Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study was to investigate the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. The landslide conditioning factors considered for the study area were slope gradient, slope aspect, altitude, lithology, land use, distance from streams, distance from roads, distance from faults, topographic wetness index, stream power index, stream transport index and plan curvature. For validation of the produced landslide susceptibility maps, the results of the analyses were compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all the landslide susceptibility models were constructed and the areas under the curves were calculated. The landslide locations were used to validate results of the landslide susceptibility maps. The verification results showed that the weights-of-evidence model (79.87%) performed better than certainty factor (72.02%) model with a standard error of 0.0663 and 0.0756, respectively. According to the results of the area under curve evaluation, the map produced by weights-of-evidence exhibits satisfactory properties.     

Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region,Uttarakhand

A comprehensive use of analytical hierarchy process (AHP) method in landslide susceptibility mapping (LSM) has been presented for rim region of Tehri reservoir. Using remote sensing data, various landslide causative factors responsible for inducing instability in the area were derived. Ancillary data such as geological map, soil map, and topographic map were also considered along with remote sensing data. Exhaustive field checks were performed to define the credibility of the random landslide conditioning factors considered in this study. Apart from universally acceptable inherent causative factors used in the susceptibility mapping, others such as impact of reservoir impoundment on terrain, topographic wetness index and stream power index were found to be important causative factors in rim region of the Tehri reservoir. The AHP method was used to acquire weights of factors and their classes respectively. Weights achieved from AHP method matched with the existing field conditions. Acceptable consistency ratio (CR) value was achieved for each AHP matrix. Weights of each factor were integrated with weighted sum technique and a landslide susceptibility index map was generated. Jenk’s natural break classifier was used to classify LSI map into very low, low, moderate, high and very high landslide susceptible classes. Validation of the susceptibility map was performed using cumulative percentage/success rate curve technique. Area under curve value of the success rate curve was converted to percentage validation accuracy and a reasonable 78.7% validation accuracy was achieved.     

Applications of analytical hierarchy process (AHP) and analytical network process (ANP) for industrial site selections in Isfahan,Iran

Industrial sites are key factors in urban and regional land use planning. Therefore, determining the location of industrial areas is a critical and complex process for development and success. Industrial site selection aims in identifying the most suitable sites for industry creation, considering a set of influential criteria. Therefore, site selection generally and industrial site selection specifically can be categorised as a multi-criteria decision-making (MCDM) problem that requires detailed evaluation of various dimensions. This study developed a set of clusters containing 10 selection criteria for industrial site selection in Isfahan metropolitan area, Iran. The relationships between the criteria and clusters were modelled and analysed using analytical hierarchy process (AHP) and analytical network process (ANP). AHP and ANP agree in finding distance to water bodies and distance to other industries as the most and least important selection criteria. Four patches have been identified as suitable alternatives for industrial construction. While AHP found Borkhar Patch 1 as the most appropriate alternative, ANP demonstrated the superiority of Ardestan Patch over others. Conducting a sensitivity analysis for the models confirmed both models robustness in industrial site selection decisions.     

Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia)

Flash floods are among the most severe hazards which have disastrous environmental, human, and economic impacts. This study is interested in the characterization of flood hazard in Gabes Catchment (southeastern Tunisia), considered as an important step for flood management in the region. Analytical hierarchy process (AHP) and geographic information system are applied to delineate and characterize flood areas. A spatial database was developed based on geological map, digital elevation model, land use, and rainfall data in order to evaluate the different factors susceptible to affect flood analysis. However, the uncertainties that are associated with AHP techniques may significantly impact the results. Flood susceptibility is analyzed as a function of weights using Monte Carlo (MC) simulation and Global sensitivity analysis. AHP and MC–AHP models gave similar results. However, compared to AHP approach, MC–AHP confidence intervals (95%) of the overall scores had small overlaps. Results obtained were validated by remote sensing data for the zones that showed very high flood hazard during the extreme rainfall event of June 2014 that hit the study basin.     

Application and verification of fuzzy algebraic operators to landslide susceptibility mapping

The aim of this study was to apply and to verify the use of fuzzy logic to landslide susceptibility mapping in the Gangneung area, Korea, using a geographic information system (GIS). For this aim, in the study, a data-derived model (frequency ratio) and a knowledge-derived model (fuzzy operator) were combined. Landslide locations were identified by changing the detection technique of KOMPSAT-1 images and checked by field studies. For landslide susceptibility mapping, maps of the topography, lineaments, soil, forest, and land cover were extracted from the spatial data sets, and the eight factors influencing landslide occurrence were obtained from the database. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslide susceptibility mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (λ = 0.975) showed the best accuracy (84.68%) while the case in which the fuzzy or operator was applied showed the worst accuracy (66.50%).     

Assessing LNRF,FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian watershed in Lorestan province,Iran

Landslides and slope instabilities are major risks for human activities which often lead to economic losses and human fatalities all over the world. The main purpose of this study is to evaluate and compare the results of Landslide Nominal Risk Factor (LNRF), Frequency Ratio (FR), and Analytical Hierarchy Process (AHP) models in mapping Landslide Susceptibility Index (LSI). The study case, Nojian watershed with an area of 344.91 km², is located in Lorestan province of Iran. The procedure was as follows: first, the effective factors of the landslide basin were prepared for each layer in the GIS software. Then, the layers and the landslides of the basin were also prepared using aerial photographs, satellite images, and fieldwork. Next, the effective factors of the layers were overlapped with the map of landslide distribution to specify the role of units in such distribution. Finally, nine factors including lithology, slope, aspect, altitude, distance from the fault, distance from river, fault land use, rainfall, and altitude were found to be effective elements in landslide occurrence of the basin. The final maps of LSI were prepared based on seven factors using LNRF, FR, and AHP models in GIS. The index of the quality sum (Qs) was also used to assess the accuracy of the LSI maps. The results of the three models with LNRF (40%), FR (39%), and AHP (44%) indicated that the whole study area was located in the classes of high to very high hazard. The Qs values for the three models above were also found to be 0.51, 0.70 and 0.70, respectively. In comparison, according to the amount of Qs, the results of AHP and FR models have slightly better performed than the LNRF model in determining the LSI maps in the study area. Finally, the study watershed was classified into five classes based on LSI as very low, low, moderate, high, and very high. The landslide susceptibility maps can be helpful to select sites and mitigate landslide hazards in the study area and the regions with similar conditions.     

Analytic hierarchy process applied to landslide susceptibility mapping of the North Branch of Argentino Lake,Argentina

Natural Hazards - In the present study, we achieved the susceptibility mapping to slope instability processes by the implementation of Analytic Hierarchy Process and Weighted Linear Combination...     

Application of analytical hierarchy process and least-squares method for landslide susceptibility assessment along the Zhong-Wu natural gas pipeline, China

As one of the major problems of geo-engineering, landslides often influence the safety of linear engineering projects that cross mountainous areas. Therefore, when selecting suitable routes for such projects, it is important to assess their susceptibility to landslides. In this paper, we used a natural gas pipeline in the northeast of the Yunnan-Guizhou Plateau of China as a case study to analyze landslide susceptibility. Based on engineering geological analogy, the analytical hierarchy process, and the least-squares method, a regional landslide susceptibility assessment model was developed and was programmed using GIS ArcEngine components under the Visual Studio.NET environment. The landslide susceptibility along the Zhong-Wu natural gas pipeline from Zhongxian County to Wuhan was assessed based on this model and classified into five levels: very safe, safe, moderate, susceptible, and very susceptible. The high accuracy and prediction capability of the model were confirmed by comparing the model results with past landslide data and performing a prediction test. The results indicated that the assessment model used in this study is reliable and can be used for landslide susceptibility assessment and route selection in other areas.     

Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey)

The northeast part of Turkey is prone to landslides because of the climatic conditions, as well as geologic and geomorphologic characteristics of the region. Especially, frequent landslides in the Rize province often result in significant damage to people and property. Therefore, in order to mitigate the damage from landslides and help the planners in selecting suitable locations for implementing development projects, especially in large areas, it is necessary to scientifically assess susceptible areas. In this study, the frequency ratio method and the analytical hierarchy process (AHP) were used to produce susceptibility maps. Especially, AHP gives best results because of allowing better structuring of various components, including both objective and subjective aspects and comparing them by a logical and thorough method, which involves a matrix-based pairwise comparison of the contribution of different factors for landslide. For this purpose, lithology, slope angle, slope aspect, land cover, distance to stream, drainage density, and distance to road were considered as landslide causal factors for the study area. The processing of multi-geodata sets was carried out in a raster GIS environment. Lithology was derived from the geological database and additional field studies; slope angle, slope aspect, distance to stream, distance to road and drainage density were invented from digital elevation models; land cover was produced from remote sensing imagery. In the end of study, the results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.     

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.     

A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method

In spite of its small size, Japan suffers many landslide disasters due to intense rainfall and earthquakes. This article describes the distribution and topography of these landslides, and a new method of evaluating the susceptibility, the analytical hierarchic process (AHP). The method assigns scores to each factor of micro-topography of landslide-prone areas identified in aerial photographs, and assesses the susceptibility of landslide from the total score. In addition, a method of simulating sliding mass runout is briefly presented for the designating sediment-related disaster warning areas.     

Municipal solid waste landfill siting by using GIS and analytical hierarchy process (AHP): a case study in Qom city,Iran

Qom is the eighth most populated city in center of Iran, and its population growth rate is among the highest in this country. Th presence of a Great Salt Lake, petroleum potential and tourism attractions in this city sheds light on the importance of how solid waste landfill locations should be disposed, located and managed as an environmental issue. Considering the key parameters in landfill site selection, in this study a series of location analysis have been conducted to locate optimum regions for municipal solid waste disposal, using analytical hierarchy process (AHP) and geographical information system (GIS). The main factors in selecting the suitable location for waste disposal include geomorphology–hydrography, environmental–social factors and design criteria, each of which are subdivided into several categories. Criteria are selected according to the regional condition; therefore, important factors such as distance from sea and forested areas were not considered. In the next step, digital layers are weighted and classified according to the available standards and expert judgment. Then, analytical multi-criteria decision-making algorithms as AHP and weighted linear combination are applied upon existing layers in GIS. The results show that by implementing the AHP method in this region only 7% of the study area has a very good and appropriate condition for landfill location and the field observation confirms them. Finally, considering the environmental effects of landfill, appropriate locations are suggested.     

Landslide susceptibility mapping by using fuzzy logic: a case study of Cham-gardalan catchment,Ilam, Iran

Landslides are introduced as regional movements, which influence different engineering structures such as roads, railways, and dams and cause the person’s death. Identification of landslide zones may decrease the financial losses and human injuries or deaths. This study tries to achieve a landslide susceptibility mapping in Cham-gardalan catchment by weighting the main criteria and the membership functions of fuzzy logic. For this, we applied the best relationship function between the presence and absence of landslides as well as a collection of the elements. At first, the landslide points were identified by the means of some components those of satellite images, topographical (1:50,000) and geographical (1:100,000) maps, field visits, and Google Earth software followed by the preparation of landslide distribution maps. Then, all effective landslide factors such as percentage of slope, slope aspect, height, geology, land uses, distance from roads, distance from drainages, distance from breakage, and precipitation map have been utilized in order to conduct the fuzzy analyses. Landslide susceptibility map was performed by fuzzy operators (Gamma, Product, Sum, Or, And) in the study area. After fuzzificating and weighting, the effective criteria of landslides were determined through fuzzy Gamma operators with the landaus of 0.2, 0.5, 0.8, and 0.9 and by comparing final maps for making an appropriate model of landslide susceptibility mapping. The regional susceptibility map represents the landslide-prone areas in five categories those of very low, low, moderate, high, and very high. Our results indicated that among the applied operators, Gamma with landau of 0.9 can be used as an appropriate method for mapping the landslide susceptibility due to the suitable fuzzification of given criteria based on landslide distribution maps. In addition, the elements of road, percentage of slope, distance from drainage, and geology were recognized as the most important factors for occurring the landslides.     

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.     

Application of logistic regression and fuzzy operators to landslide susceptibility assessment in Azdavay (Kastamonu,Turkey)

Landslides and their assessments are of great importance since they damage properties, infrastructures, environment, lives and so on. Particularly, landslide inventory, susceptibility, and hazard or risk mapping have become important issues in the last few decades. Such maps provide useful information and can be produced by qualitative or quantitative methods. The work presented in this paper aimed to assess landslide susceptibility in a selected area, covering 570.625 km² in the Western Black Sea region of Turkey, by two quantitative methods. For this purpose, in the first stage, a detailed landslide inventory map was prepared by extensive field studies. A total of 96 landslides were mapped during these studies. To perform landslide susceptibility analyses, six input parameters such as topographical elevation, lithology, land use, slope, aspect and distance to streams were considered. Two quantitative methods, logistic regression and fuzzy approach, were used to assess landslide susceptibility in the selected area. For the fuzzy approach, the fuzzy and, or, algebraic product, algebraic sum and gamma operators were considered. At the final stage, 18 landslide susceptibility maps were produced by the logistic regression and fuzzy operators in a GIS (Geographic Information System) environment. Two performance indicators such as ROC (relative operating characteristics) and cosine amplitude method (r _ij ) were used to validate the final susceptibility maps. Based on the analyses, the landslide susceptibility map produced by the fuzzy gamma operator with a level of 0.975 showed the best performance. In addition, the maps produced by the logistic regression, fuzzy algebraic product and the higher levels of gamma operators showed more satisfactory results, while the fuzzy and, or, algebraic sum maps were not sufficient to provide reliable outputs.     

Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520 $ ({\text{AUC}}_{\text{AHP}} = 75.70\;\% ,\;{\text{AUC}}_{\text{SI}} = 80.37\;\% ,\;{\text{and}}\;{\text{AUC}}_{\text{BLR}} = 85.20\;\% ) $ ( AUC AHP = 75.70 % , AUC SI = 80.37 % , and AUC BLR = 85.20 % ) . Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.     

Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal

The goal of this paper is to evaluate and compare the consistency of GIS-based heuristic and bivariate landslide susceptibility mapping techniques in the Himalayan region, taking the Kulekhani watershed of central Nepal as an example. For this purpose, a heuristic and two statistical bivariate landslide susceptibility mapping methods are applied, and three separate landslide susceptibility zonation maps are produced. The maps are compared using three approaches: landslide density analysis, success rate analysis, and agreed area analysis. A comparison of the values obtained from landslide density analysis and the curves of success rate analysis indicate that the two bivariate methods produce almost identical results, whereas the map produced with the heuristic method differs significantly from the others. On the other hand, the agreed area analysis highlights significant spatial differences in the maps obtained from the three methods. Although the three approaches evaluate the consistency of susceptibility maps, only the agreed area analysis is capable of spatially comparing them. Hence, this approach proves to be more suitable for spatially and quantitatively evaluating the consistency of various landslide susceptibility zonation maps.     

Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal,India

A field experiment was conducted from 2 May 2010 to 1 May 2012 in the Gurbantunggut Desert, the second largest desert in China, to investigate saltation activity and its threshold velocity, and their relations with atmospheric and soil conditions. The results showed that saltation activity occurred more frequently during 08:00–20:00 Local Standard Time in spring and summer, with air temperatures between 20.0 and 29.0 °C, water vapor pressures between 0.6 and 0.9 kPa, soil temperatures between 25.0 and 30.0 °C, and a soil moisture lower than 0.04 m³/m³. At 2 m height, the saltation threshold velocity varied between 11.1 and 13.9 m/s, with a mean of 12.5 m/s. Threshold velocity showed clear seasonal variations in the following sequence: spring (11.7 m/s) < autumn (12.7 m/s) < summer (13.6 m/s). Affected by soil conditions, aeolian sand transport was weak, with an average annual aeolian sand that transported across a section (1.0 m × 2.0 m) of less than 6.0 kg.     

GIS based landslide susceptibility mapping using a fuzzy logic approach: A case study from Ghurmi-Dhad Khola area, Eastern Nepal

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