Slope Units Research Articles

Landslide dynamic hazard prediction based on precipitation variation trend and backpropagation neural network

The assessment of landslide hazards is crucial for disaster prevention and mitigation, but it has not considered the dynamic influencing factors that trigger landslides. The timeliness and practical value of the assessment results still need to be further improved. This study constructed a dynamic landslide hazard assessment system using information value model, dynamic precipitation data, and Backpropagation Neural Network (BPNN) model. Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. It is predicted that the percentage of very high and high areas in 2025 and 2030 will be 50.5% and 57.5% respectively.

An integrated approach for mapping slow-moving hillslopes and characterizing their activity using InSAR, slope units and a novel 2-D deformation scheme

An integrated approach for mapping slow-moving hillslopes and characterizing their activity using InSAR, slope units and a novel 2-D deformation scheme

Slope unit-based genetic landform mapping on Tibetan plateau- a terrain unit-based framework for large spatial scale landform classification

Genetic landform classification and mapping provide crucial information concerning the formation and evolution of the earth surface, aiding in understanding geological processes, climate variation, and driving mechanisms of earth dynamics. Conventional pix and object unit-based research paradigms poss challenges in classifying and mapping the genetic landform on a large spatial scale, while the terrain unit-based method offers an inspiring opportunity for this typical landform task. In this context, we herein developed a slope unit (SU)-based landform classification framework, involving improving the mean curvature method for SU segmentation, developing the spectra method to determine the minimal area size of SU, introducing a specific quantitative indicator system for describing SU, and a universal classification model based on XGBoost. Using the entire Tibetan Plateau as the study area, we first implemented the genetic landform map and divided it into nine genetic landform types including aeolian, arid, loess, karst, periglacial, fluvial, glacial, volcanic-lava, and lacustrine. Classification results achieved an encouraging performance with overall accuracy, kappa coefficient, and mean accuracy of up to 90.2%, 84.71%, and 88.65%, respectively. Experimental results suggested that delineated genetic landform regions and their boundaries are generally closely correlated with regional climatic conditions, geological factors, or surface processes. Compared to pix or object-unit-based methods, the SU delineation results possess internal homogeneity and clear terrain boundaries with geo-meaning. Compared to the watershed-unit-based method, our method is better suited for large-scale landform classification. In the end, we confirmed that the proposed framework can be extended to large-scale planetary genetic landform mapping via two case studies on Mars and Moon, which is transferable yet straightforward. In brief, we argued that the proposed framework offered a powerful and effective method in large-scale genetic landform classification, somewhat effectively circumventing the limitations of existing landform classification methods.

Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be

The geoscientific community primarily focuses on predicting where landslides are likely to occur through data-driven susceptibility models. Recently, few researchers have turned to statistical estimation of landslide planimetric area within a given terrain unit and exploration of the spatiotemporal distribution of landslide occurrence. However, these data-driven approaches cannot fulfill the commonly accepted definition of landslide hazard and cannot predict the location, time/frequency, and magnitude of landslide occurrence simultaneously. This study proposes a unified and data-driven framework for landslide hazard spatiotemporal modeling, enabling dynamic and probabilistic estimation of landslide occurrence within a given slope unit at a specific time period and for a specific landslide magnitude. This framework not only involves static and dynamic factors in modelling, but also considers spatial and temporal interactions to explore the spatiotemporal variation effects of landslide hazard. We test this framework on the main island of Taiwan with a multi-temporal landslide inventory from 2004 to 2018. Specifically, this framework assumes that the occurrence and size of landslides spatiotemporally follows a binomial and a Log-Gaussian distribution, respectively, and then uses generalized additive models to achieve the estimation of landslide hazard probability. Finally, the performance is validated by a spatiotemporal leave-one-out cross-validation scheme. We believe that this framework will lay the foundation for the community to estimate landslide hazard in a unified and probabilistic data-driven prototype. We envision it could lead to studies of dynamic hazard responses to climate change.

A novel CGBoost deep learning algorithm for coseismic landslide susceptibility prediction

The accurate prediction of landslide susceptibility shortly after a violent earthquake is quite vital to the emergency rescue in the 72-h “golden window”. However, the limited quantity of interpreted landslides shortly after a massive earthquake makes landslide susceptibility prediction become a challenge. To address this gap, this work suggests an integrated method of Crossing Graph attention network and xgBoost (CGBoost). This method contains three branches, which extract the interrelations among pixels within a slope unit, the interrelations among various slope units, and the relevance between influencing factors and landslide probability, respectively, and obtain rich and discriminative features by an adaptive fusion mechanism. Thus, the difficulty of susceptibility modeling under a small number of coseismic landslides can be reduced. As a basic module of CGBoost, the proposed Crossing graph attention network (Crossgat) could characterize the spatial heterogeneity within and among slope units to reduce the false alarm in the susceptibility results. Moreover, the rainfall dynamic factors are utilized as prediction indices to improve the susceptibility performance, and the prediction index set is established by terrain, geology, human activity, environment, meteorology, and earthquake factors. CGBoost is applied to predict landslide susceptibility in the Gorkha meizoseismal area. 3.43% of coseismic landslides are randomly selected, of which 70% are used for training, and the others for testing. In the testing set, the values of Overall Accuracy, Precision, Recall, F1-score, and Kappa coefficient of CGBoost attain 0.9800, 0.9577, 0.9999, 0.9784, and 0.9598, respectively. Validated by all the coseismic landslides, CGBoost outperforms the current major landslide susceptibility assessment methods. The suggested CGBoost can be also applied to landslide susceptibility prediction in new earthquakes in the future.

From spatio-temporal landslide susceptibility to landslide risk forecast

The literature on landslide susceptibility is rich with examples that span a wide range of topics. However, the component that pertains to the extension of the susceptibility framework toward space–time modeling is largely unexplored. This statement holds true, particularly in the context of landslide risk, where few scientific contributions investigate risk dynamics in space and time. This manuscript proposes a modeling protocol where a dynamic landslide susceptibility is obtained via a binomial Generalized Additive Model whose inventories span nine years (from 2013 to 2021). For the analyses, the data cube is organized with a mapping unit consisting of 26,333 slope units repeated over an annual temporal unit, resulting in a total of 236,997 units. This phase already includes several interesting modeling experiments that have rarely appeared in the landslide literature (e.g., variable interaction plots). However, the main innovative effort is in the subsequent phase of the protocol we propose, as we used climate projections of the main trigger (rainfall) to obtain future estimates of yearly susceptibility patterns. These estimates are then combined with projections of urban settlements and associated populations to create a dynamic risk model, assuming vulnerability = 1. Overall, this manuscript presents a unique example of such a modeling routine and offers a potential standard for administrations to make informed decisions regarding future urban development.

Optimizing slope unit-based landslide susceptibility mapping using the priority-flood flow direction algorithm

Regional-scale landslide susceptibility assessments are challenging and have recently captured the attention of scientists and technicians. A slope unit is a typical map unit that enhances the readability of landslide susceptibility maps (LSMs) by reducing ridge effects. Slope units are commonly obtained via the subbasin division (SBD) method from a digital elevation model (DEM) containing sink features, such as depressions, pits and flats. The SBD method addresses the depression problem by filling features but subsequently faces unresolved problems with parallel slope-unit boundaries. The main objective of this study was to introduce the priority-flood flow direction (PFD) algorithm to exploit flow information for depressions and compare PFD-improved slope units to SBD-based slope units in corresponding LSMs. An LSM case study was generated using an artificial neural network in Yan’an city, China, where loess ridge and hill landforms are developed. Data for a total of 447 landslides were obtained from historical records and field surveys to construct a landslide inventory map, where 313 landslides (70%) and 134 landslides (30%) were used for training and validation, respectively. Then, 9 conditioning factors related to landslides were prepared as inputs to generate six LSMs by combining two map units and three catchment area thresholds. These factors included elevation, slope, river distance, road distance, rainfall, normalized difference vegetation index (NDVI), aspect, land use and lithology. Finally, the resulting LSMs were validated and compared between the PFD and SBD methods using the precision, recall, accuracy, F1 score, success rate and prediction rate based on receiver operating characteristic (ROC) and cumulative landslide occurrence (CLO) curves. The results showed that the improved slope units produced no parallel boundaries, and the environment-factor layers generated based on these slope units prevented the generation of parallel factor-subclass boundaries, thus reducing striped distributions of factors and optimizing patterns in the LSMs. The PFD-improved LSM yielded ROC-based success rates ranging from 0.8849 to 0.8901, which were better than those obtained for the SBD-based LSM, at 0.8779–0.8823. The prediction rate revealed that the PFD-improved slope units were more suitable for large regions, as they provide more stable performance than the SBD-based slope units at large scales. The PFD algorithm resolved the problem of parallel boundaries and optimized LSM patterns, resulting in enhanced performance over the SBD method in large-scale research. The study results could be useful for local government management and land use planning in large regions.

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

Landslide susceptibility prediction (LSP) is the basis for risk management and plays an important role in social sustainability. However, the modeling process of LSP is constrained by various factors. This paper approaches the effect of landslide data integrity, machine-learning (ML) models, and non-landslide sample-selection methods on the accuracy of LSP, taking the Yinghu Lake Basin in Ankang City, Shaanxi Province, as an example. First, previous landslide inventory (totaling 46) and updated landslide inventory (totaling 46 + 176) were established through data collection, remote-sensing interpretation, and field investigation. With the slope unit as the mapping unit, twelve conditioning factors, including elevation, slope, aspect, topographic relief, elevation variation coefficient, slope structure, lithology, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), distance to road, distance to river, and rainfall were selected. Next, the initial landslide susceptibility mapping (LSM) was obtained using the K-means algorithm, and non-landslide samples were determined using two methods: random selection and semi-supervised machine learning (SSML). Finally, the random forest (RF) and artificial neural network (ANN) machine-learning methods were used for modeling. The research results showed the following: (1) The performance of supervised machine learning (SML) (RF, ANN) is generally superior to unsupervised machine learning (USML) (K-means). Specifically, RF in the SML model has the best prediction performance, followed by ANN. (2) The selection method of non-landslide samples has a significant impact on LSP, and the accuracy of the SSML-based non-landslide selection method is controlled by the ratio of the number of landslide samples to the number of mapping units. (3) The quantity of landslides has an impact on how reliably the results of LSM are obtained because fewer landslides result in a smaller sample size for LSM, which deviates from reality. Although the results in this dataset are satisfactory, the zoning results cannot reliably anticipate the recently added landslide data discovered by the interpretation of remote-sensing data and field research. We propose that the landslide inventory can be increased by remote sensing in order to achieve accurate and impartial LSM since the LSM of adequate landslide samples is more reasonable. The research results of this paper will provide a reference basis for uncertain analysis of LSP and regional landslide risk management.

Research on blasting mechanism and blasting effect of aqueous media in open pit coal mines

Surface coal mining procedures include piercing—blasting—mining and loading—transportation—discharging, blasting link exists due to the poor blasting effect leads to low loading efficiency, blasting dust caused by environmental pollution and other problems. In this paper, from the mechanical characteristics of the water medium, we analyze in detail the transferring effect, transducing effect and bubble pulsation phenomenon of the water medium in the blasting process. The results show that when the blasting medium is water medium, the maximum principal stress is 1.53 times that of air medium; the peak energy transfer can be up to 2.73 times that of air medium. With the help of TrueGrid/LS-DYNA finite element analysis software to simulate the dynamic process of blasting, the study of the maximum principal stresses around the hole, the top of the slope, the foot of the slope on the maximum principal stress changes, the results show that the maximum principal stresses around the hole, the top of the slope, the foot of the slope unit with the increase in the water content is gradually increasing trend. Finally, combined with the actual mine production conditions for blasting field test, water-mediated blasting dust reduction rate of 75%, the use of AHP—fuzzy comprehensive evaluation method of two groups of traditional dry hole blasting and three groups of water-mediated blasting comprehensive evaluation, the results show that the water-mediated blasting scores are higher than the traditional dry hole blasting, proving that the water-mediated blasting has a certain prospect of engineering applications.

Susceptibility Assessment of Landslides in the Loess Plateau Based on Machine Learning Models: A Case Study of Xining City

Landslide susceptibility assessment can effectively predict the spatial distribution of potential landslides, which is of great significance in fields such as geological disaster prevention, urban planning, etc. Taking Xining City as an example, based on GF-2 remote sensing image data and combined with field survey data, this study delineated the spatial distribution range of developed landslides. Key factors controlling landslides were then extracted to establish a landslide susceptibility assessment index system. Based on this, the frequency ratio (FR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models were applied to spatially predict landslide susceptibility with slope units as the basis. The main results are as follows: (1) The overall spatial distribution of landslide susceptibility classes in Xining City is consistent, but the differences between different landslide susceptibility classes are significant. (2) The high-susceptibility area predicted by the FR-RF model is the largest, accounting for 15.48% of the total study area. The prediction results of the FR-ANN and FR-SVM models are more similar, with high-susceptibility areas accounting for 13.96% and 12.97%, respectively. (3) The accuracy verification results show that all three coupled models have good spatial prediction capabilities in the study area. The order of landslide susceptibility prediction capabilities from high to low is FR-RF model > FR-ANN model > FR-SVM model. This indicates that in the study area, the FR-RF model is more suitable for carrying out landslide susceptibility assessment.

Application of AHP-ICM and AHP-EWM in Collapse Disaster Risk Mapping in Huinan County

Collapses are one of the most common geological disasters in mountainous areas, which easily damage buildings and infrastructures and bring huge property losses to people’s production and life. This paper uses Huinan County as the study area, and with the help of a geographic information system (GIS) based on the formation principle of natural disaster risk, the information content method (ICM), the analytical hierarchy process (AHP), and the analytical hierarchy process–information content method (AHP-ICM) model are applied to hazard mapping, and the analytical hierarchy process-entropy weight method (AHP-EWM) model is applied to exposure, vulnerability and emergency responses, and recovery capability mapping. A risk mapping model for collapse disasters was also constructed using these four elements. Firstly, an inventory map of 52 landslides was compiled using remote sensing interpretation, field verification, and comprehensive previous survey data. Then, the study area mapping units were delineated using the curvature watershed method in the slope unit, and 21 indicators were used to draw the collapse disaster risk zoning map by considering the four elements of geological disaster risk. The prediction accuracy of the three hazard mapping models was verified using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC) results of the AHP, ICM, and AHP-ICM models were 80%, 85.7%, and 87.4%, respectively. After a comprehensive comparison, the AHP-ICM model is the best of the three models in terms of collapse hazard mapping, and it was applied to collapse risk mapping with the AHP-EWM model to produce a reasonable and reliable collapse risk zoning map, which provides a basis for collapse management and decision making.

Different slope units division-based geohazard susceptibility evaluation of support vector machine optimized by sparrow search algorithm

Different slope units division-based geohazard susceptibility evaluation of support vector machine optimized by sparrow search algorithm

Experimental study on the reinforcement mechanism and wave thumping resistance of EICP reinforced sand slopes

Sand slope is an important part of coastal zone and islands, which is severely affected by wave erosion and causes problems such as degradation of coastal zone and reduction of island area. Enzyme-induced calcium carbonate precipitation (EICP) technology is a new reinforcement technology with environmental friendly and excellent effect, which has been widely studied in the field of geotechnical engineering in recent years. In this research, we focus on the coastal or reef sand slopes in marine environments. The EICP reinforcement of representative sand slope units and large scale flume wave thumping experimental study are conducted indoors. By analyzing the physical and mechanical properties, erosion resistance, and microstructure of EICP-reinforced sand slopes, the mechanism of EICP reinforced sand slopes is revealed, the feasibility of EICP reinforced sand slopes is confirmed, and a feasible solution for EICP reinforced sand slopes is finally obtained. Results show that: (1) EICP reinforcement effectively enhances the surface strength and erosion resistance of sand slopes. Higher calcium carbonate content in the sand slopes corresponds to greater surface strength and improved erosion resistance. When the calcium carbonate content is similar, using low-concentration reinforcement twice is more advantageous than using high-concentration reinforcement once due to its superior uniformity. (2) The intensity of waves, the angle of the sand slope, and the severity of erosion damage are interrelated. Higher wave intensity, steeper sand slope angles, and more serious erosion damage require stronger reinforcement measures. (3) Scanning Electron Microscope (SEM) image analysis reveals that the reinforcing effect of sand slopes primarily depends on the amount of calcium carbonate crystals cemented between sand particles. A higher content of calcium carbonate crystals leads to better erosion resistance in the sand slope.

An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm

InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter’s experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation.

Using Public Landslide Inventories for Landslide Susceptibility Assessment at the Basin Scale: Application to the Torto River Basin (Central-Northern Sicily, Italy)

In statistical landslide susceptibility evaluation, the quality of the model and its prediction image heavily depends on the quality of the landslide inventories used for calibration. However, regional-scale inventories made available by public territorial administrations are typically affected by an unknown grade of incompleteness and mapping inaccuracy. In this research, a procedure is proposed for verifying and solving such limits by applying a two-step susceptibility modeling procedure. In the Torto River basin (central-northern Sicily, Italy), using an available regional landslide inventory (267 slide and 78 flow cases), two SUFRA_1 models were first prepared and used to assign a landslide susceptibility level to each slope unit (SLU) in which the study area was partitioned. For each of the four susceptibility classes that were obtained, 30% of the mapping units were randomly selected and their stable/unstable status was checked by remote analysis. The new, increased inventories were finally used to recalibrate two SUFRA_2 models. The prediction skills of the SUFRA_1 and SUFRA_2 models were then compared by testing their accuracy in matching landslide distribution in a test sub-basin where a high-resolution systematic inventory had been prepared. According to the results, the strong limits of the SUFRA_1 models (sensitivity: 0.67 and 0.57 for slide and flow, respectively) were largely solved by the SUFRA_2 model (sensitivity: 1 for both slide and flow), suggesting the proposed procedure as a possibly suitable modeling strategy for regional susceptibility studies.

Semi-automated classification of layered rock slopes using digital elevation model and geological map

Abstract Layered rock slopes are the most widely distributed slopes with the simplest structure. The classification of layered rock slopes is the basis for correctly analyzing their deformation and failure mechanisms, evaluating their stability, and adopting reasonable support methods. It is also one of the essential indicators to support the evaluation of urban and rural construction suitability and the assessment of landslide hazards. However, the present-day classification methods for layered rock slopes are not sufficiently automated. In the application process of these methods, a lot of manual intervention is still needed, and sufficient strata orientation data obtained through field surveys is required, which is not effective for large-scale applications and involves high subjectivity. Thus, this study proposes a semi-automated classification method for layered rock slopes based on digital elevation model (DEM) and geological maps, which greatly reduces human intervention. On the basis of slope unit division, the method extracts structural information of slopes using DEM and geological maps and classifies slopes according to their structural characteristics. An experiment has been carried out in the northern region of Mount Lu in Jiangxi Province, and the results demonstrate the effectiveness of this semi-automated classification method. Compared to the existing manual or semi-automated classification methods, the method proposed in this article is objective and highly automated, which can meet the requirements of classification of layered rock slopes over large areas, even in the case of sparse measured orientation data.

Terminal Ediacaran–Late Ordovician evolution of the NE Laurentia palaeocontinent: rift–drift–onset of Taconic Orogeny, sea-level change and ‘Hawke Bay’ onlap (not offlap)

Abstract Rodinia break-up with late Ediacaran rifting defined a NE Laurentia triple junction (New York Promontory–Ottawa–Bonnechere aulacogen (OBA)–Quebec Reentrant). Rifting persisted to c. 510 Ma. The oldest passive-margin shelf units (Forestdale Marble and Moosalamoo Phyllite) underlie a sandstone (Cheshire) commonly regarded as the oldest passive unit. Late Dyeran–Middle Cambrian rifting led to the oldest OBa sedimentation and formed the Franklin Basin (NW Vermont). Cambrian–Darriwillian shelf–slope facies are linked eustatically – not Taconic Orogeny onset. Onlap and shelf carbonates are coeval with black slope mud; and lowstand shelf unconformities with green, oxic slope mud. Early–middle Dyeran eustatic change defined slope units: (1) Browns Pond Formation dysoxic–anoxic (d–a) interval with debrite cap (Holcombville Member, new); (2) Middle Granville Formation Oxic Interval (new); and (3) lower Hatch Hill Formation d–a interval. Our analysis leads to two controversial conclusions: (i) the existence of the Dashwoods and other micro-continental blocks due to hyperextension is not supported by cover sequences linking Laurentia to proposed Dashwoods areas (i.e. Green Mountains) and an arc origin of the type Dashwoods; and (ii) ‘Hawke Bay Event(s)’, widely interpreted as Cambrian global regressive event(s), is a local highstand systems tract facies with shelf sand bypass onto the Hatch Hill Formation slope in its NE Laurentia type region.

A novel QLattice‐based whitening machine learning model of landslide susceptibility mapping

AbstractLandslide susceptibility mapping (LSM) enables the prediction of landslide occurrences, thereby offering a scientific foundation for disaster prevention and control. In recent years, numerous studies have been conducted on LSM using machine learning techniques. However, the majority of machine learning models is considered “black box” models due to their lack of transparent explanations. In contrast, the QLattice model serves as a white box model, as it can elucidate the decision‐making mechanism while representing a novel approach to whitening machine learning models. QLattice possesses the capability to automatically select and scale data features. In this study, Fengjie County in China was selected as the research area, with slope units serving as evaluation units. A geospatial database was constructed using 12 conditioning factors, including elevation, slope, and annual average rainfall. LSM models were conducted using both the QLattice and random forest (RF) algorithms. The findings demonstrate that the QLattice model achieved an area under curve value of 0.868, while the RF model attained an area under curve value of 0.849 for the test datasets. These results highlight the superior predictive ability and stability of the QLattice model compared with RF. Furthermore, QLattice can explicate and clarify the change processes of conditioning factors, thereby revealing the internal decision‐making mechanism and causes behind the LSM model's decisions. The innovative QLattice‐based model provides new ideas and methodologies for LSM research.

Morphological characteristics and evolution model of slope units along loess gully cross section

Morphological characteristics and evolution model of slope units along loess gully cross section

A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines

Preparation of pipeline risk zoning is essential for pipeline construction and safe operation. Landslides are one of the main sources of risk to the safe operations of oil and gas pipelines in mountainous areas. This work aims to propose a quantitative assessment model of landslide-induced long-distance pipeline risk by analyzing historical landslide hazard data along oil and gas pipelines. Using the Changshou-Fuling-Wulong-Nanchuan (CN) gas pipeline dataset, two independent assessments were carried out: landslide susceptibility assessment and pipeline vulnerability assessment. Firstly, the study combined the recursive feature elimination and particle swarm optimization-AdaBoost method (RFE-PSO-AdaBoost) to develop a landslide susceptibility mapping model. The RFE method was used to select the conditioning factors, while PSO was used to tune the hyper-parameters. Secondly, considering the angular relationship between the pipelines and landslides, and the segmentation of the pipelines using the fuzzy clustering (FC), the CRITIC method (FC-CRITIC) was combined to develop a pipeline vulnerability assessment model. Accordingly, a pipeline risk map was obtained based on pipeline vulnerability and landslide susceptibility assessment. The study results show that almost 35.3% of the slope units were in extremely high susceptibility zones, 6.68% of the pipelines were in extremely high vulnerability areas, the southern and eastern pipelines segmented in the study area were located in high risk areas and coincided well with the distribution of landslides. The proposed hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines can provide a scientific and reasonable risk classification for new planning or in service pipelines to avoid landslide-oriented risk and ensure their safe operation in mountainous areas.