Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal

<p>Landslides problems are one of the major natural hazards in the mountainous region. Every year due to the increase in anthropogenic factors and changing climate, the problem of landslides is increasing, which leads to huge loss of property and life. Landslide is a common and regular phenomenon in most of the northeastern states of India.  However, in recent past years, Manipur has experienced several landslides including mudslides during the rainy season. Manipur is a geologically young and geodynamically active area with many streams flowing parallel to fault lines. As a first step toward hazard management, a landslide susceptibility map is the prime necessity of the region. In this study, we have prepared a landslide hazard map of the state using freely available earth observations datasets and multi-criteria decision making technique, i.e., Analytic Hierarchy Process (AHP). For this purpose, lithology, rainfall, slope, aspect, relative relief, Topographic Wetness Index, and distance from road, river and fault were used as the parameters in AHP based on the understanding of their influence towards landslide in that region. The hazard map is classified into four hazard zones: Very High, High, Moderate, and Low. About 40% of the state falls under very high and high hazard zone, and the hilly regions such as Senapati and Chandel district are more susceptible to the landslide. Among the factors, slope and rainfall have a more significant contribution towards landslide hazard. It is also observed that areas nearer to NH-39 that lies in the fault zones i.e., Mao is also susceptible to high hazard. The landslide susceptibility map gives an first-hand impression for future land use planning and hazard mitigation purpose.</p>

Mountainous areas in Nepal are prone to landslides, resulting in an enormous loss of life and property every year. As a first step towards mitigating or controlling such problems, it is necessary to prepare landslide susceptibility maps. Various methodologies have been proposed for landslide susceptibility mapping. This study applies the weight of evidence method to the Tinau watershed in west Nepal. A landslide susceptibility map is prepared on the basis of field observations and available data of geology, land use, topography and hydrology. Predicted susceptibility levels are found to be in good agreement with the locations of past landslides. The results show that about 30 % of the area is highly susceptible to landsliding. The present results provide useful information to the authorities concerning the landslide susceptibility zones and possible improvements for disaster management activities and sustainable development.

Review on landslide susceptibility mapping using support vector machines

INTRODUCTION: Landslides are one of the recurrent natural problems that are widespread throughout the world, especially in mountainous areas, and cause a significant injury to and loss of human life and damage to properties and infrastructures. This study aimed to assess landslide susceptibility using the analytic hierarchy process (AHP) in Binalood Mountains, Razavi Khorasan Province, Iran. METHODS: Since the Binalood Mountains range has a high potential for landslides occurrence, the present study went through to map landslide susceptibility. To accomplish this, the AHP method was used, and then, receiver operating characteristic/area under the curves (AUCs) were prepared to evaluate the performance of the susceptibility map. Multiple data, such as lithology, distance to faults, land use, distance to roads, altitude, slope, aspect, stream power index, topographic wetness index, rainfall, distance from rivers, slope length index, and topographic location index, were considered for delineating the landslide susceptibility maps. These thematic layers were assigned suitable weights on the Saaty's scale according to their relative importance in landslide occurrence in the study area. The assigned weights of the thematic layers and their features were subsequently normalized using the AHP technique. Finally, all thematic layers were integrated by a weighted linear combination method in a geographic information system tool to generate landslide susceptibility maps. FINDINGS: The landslide susceptibility maps are split into five classes, namely very low, low, moderate, high, and very high. The results showed that the geological factor was the most important factor affecting the occurrence of landslides in the study area. Generally, 47.8% of the total area was considered high and very high-risk areas. The prediction accuracy of this map showed the values of AUC equal to 81.7% that showed the AHP model had very good accuracy. CONCLUSION: Overall, AHP is acceptable for landslide susceptibility mapping in the study area. A landslide susceptibility map is a useful tool to help with land management in landslide-prone areas. The results revealed that the predicted susceptibility levels were found to be in good agreement with the past landslide occurrences. Possibly, this map can be used by the concerned authorities in disaster management planning to prepare rescue routes, service centers, and shelters

This research is focused on developing landslide susceptibility, risk and management zonation map in the Ilam dam basin, in the west of Iran. For this purpose, all existent landslide locations in the basin (50 landslides) were registered using GPS device and 70% of these points (35 landslides) were used for landslide susceptibility modeling and the rest (15 events) were used to evaluate the model. In order to prepare landslide susceptibility map, eight key factors were used for landslide occurrence such as distance to fault, distance to stream, distance to road, lithology, land use/cover, slope percent, aspect and precipitation derived from the spatial database in Arc GIS 9.3. A hybrid method of logistic regression and Analytic Hierarchy Process (AHP) were respectively used to determine the weight and rate of different factors and their classes. After applying rate to classes of parameters using the AHP method, landslide susceptibility map was prepared by means of logistic regression analysis tool in IDRISI software. The model accuracy was assessed by receiver operating characteristic (ROC) indicator and the pseudoR 2 and used as a basis for risk mapping. The landslide risk map was prepared using Varnes equation through combining three maps of susceptibility, vulnerability and the elements at risk. In order to provide the landslide management map, multi-criteria evaluation (MCE) method was used incorporating susceptibility and risk variables. The results suggest the logistic regression and AHP model has high accuracy (ROC= 81.2%, pseudo-R 2 = 0.32). We found that 39.84, 72.45, and 76.33 km 2 of Ilam dam basinare located in the high and very high classes of lands lide susceptibility, risk and management maps, respectively.

The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection.

Landslide susceptibility mapping is of critical importance to identify landslide-prone areas to reduce future landslides, causalities, and infrastructural damages. This paper presents landslide susceptibility maps at a regional scale for the Chittagong Hilly Areas (CHA), Bangladesh. The frequency ratio (FR) was integrated with the analytical hierarchy process (AHP) (FR_AHP) and logistic regression (LR) (FR_LR). A landslide inventory of 730 landslide locations and 13 landslide predisposing factors including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), land use/land cover, rainfall, distance from drainage network, distance from fault lines, lithology, and normalized difference vegetation index (NDVI) were used. Landslide locations were randomly split into training (80%) and validation (20%) sites to support the susceptibility analysis. A safe zone was determined based on a slope threshold for logistic regression using the exploratory data analysis. The same number of non-landslide locations were randomly selected from the safe zone to train the model (FR_LR). Success and prediction rate curves and statistical indices, including overall accuracy, were used to assess model performance. The success rate curves show that FR_LR showed the highest area under the curve (AUC) (79.46%), followed by the FR_AHP (77.15%). Statistical indices also showed that the FR_LR model gave the best performance as the overall accuracy was 0.86 for training and 0.82 for validation datasets. The prediction rate curve shows similar results. The correlation analysis shows that the landslide susceptibility maps produced by FR and FR_AHP are highly correlated (0.95). In contrast, the correlation between the maps produced by FR and FR_LR was relatively lower (0.85). It indicates that the three models are highly convergent with each other. This study’s integrated methods would be helpful for regional-scale landslide susceptibility mapping, and the landslide susceptibility maps produced would be useful for regional planning and disaster management of the CHA, Bangladesh.

Identifying landslide-prone areas is an essential step in assessing landslide risk and reducing landslide damage. In this paper, GIS-based spatial analysis has been used to prepare the landslide susceptibility (LS) map in the north of Lorestan province in western Iran. For this purpose, three main criteria and their sub-criteria were identified as causative factors including geology and topography (i.e., distance from the fault, lithology, slope, aspect, and elevation), climate (i.e., rainfall and distance from the river), and environmental parameters (i.e., distance from the road, land-cover, NDVI). One hundred thirty-six known landslides were randomly divided into training ([Formula: see text] 70%) and validation ([Formula: see text] 30%) datasets. This study is based on the integration of popular analytic hierarchy process (AHP), frequency ratio (FR), and the fuzzy gamma operator (FGO) techniques. AHP was utilized to prioritize causal factors and fuzzy technique was applied in two stages of factor map fuzzification and calculation of sub-criteria maps and then overlap of fuzzified map layers. The fuzzy membership (FM) values were determined based on the FR method, which was normalized between the ranges of 0 and 1. Finally, LS zoning maps were estimated in five susceptibility classes (very low, low, moderate, high, and very high). Validation processes by comparing the three output maps with the layer of validation landslides in the study area and area under receiver operating characteristic curve confirm that the gamma value of 0.9 (AUC = 0.88) offers a more accurate LS map compared to other gamma values. The results of this study will be reliable for landslide risk reduction strategies.

This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residences, the demise of animal life, and the forced relocation of local inhabitants from their dwellings. The present investigation utilized remote sensing and GIS techniques in conjunction with the Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models to map landslide risk zones. For the landslide susceptibility mapping of this study, nine causative factors, such as “elevation, slope, aspect, drainage density, lineament density, land use and land cover (LULC), soil texture, rainfall, and lithology”, were considered. A total of 130 past landslide events were identified via field survey and Google Earth image, of which 70% (91) of them were used as training datasets and 30% (39) were used as validation datasets of the proposed models (i.e., FR and AHP). The nine contributing variables and their classes were evaluated, and factor weights were computed using the IDRISI Selva 17.0 expert programme. The landslide susceptibility indexes (LSI) of the FR and AHP models were calculated and categorized into five relative zones using ArcGIS 10.7. The landslide susceptibility map (LSM) produced by the FR model shows that 93.2 km2 (14.52%) of the study area is classified as very low susceptibility, 167.51 km2 (26.09%) as low susceptibility, 174.10 km2 (27.12%) as moderate susceptibility, 137.03 km2 (21.34%) as high susceptibility, and 70.30 km2 (10.93%) as very high susceptibility to landslides. Based on the AHP model's LSM, different landslide susceptibility zones were identified in the study area. Specifically, 140.46 km2 (21.88%) of the region falls into the very low susceptibility zone, 116.78 km2 (18.59%) falls into the low susceptibility zone, 147.94 km2 (23.04%) falls into the moderate susceptibility zone, 154.04 km2 (23.99%) falls into the high susceptibility zone, and 82.78 km2 (12.89%) falls into the very high susceptibility zone. The validation investigation demonstrated that the FR and AHP models had accuracy rates of 89.73 and 87.18%, respectively. The FR model exhibited marginally more accurate results than AHP, primarily because of the direct correlation between previous and current occurrences of landslides. Nevertheless, the AHP model’s effectiveness relies on the individual’s expertise and the characteristics of the components that cause the outcome. The landslide susceptibility maps generated through these models provide valuable insights for land management and disaster mitigation efforts, with delineated zones indicating very low to very high susceptibility areas.

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–based 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 and landslide hazard mapping are approaches used to assess the potential for landslides and predict the occurrence of landslides, respectively. We evaluated and tested a limit equilibrium approach to produce a local-scale, multi-temporal geographic information system-based landslide hazard map that utilized satellite soil moisture data, soil strength and hydrologic data, and a high-resolution (1.5 m) LiDAR-derived digital elevation map. The final multi-temporal landslide hazard map was validated temporally and spatially using four study sites at known landslide locations and failure dates. The resulting product correctly indicated low factor of safety values at the study sites on the dates the landslide occurred. Also, we produced a regional-scale landslide susceptibility map using a logistic regression machine learning model using 15 variables derived from the geomorphology, soil properties, and land-cover data. The area under the curve of the receiver operating characteristic curve was used for the accuracy of the model, which yielded a success rate of 0.84. We show that using publicly available data, a multi-temporal landslide hazard map can be created that will produce a close-to-real-time landslide predictive map. The landslide hazard map provides an understanding into the evolution of landslide development temporally and spatially, whereas the landslide susceptibility map indicates the probability of landslides occurring at specific locations. When used in tandem, the two mapping models are complementary to each other. Specifically, the landslide susceptibility mapping identifies the area most susceptible to landslides, while the landslide hazard mapping predicts when landslide may occur within the identified susceptible area.

Particularly in the last decade, landslide susceptibility and hazard maps have been used for urban planning and site selection of infrastructures. Most of the procedures for preparing of landslide susceptibility maps need high-quality landslide inventory map. Although the rainfall and seismic activities are accepted as triggering factor for landslides, designation of the triggering factor for each landslide in the inventory is almost impossible when well-documented records are unavailable. Therefore, during preparation of landslide susceptibility map, whole landslide records in the inventory map are used together without classifying based on the triggering factors. Although seismic activity is accepted as a triggering factor, possible effect of the use of seismic activity on production of landslide susceptibility map was investigated in this study, and the subject is open to discussion. For this purpose, a series of stability analyses based on circular failure and infinite slope model were performed considering different pseudostatic conditions. The results of analyses show that gentle slopes have higher susceptibility to failure than steeper ones, even if their stability conditions (susceptibilities) are similar for static condition. The seismic forces acting on failure surfaces may not be sufficiently taken into consideration in the conventionally prepared landslide susceptibility maps. Employing the general decreasing trend in stability condition based on slope face angle and the seismic acceleration, a new procedure was introduced for preparing of the landslide susceptibility map for a scenario earthquake. The prediction performance of occurring landslides increased after the procedure was applied to the conventionally prepared landslide susceptibility map. According to the threshold independent spatial performance analyses of the proposed methodology and the produced landslide susceptibility maps, the area under ROC curve values were calculated as 0.801, 0.933, and 0.947 for the maps prepared by considering conventional method and scenario earthquakes having M w values of 5.5 and 7.5, respectively.

The main purpose of this study was to map landslide susceptibility through the AHP and CF models, using a geographic information system (GIS), for the Baozhong region of Baoji City, China. At first, a landslide inventory map was prepared using technical reports, aerial photographs, and coupling with field surveys. A total of 79 landslides were mapped, out of which 55 (70 %) were randomly selected for building landslide susceptibility models, while the rest 24 landslides (30 %) were applied for validating the models. In this case study, the following landslide conditioning factors were evaluated: slope degree, slope aspect, plan curvature, altitude, geomorphology, lithology, distance from faults, distance from rivers, and precipitation. Subsequently, landslide susceptibility maps were produced using the AHP and CF models. Finally, the validation of landslide susceptibility map was accomplished with areas under the curve (AUC) and the Seed Cell Area Index (SCAI). The AUC plot estimation results indicated that the susceptibility map applying CF model has a higher prediction accuracy of 81.43 % than the accuracy of 75.97 % applying AHP model. Similarly, the validation results also showed that the success rate of the CF model was 85.93 %, while the success rate was 77.80 % for the AHP model. According to the validation results of the AUC evaluation, the map produced by CF model behaves better performance. Furthermore, the validation results using the SCAI also indicated that the CF model has a higher predication accuracy than the AHP model. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation.

This paper presents a comprehensive review of the application of the Analytical Hierarchy Process (AHP) in site suitability analysis, with a particular focus on disaster-prone areas such as flood and landslide zones. AHP, a multi-criteria decision analysis (MCDA) method, has been widely employed in spatial planning to evaluate and prioritize alternative sites based on a range of environmental, socio-economic, and regulatory criteria. Its strength lies in its structured hierarchical framework, the ability to incorporate both quantitative data and qualitative expert judgment, and its integration with Geographic Information Systems (GIS). The review systematically analyzes methodological approaches across numerous studies, highlighting best practices in criteria selection, pairwise comparison, consistency evaluation, and final suitability mapping. Key findings indicate that slope, lithology, and land use/land cover (LULC) are the most frequently prioritized factors in landslide susceptibility mapping, while flood susceptibility analysis consistently emphasizes rainfall intensity, proximity to rivers, and drainage density. This paper also explores sub-criteria weighting techniques such as straight ranking, reciprocal ranking, exponential ranking, and rank order centroid (ROC), evaluating their practicality in enhancing decision-making. A significant contribution of this review is its comparative synthesis of 20 landslide and 20 flood susceptibility studies across various global contexts. The results underscore the importance of context-specific criteria selection while advocating for standardized methodologies to enhance transparency and comparability. Despite its widespread use, AHP is not without limitations. Issues such as subjectivity in pairwise comparisons, sensitivity to inconsistencies, and methodological variability across studies may affect the robustness of results. The paper concludes with actionable recommendations to improve the consistency, reliability, and adaptability of AHP-based site suitability assessments, particularly in high-risk areas. This review thus serves as a valuable resource for researchers and practitioners aiming to leverage AHP for evidence-based and sustainable spatial decision-making.

Landslide susceptibility maps are very important tools in the planning and management of landslide prone areas. Qualitative and quantitative methods each have their own advantages and dis-advantages in landslide susceptibility mapping. The aim of this study is to compare three models, i.e., frequency ratio (FR), Shannon’s entropy and analytic hierarchy process (AHP) by implementing them for the preparation of landslide susceptibility maps. Shimla, a district in Himachal Pradesh (H.P.), India was chosen for the study. A landslide inventory containing more than 1500 landslide events was prepared using previous literature, available historical data and a field survey. Out of the total number of landslide events, 30% data was used for training and 70% data was used for testing purpose. The frequency ratio, Shannon’s entropy and AHP models were implemented and three landslide susceptibility maps were prepared for the study area. The final landslide susceptibility maps were validated using a receiver operating characteristic (ROC) curve. The frequency ratio (FR) model yielded the highest accuracy, with 0.925 fitted ROC area, while the accuracy achieved by Shannon’s entropy model was 0.883. Analytic hierarchy process (AHP) yielded the lowest accuracy, with 0.732 fitted ROC area. The results of this study can be used by engineers and planners for better management and mitigation of landslides in the study area.