged and rectified to prepare a final susceptibility zonation map, which was found to have an accuracy of more than 77 %. The multivariate approach was ascertained to yield a better prediction rate. From this study, it is understood that rectification of susceptibility zonation map is appropriate and reliable when multiple event-based landslide database is available for the same area. The analytical results lead to a significant understanding of improvement in bivariate and multivariate approaches as well as the success rate and prediction rate of the susceptibility maps.     

The analysis of rainfall-induced slope failures at Iva Valley area of Enugu State,Nigeria

This paper analyses the dominant mechanisms of slope failures and identifies potential obstacles to landslide-hazard reduction at the Iva Valley area, Enugu, Nigeria. The landscape is replete with landslide scars and gullies of varied sizes and the slope deposits comprise unconsolidated, friable sands inter-bedded with thin units of montmorillonitic claystone. Forty-three landslide events were identified in the study area with most being shallow, short run-out movements with slip-surface depth <2 m. The study found the landslides mainly occur in the beginning of rainy season characterized by short duration, high intensity rainfall. An integrated approach comprising field mapping, laboratory tests and numerical analyses reveals that the barren nature of the slopes prior to the outset of rainy season, high rainfall intensity, erosion, overgrazing, soil characteristics and the site’s unique lithologic sequence are the main causes of instability. Shearing tests under several conditions showed that the soils strongly strain-soften until low steady-state strength is achieved. A computer code, based on this strength reduction technique, used input parameters obtained from the field and laboratory studies to simulate a landslide with similar structure, travel distance and distribution area. It is noted that urbanization has gradually increased the vulnerability of the society’s poor to landslide hazards as they now erect unplanned residence (tents and blocks) on the slopes. This work is part of a regional study aimed at finding ways of protecting the vulnerable by generating data that could be used to build future landslide susceptibility map.     

Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County,Three Gorges Reservoir,China

Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors (“cases”). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model’s results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior.

     

川藏交通廊道黏土化蚀变岩发育特征及其对大型滑坡的促滑作用

黏土化蚀变岩是在岩浆期后热液作用或后期水热作用下形成的具有不良工程地质性质的特殊地质体.在野外调查、黏土矿物测试和物理力学试验分析的基础上,阐述了川藏交通廊道黏土化蚀变岩的形成条件、区域分布特征、地质特征及蚀变程度的判据,以白格滑坡为例揭示了黏土化蚀变岩对大型滑坡的促滑作用.结果表明,黏土化蚀变岩的区域分布受活动断裂、热液作用和地层岩性控制,常沿侵入岩脉、断裂带、岩浆岩节理密集带、侵入岩体与其他岩层的接触带等部位发育,其蚀变程度按蚀变系数可划分为微蚀变、弱蚀变、中等蚀变和强蚀变4个等级,在干湿交替和松弛条件下极易发生崩解、软化,中等-强蚀变的岩体抗剪强度低.黏土化蚀变岩对滑坡的促滑作用主要体现在强度弱化效应、岩体结构劣化效应及失稳滞后效应3个方面,是促进构造混杂岩带深切河谷斜坡失稳、形成大型滑坡不可忽视的重要因素.      

Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey)

Ardesen is a settlement area which has been significantly damaged by frequent landslides which are caused by severe rainfalls and result in many casualties. In this study a landslide susceptibility map of Ardesen was prepared using the Analytical Hierarchy Process (AHP) with the help of Geographical Information Systems (GIS) and Digital Photogrametry Techniques (DPT). A landslide inventory, lithology–weathering, slope, aspect, land cover, shear strength, distance to the river, stream density and distance to the road thematics data layers were used to create the map. These layer maps are produced using field, laboratory and office studies, and by the use of GIS and DPT. The landslide inventory map is also required to determine the relationship between these maps and landslides using DPT. In the study field in the Hemsindere Formation there are units that have different weathering classes, and this significantly affects the shear strength of the soil. In this study, shear strength values are calculated in great detail with field and laboratory studies and an additional layer is evaluated with the help of the stability studies used to produce the landslide susceptibility map. Finally, an overlay analysis is carried out by evaluating the layers obtained according to their weight, and the landslide susceptibility map is produced. The study area was classified into five classes of relative landslide susceptibility, namely, very low, low, moderate, high, and very high. Based on this analysis, the area and percentage distribution of landslide susceptibility degrees were calculated and it was found that 28% of the region is under the threat of landslides. Furthermore, the landslide susceptibility map and the landslide inventory map were compared to determine whether the models produced are compatible with the real situation resulting in compatibility rate of 84%. The total numbers of dwellings in the study area were determined one by one using aerial photos and it was found that 30% of the houses, with a total occupancy of approximately 2,300 people, have a high or very high risk of being affected by landslides.     

Landslide susceptibility mapping of the Sera River Basin using logistic regression model

Of the natural hazards in Turkey, landslides are the second most devastating in terms of socio-economic losses, with the majority of landslides occurring in the Eastern Black Sea Region. The aim of this study is to use a statistical approach to carry out a landslide susceptibility assessment in one area at great risk from landslides: the Sera River Basin located in the Eastern Black Sea Region. This paper applies a multivariate statistical approach in the form of a logistics regression model to explore the probability distribution of future landslides in the region. The model attempts to find the best fitting function to describe the relationship between the dependent variable, here the presence or absence of landslides in a region and a set of independent parameters contributing to the occurrence of landslides. The dependent variable (0 for the absence of landslides and 1 for the presence of landslides) was generated using landslide data retrieved from an existing database and expert opinion. The database has information on a few landslides in the region, but is not extensive or complete, and thus unlike those normally used for research. Slope, angle, relief, the natural drainage network (including distance to rivers and the watershed index) and lithology were used as independent parameters in this study. The effect of each parameter was assessed using the corresponding coefficient in the logistic regression function. The results showed that the natural drainage network plays a significant role in determining landslide occurrence and distribution. Landslide susceptibility was evaluated using a predicted map of probability. Zones with high and medium susceptibility to landslides make up 38.8 % of the study area and are located mostly south of the Sera River Basin and along streams.     

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 mapping using a modified decision tree classifier in the Xanthi Perfection,Greece

The objective of this study was to validate the outcomes of a modified decision tree classifier by comparing the produced landslide susceptibility map and the actual landslide occurrence, in an area of intensive landslide manifestation, in Xanthi Perfection, Greece. The values that concerned eight landslide conditioning factors for 163 landslides and 163 non-landslide locations were extracted by using advanced spatial GIS functions. Lithological units, elevation, slope angle, slope aspect, distance from tectonic features, distance from hydrographic network, distance from geological boundaries and distance from road network were among the eight landslide conditioning factors that were included in the landslide database used in the training phase. In the present study, landslide and non-landslide locations were randomly divided into two subsets: 80 % of the data (260 instances) were used for training and 20 % of the data (66 instances) for validating the developed classifier. The outcome of the decision tree classifier was a set of rules that expressed the relationship between landslide conditioning factors and the actual landslide occurrence. The landslide susceptibility belief values were obtained by applying a statistical method, the certainty factor method, and by measuring the belief in each rule that the decision tree classifier produced, transforming the discrete type of result into a continuous value that enabled the generation of a landslide susceptibility belief map. In total, four landslide susceptibility maps were produced using the certainty factor method, the Iterative Dichotomizer version 3 algorithm, the J48 algorithm and the modified Iterative Dichotomizer version 3 model in order to evaluate the performance of the developed classifier. The validation results showed that area under the ROC curves for the models varied from 0.7936 to 0.8397 for success rate curve and 0.7766 to 0.8035 for prediction rate curves, respectively. The success rate and prediction curves showed that the modified Iterative Dichotomizer version 3 model had a slightly higher performance with 0.8397 and 0.8035, respectively. From the outcomes of the study, it was induced that the developed modified decision tree classifier could be efficiently used for landslide susceptibility analysis and in general might be used for classification and estimation purposes in spatial predictive models.     

GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey)

Devrek town with increasing population is located in a hillslope area where some landslides exist. Therefore, landslide susceptibility map of the area is required. The purpose of this study was to generate a landslide susceptibility map using a bivariate statistical index and evaluate and compare the results of the statistical analysis conducted with three different approaches in seed cell concept resulting in different data sets in Geographical Information Systems (GIS) based landslide susceptibility mapping applied to the Devrek region. The data sets are created from the seed cells of (a) crowns and flanks, (b) only crowns, and (c) only flanks of the landslides by using ten different causative parameters of the study area. To increase the data dependency of the analysis, all parameter maps are classified into equal frequency classes based directly on the percentile divisions of each corresponding seed cell data set. The resultant maps of the landslide susceptibility analysis indicate that all data sets produce fairly acceptable results. In each data set analysis, elevation, lithology, slope, aspect, and drainage density parameters are found to be the most contributing factors in landslide occurrences. The results of the three data sets are compared using Seed Cell Area Indexes (SCAI). This comparison shows that the crown data set produces the most accurate and successful landslide susceptibility map of the study area.