Landslide Susceptibility Zonation Map Research Articles

Investigating the dynamic nature of landslide susceptibility in the Indian Himalayan region.

Landslide susceptibility zonation (LSZ) mapping is used to delineate areas prone to landslides and is critical for effective landslide hazard management. The existing methodologies for generating such maps tend to neglect the influence of dynamic environmental variables on landslide occurrences, which may lead to obsolete and erroneous estimates of landslide susceptibility (LS) for a concerned area. Although recent studies have started to report the effects of Land Use/ Land Cover (LULC) variation on LSZ mapping, variations in other dynamic variables like rainfall, soil moisture, and evapotranspiration apart from LULC may also influence slope stability in mountainous regions. The present study investigates the impact of variations in these four variables on the LS distribution, of a selected Indian Himalayan region between 2017 and 2021. Random Forest (RF) susceptibility models are utilized for evaluating the LS for the selected years and geospatial technologies are employed for LS change detection. The results indicate up to 19% variations in the spatial extent for some of the zones of the generated LSZ maps. The research findings of this study are crucial since they reveal the impact of dynamic behavior on LS, which has not been previously documented in the literature.

Assessing Landslide Susceptibility along India’s National Highway 58: A Comprehensive Approach Integrating Remote Sensing, GIS, and Logistic Regression Analysis

The NH 58 area in India has been experiencing an increase in landslide occurrences, posing significant threats to local communities, infrastructure, and the environment. The growing need to identify areas prone to landslides for effective disaster risk management, land use planning, and infrastructure development has led to the increased adoption of advanced geospatial technologies and statistical methods. In this context, this research article presents an in-depth analysis aimed at developing a landslide susceptibility zonation (LSZ) map for the NH 58 area using remote sensing, GIS, and logistic regression analysis. The study incorporates multiple geo-environmental factors for analysis, such as slope aspect, curvature, drainage density, elevation, fault distance, flow accumulation, geology, geomorphology, land use land cover (LULC), road distance, and slope angle. Utilizing 50% of the landslide inventory data, the logistic regression model was trained to determine correlations between causal factors and landslide occurrences. The logistic regression model was then employed to calculate landslide probabilities for each mapping unit within the NH 58 area, which were subsequently classified into relative susceptibility zones using a statistical class break technique. The model’s accuracy was verified through ROC curve analysis, resulting in a 92% accuracy rate. The LSZ map highlights areas near road cut slopes as highly susceptible to landslides, providing crucial information for land use planning and management to reduce landslide risk in the NH 58 area. The study’s findings are beneficial for policymakers, planners, and other stakeholders involved in regional disaster risk management. This research offers a comprehensive analysis of landslide-influencing factors in the NH 58 area and introduces an LSZ map as a valuable tool for managing and mitigating landslide risks. The map also serves as a critical reference for future research and contributes to the broader understanding of landslide susceptibility in the region.

GIS based landslide susceptibility zonation mapping using frequency ratio, information value and weight of evidence: a case study in Kinnaur District HP India

GIS based landslide susceptibility zonation mapping using frequency ratio, information value and weight of evidence: a case study in Kinnaur District HP India

Application of index of entropy and Geospatial techniques for landslide prediction in Lunglei district, Mizoram, India

The present study focuses on developing a landslide susceptibility zonation (LSZ) using GIS-based bivariate statistical model in the Lunglei district of Mizoram. Initially, 17 factors were selected after calculating the multicollinearity test for LSZ. A landslide inventory map was created based on 234 historic landslide events, which were randomly divided into training (70%) and testing (30%) datasets. Using the Index of Entropy (IOE) model, nine causative factors were identified as having significant weightage for LSZ: elevation, slope, aspect, curvature, normalized difference vegetation index, geomorphology, distance to road, distance to lineament, and distance to river. On the other hand, factors such as land use land cover, stream power index, terrain ruggedness index, terrain roughness, topographic wetness index, annual rainfall, topographic position index, and geology had negligible weightage. Based on the relative importance of the causative factors, two models were developed: scenario 1, which considered nine factors, and scenario 2, which considered all 17 factors. The results revealed that 16% and 14% of the district area were identified as very highly landslide prone in scenario 1 and scenario 2, respectively. The high susceptibility zone accounted for 26% and 25% of the area in scenario 1 and scenario 2, respectively. To assess the accuracy of the models, a receiver operating characteristic (ROC) curve and quality sum ratio method was performed using 30% of the testing landslide data and an equal number of non-landslide data points. The area under the curve (AUC) for scenario 1 and scenario 2 were 0.947 and 0.922, respectively, indicating higher efficiency for scenario 1. The quality sum ratios were 0.435 and 0.43 for scenario 1 and scenario 2, respectively. Based on these results, the LSZ mapping from scenario 1 is considered suitable for policymakers to address development and risk reduction associated with landslides.

GIS-based landslide susceptibility zonation (LSZ) mapping of Darjeeling Himalaya, India using weights of evidence (WoE) model

GIS-based landslide susceptibility zonation (LSZ) mapping of Darjeeling Himalaya, India using weights of evidence (WoE) model

GIS based landslide susceptibility mapping and assessment using weighted overlay method in Wayanad: A part of Western Ghats, Kerala

The objectives of the study are to generate a landslide susceptibility zonation (LSZ) map of Wayanad for effective and efficient disaster management in the future and to determine the various parameters and usefulness of remote sensing and GIS in landslide susceptibility mapping. The topographical factors like slope, elevation, and aspects influencing landslides are derived from the digital elevation model (DEM). The rainfall data were collected from 2011 to 2019 from the Indian Meteorological Department (IMD) and four sampling sites were identified through a field visit. We used Landsat8 Enhanced Thematic Mapper Plus (ETM+) satellite imagery with a resolution of 30 m to create land use and land cover (LULC) using the unsupervised classification method. It was divided into seven categories based on the annual precipitation of the study area using inverse distance weighting (IDW). The LSZ map was derived using the weighted overlay method by analysing the geographical and climatic factors. These factors helped produce the final LSZ map, which was divided into three zones from high to low. LSZ areas with steep slopes, high elevation, prolonged rainfall, and predominant land use changes. Study reveals that the north-east, south, and south-east regions of the study area are more vulnerable to landslides.

An Improved Unascertained Measure-Set Pair Analysis Model Based on Fuzzy AHP and Entropy for Landslide Susceptibility Zonation Mapping

Landslides are one of the most destructive and common geological disasters in the Tonglvshan mining area, which seriously threatens the safety of surrounding residents and the Tonglvshan ancient copper mine site. Therefore, to effectively reduce the landslide risk and protect the safety of the Tonglvshan ancient copper mine site, it is necessary to carry out a systematic assessment of the landslide susceptibility in the study area. Combining the unascertained measure (UM) theory, the dynamic comprehensive weighting (DCW) method based on the fuzzy analytic hierarchy process (AHP)-entropy weight method and the set pair analysis (SPA) theory, an improved UM-SPA coupling model for landslide susceptibility assessment is proposed in this study. First, a hierarchical evaluation index system including 10 landslide conditioning factors is constructed. Then, the dynamic comprehensive weighting method based on the fuzzy AHP-entropy weight method is used to assign independent comprehensive weights to each evaluation unit. Finally, we optimize the credible degree recognition criteria of UM theory by introducing SPA theory to quantitatively determine the landslide susceptibility level. The results show that the improved UM-SPA model can produce landslide susceptibility zoning maps with high reliability. The whole study area is divided into five susceptibility levels. 5.8% and 10.16% of the Tonglvshan mining area are divided into extremely high susceptibility areas and high susceptibility areas, respectively. The low and extremely low susceptibility areas account for 30.87% and 34.14% of the total area of the study area, respectively. Comparison with the AHP and Entropy-FAHP models indicates that the improved UM-SPA model (AUC = 0.777) shows a better performance than the Entropy-FAHP models (AUC = 0.764) and the conventional AHP (AUC = 0.698). Therefore, these results can provide reference for emergency planning, disaster reduction and prevention decision-making in the Tonglvshan mining area.

Landslide susceptibility and risk analysis in Benighat Rural Municipality, Dhading, Nepal

The complex geology and undulating terrain made Nepal vulnerable to natural disasters like landslides. Benighat-Rorang Rural Municipality (RM), in the Dhading district of Nepal's Bagmati province, has experienced several minor to massive landslides that have harmed both nature and civilization. This study examines the factors influencing landslides in the Benighat-Rorang RM by analyzing soil structure, geology, land cover, geomorphology (primarily slopes and aspects), fault lines, drainage density, weather data, and road density to generate a comprehensive Landslide Susceptibility Mapping (LSM). The LSM will help in identifying landslide-prone zones (high to low), which will, in turn, enable stakeholders to implement appropriate mitigation measures across the landslide-induced rural municipality. The current study intends to create Landslide susceptibility zonation mapping within and around the studied area by applying the AHP method while taking into account the optimal set of geo-environmental parameters to identify regions at risk of future landslides. Elevation, Slope, Aspect, Drainage, Geology, Soil Classes, Fault Line, Lineaments, Land-cover, Road Networks, Population, and climatic parameters (Rainfall, Temperature, Relative Humidity, Surface Pressure, and Wind Speed) are among the fourteen geo-environmental elements used for this research. Using the field verification approach, the results of this procedure have been validated, which can be observed in an estimated success rate curve. Meteorological factors, such as temperature, rainfall, relative humidity, surface pressure, and wind speed, have been examined regarding landslide susceptibility. Thus, an integrated assessment of landslide susceptibility was applied to the area to identify inhabited areas vulnerable to or at risk of landslides. Furthermore, the placement of public amenities throughout the research zone was considered while conducting the social vulnerability risk analysis. Finally, landslide susceptibility zonation, climatic factors influencing landslide susceptibility, and social vulnerability assessment results of the study area have been combined to generate a risk map identifying landslide-prone municipal facilities and vulnerable communities. This study will help in building resilient landslide communities through effective spatial urban planning that incorporates regional risks induced by landslides with infrastructure development and management strategies.

Using Landslide Statistical Index Technique for Landslide Susceptibility Mapping: Case Study: Ban Khoang Commune, Lao Cai Province, Vietnam

Ban Khoang is a mountainous commune in Sa Pa district located in the central part of Lao Cai province, Vietnam. Landslides occur frequently in this area and seriously affect the local living conditions. To help the local authority in developing a landslide disaster action plan, the statistical index method for landslide susceptibility mapping is applied. As the result, the landslide susceptibility zonation (LSZ) map was created. The LSZ map indicates that areas of low, moderate, high and very high landslide susceptibility zones are, respectively, 20.3 km2, 12.4 km2, 15.4 km2, and 5.2 km2; most of the observed landslide areas that are well predicted belong to high or very high landslide susceptibility classes. In detail, 80% observed landslide areas and 78.57% number of observed landslides were well predicted, and the area (AUC) under the receiver operating characteristic (ROC) curve obtained 80.3%. Hence, the high and very high landslide susceptibility classes in the LSZ map can be considered highly believable, and the LSZ map will be reliable to use in the practice.

GIS-based landslide susceptibility zonation and risk assessment in complex landscape: A case of Beshilo watershed, northern Ethiopia

Landslides necessitate comprehensive prediction of susceptible zones for timely intervention to mitigate any disaster. Present study at Beshilo-watershed in Amhara Regional State of Ethiopia was attempted to prepare landslide susceptibility zonation (LSZ) map and to carry out risk assessment. GIS-based multi-criteria decision analysis was adopted by considering factors; land-use/land-cover, slope, elevation, aspect, drainage-density, rainfall, lithology, lineament/fault density, soil depth, and texture. Factor weights were determined through Analytical Hierarchy Process (AHP) and the factor classes rating were assigned through logical judgment. Landslide susceptibility indices were determined based on a continuous numerical scale developed for this purpose. Very high and high susceptibility zones were found to spread in 1215.28 km2 and 2279.87 km2, respectively in the eastern and north-eastern parts that are characterized by high lineament/fault density, barren lands, shrubs, bushes, grassland, or croplands mostly having unconsolidated soils with shallow depth. The Receiver Operating Characteristics (ROC) curve showed acceptable results. Further, risk assessment indicated that populations in high to very high-risk zones need timely attention of the local authorities to avert any unwanted happenings.

Performance assessment for three statistical models of landslide susceptibility zonation mapping: A case study for Dharamshala Region, Himachal Pradesh, India

Performance assessment for three statistical models of landslide susceptibility zonation mapping: A case study for Dharamshala Region, Himachal Pradesh, India

GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya.

Landslides are one the most destructive and life-endangering hazard in the Darjeeling Himalayan region and keeping in mind the interest of society and their future prospects identification of landslide potential areas is a very pertinent task in this area. Therefore, the present study aimed toward the landslide susceptibility zonation (LSZ) mapping in and around the Kalimpong region by applying Analytic Hierarchy Process (AHP) method integrated with fifteen causative factors including slope, lineament, drainage density, land use land cover, relative relief, soil texture, lithology, elevation, aspect, thrust and faults, plan curvature, profile curvature, road network, topographic wetness index and stream power index. Tolerance and variance inflation factors with Pearson's correlation coefficient are used to assess potential collinearity among the selected factors, and subsequently, the final model has been constructed by enduring an acceptable consistency ratio (<0.10). Thereafter, to classify this region into very low, low, moderate, high and very high susceptible zones quantile, geometric interval, Jenk's natural break and success rate curve (SRC) techniques are adopted to compare and check the optimum LSZ categorization. Considering the identified 647 landslides, Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curve is used to gauge the best LSZ map. The AUC ROC shows SRC method (m = 0.9) yields the highest result, achieving a prediction accuracy of 79.5% and, therefore, is considered the most promising LSZ form for the present study area. The results obtained from the study highlight the spatial information of areas that may face slope instability and helps government agencies, stakeholders for drafting adequate measures due to absence of proper landslide early warning systems in this region.

Evaluation of Parameter Selection in the Bivariate Statistical-based Landslide Susceptibility Modeling (Case Study: the Citarik Sub-watershed, Indonesia)

A landslide susceptibility mapping is essential for landslide hazard mitigation to reduce the associated risk. This paper aims to present the results of the landslide susceptibility modeling in the Citarik sub-watershed using three bivariate statistical-based methods, i.e., frequency ratio (FR), information value (IV), and weight of evidence (WoE). The main objective of this study is to evaluate the significance of the threshold of the area under curve (AUC) value in parameter selection. In this study, 118 landslide pixels were compiled from Google Earth images, unmanned aircraft vehicle (UAV) aerial photos taken just after the landslide, official landslide reports, and field observation. Thirteen landslide causative factors were prepared in Geographic Information System (GIS) environment, derived from various satellite images and maps. The landslide data were divided into two groups, 70% of data as training data and the rest as test data. Two scenarios that involve a different number of parameters were compared to explain the threshold of the AUC value in parameter selection and model accuracy. The result of this study shows that the AUC value threshold of 0.6 for parameter selection cannot be applied in all cases, and the performance of both two scenarios was excellent in assessing landslide susceptibility in this study area. Those three landslide susceptibility zonation maps of the best scenario showed that the sub-watershed's northern, northeastern, south-eastern, and southern parts were under high to very high susceptibility to landslides, including the Cimanggung area where a recent deadly double landslide occurred.

Landslide susceptibility assessment in the Himalayan range based along Kasauli – Parwanoo road corridor using weight of evidence, information value, and frequency ratio

The Himalayan region is prone to landslides. Rainfall-induced slope failure activities in the Indian Himalaya cause considerable damage, posing a serious risk to life and property. This study attributes information regarding landslide triggering parameters to further delineate landslide susceptibility maps of Himachal Pradesh in the Himalayan region. The landslide inventory map was created using information from field visits, Linear Imaging Self-Scanning Sensor (LISS III), and Google Earth. Thereafter, eight landslide causative factors, viz. slope, aspect, curvature, elevation, Landuse Landcover (LULC), soil, lithology, and drainage density were performed by employing the weight of evidence (WOE), information value method (IVM) and the frequency ratio (FR) methods. Using the ArcGIS reclassification tool, the final landslide susceptibility zonation (LSZ) maps were categorized into five susceptibility zones: “very low, low, medium, high, and very high.” The success rate for WOE, FR, and IVM models was determined as 76.27%, 78.20%, and 76.09% respectively, depicting that the FR model based LSZ map is more accurate. According to the FR model based LSZ map, the highly susceptible classes in the case of aspect, curvature, and lithology are southeast, concave, and TBS, respectively. The sparsely vegetated areas are more prone to landslides than the other LULC areas. The higher elevations, ranging from 1191 to 1434, 1434–1655, and 1655–1876 m, are more vulnerable to landslide activities as compared to low elevations. The slope classes 30–45 and 45–60, medium drainage density class and fine loamy class of soils are more likely to landslides. The prepared susceptibility zone map can be used for future mitigation planning in the high and very high susceptibility zones in order to reduce landslide-related human and economic losses.

Landslide Susceptibility Mapping along Manipur-Assam NH-37

Landslides are a natural hazard in steep places that occur regularly and cause significant damage. To avoid and minimise hazards, comprehensive landslide remediation and control, landslide assessment, and hazard zonation are required. Various methods are established based on different assessment methodologies, which are essentially split into qualitative and quantitative approaches. GIS-based landslide susceptibility mapping was carried out along the National Highway 37, which connects Assam and Manipur and is a vital lifeline for the state, to identify and demarcate possible failure zones. A field visit was used to create a landslide inventory map along the road network. Google Earth and LANDSAT satellite imagery To perform landslide susceptibility zonation, thematic layers of several landslide causative elements were constructed in the study region. The study region has been divided into five endangered zones i.e. (“very low, low, moderate, high, and extremely high”). The landslide susceptibility zonation map was validated using the AUC and landslide density methods. The final map will be helpful to a variety of stakeholders, including town planners, engineers, geotechnical engineers, and geologists, for development and construction in the study region.

Landslide susceptibility zonation mapping using statistical index and landslide susceptibility analysis methods: A case study from Gindeberet district, Oromia Regional State, Central Ethiopia

The present study was carried out in Gindeberet district, central Ethiopia and covers a total of 128 km2. The geology of the area is comprised of sandstone, mudstone, limestone and basalt. The general objective was to evaluate the factors influencing the landslide initiation and to prepare a landslide susceptibility zonation map. One hundred thirty landslides were identified through field investigation and Google Earth image interpretation. Statistical Index Model (SIM) and Landslide Susceptibility Analysis (LSA) were used for the preparation of landslide susceptibility map. Lithology, slope steepness, aspect, land−use/land cover, and distance from drainage are the causative factors selected, whereas rainfall and human activities are considered as the triggering factors. The relations of landslides and causative factors were determined in terms of weighted value (Wij). From the lithology limestone, mudstone and weathered basalt have strong relationship with the landslides. Moreover, the result shows that weighted values (Wij) are high for N and W orientation class of slope aspect; greater than 45° slope steepness; agricultural land and moderately vegetated land−use/land cover class and 0−100 m and 100−200 m distance from drainage class. Landslide susceptibility zonation (LSZ) map was prepared and classified into very low, low, high, and very high susceptible zones. For LSZ map prepared by SIM model out of 130 landslides inventory data, 2 (1.54%) of the landslides fall in very low susceptible zone, 11 (8.46%) in low susceptible zone, 12 (9.23%) in moderate susceptible zone, 16 (12.31%) falls in high susceptible zone and 89 (68.46%) in very high susceptible zone, whereas LSZ map prepared by LSA model shows that out of 130 landslides inventory data, 11 (8.5%) of the landslides fall in very low susceptible zone, 12 (9.2%) in low susceptible zone, 15 (11.5%) in moderate susceptible zone, 25 (19.3%) in high susceptible zone and 67 (51.5%) in very high susceptible zone. The percent of existing landslide determined from LSZ maps prepared by SIM and LSA falls in high and very high susceptible zones were 81% and 71% respectively. The result of verification shows that areal distributions and occurrences of landslides were relatively comparable in both methods.

Application of Frequency Ratio and Logistic Regression Model in the Assessment of Landslide Susceptibility Mapping for Nilgiris District, Tamilnadu, India

This study aims to develop landslide susceptibility mapping for Nilgiris district of Tamil Nadu, India using GIS and remote sensing. The Northeast monsoons often trigger numerous landslides in the Nilgiris district of Tami Nadu, resulting in a high death toll and considerable property damage. The landslide causative factors namely elevation, slope, aspect, land use/land cover, rainfall, geology, geomorphology, soil, distance to lineament, distance to road, distance to stream, stream power index and topographic wetness index extracted from the spatial database were incorporated in the analysis, where the correlation between each parameter and landslides were computed using the frequency ratio model and logistic regression model. Finally, the resultant landslide susceptibility zonation maps were obtained from both the models, which were classified as very high, high, moderate, low and very low landslide susceptible zones. The results convey that about 8.78% and 23.22% of the study area were found to be prone to very high landslide susceptibility zones based on the frequency ratio and logistic regression model, respectively. The model validation was carried out using the Receiver Operating Characteristic (ROC) curve by correlating the information obtained from the field verification. The obtained Area Under the Curve (AUC) value of the frequency ratio and logistic regression model was 82.3% and 84.2%, respectively. Therefore, the landslide susceptibility index map of Nilgiris district is claimed to be valuable for the decision-makers to reduce the susceptibility associated with landslide hazard.

Impact of Main Boundary Thrust (MBT) on Landslide Susceptibility in Garhwal Himalaya: A Case Study

In Garhwal Himalayan region, seismicity plays a major role in inducing mass movement. Especially at places where active faulting occurs, multiple and retrogressive slope failures have been observed. The active tectonism of the Himalayan collision zone makes the whole mountain chain fragile and instable. In this context, an endeavor has been made in the present study to correlate the impact of Main Boundary Thrust (MBT) on the overall landslide susceptibility for a 74-km stretch of NH 7 in the Garhwal Himalaya, using statistical models of landslide susceptibility zonation (LSZ) mapping. The results of the study show that slope angle, road network and distance from MBT are the most crucial landslide causative parameters in the study area, with statistical contribution of 26%, 20% and 18%, respectively. The study also produces an accurate LSZ map for this stretch of NH 7, which shows that almost 30% of the area has high to very high degree of landslide susceptibility. The prepared LSZ map will be of practical significance for institutionalizing any future landslide mitigation programs in the study area.

Landslide Susceptibility Zonation With Special Emphasis on Tectonic Features for Occurrence of Landslides in Lower Indian Himalaya

Landslide susceptibility zonation (LSZ) map is prepared for a part of the lower Himalayas using the fuzzy cosine amplitude method. The prepared LSZ map is validated both statistically and physically, and it is found that around 30% of the study area lies in zones of high to very high landslide susceptibility. To correlate the Himalayan seismicity with the landslide hazard, the prepared LSZ map is further assessed in terms of the impact of fault distance on the occurrence and spatial distribution of landslides in the study area. The results quantify a strong inverse correlation between fault distance and the probability of landslide occurrence. A high coefficient of determination (R2 = 0.986) indicates that the equation can realistically predict the impact of fault distance on the study area's landslide occurrence. Further, landslide relative frequency (LRF) method is used to evaluate the statistical contribution of fault distance toward overall landslide susceptibility for the study area. It is found that distance from any major tectonic feature is a significant landslide causative parameter, with a relative importance of 21%. The prepared LSZ map will be valuable for decision-makers during smart town planning and sustainable development in the study area.

Landslide Susceptibility Zonation Mapping Using Bivariate Statistical Frequency Ratio method and GIS: A Case Study in Part of SH 37 Ghat Road, Nadugani, Panthalur Taluk, The Nilgiris

Landslide is more universal calamity in mountain areas. It is a threat to life and socio-economy. The ghat section Calicut–Nilampur–Gudalur State Highway 37 (SH-37) comes under the Survey of India toposheet 58A/7. It is important road network to connect Calicut. The study area is covered in 6.5 km ghat section of 103/6 to 109/8 km stone on SH 37. This study was carried out to prepare landslide susceptibility zonation (LSZ) mapping on 1:50,000 scale using frequency ratio model. Seventeen parameters such as elevation, slope, slope aspect, curvature, road buffer, drainage buffer, lineament buffer, land use, geomorphology, run-off, drainage density, drainage frequency, lineament density, lineament frequency, weathering condition, soil thickness and geology were considered as landslide-inducing factors. LSZ map was organized by manipulating association between the landslide persuade factors and old landslide using this model. Study area has grouped into five levels of susceptibility groups such as very low, low, moderate, high and very high. The LSZ map was validated by the old landslide record data collected from field. The landslide inventory percentage fall in very low hazard is 1.02%, 1.03% under low-susceptibility, 7.22% in moderate-susceptibility, 39.18% present in high-susceptibility and 51.55% noticed in very high-susceptibility zone, and using these data, the 18 rock vulnerable cut slopes were identified.