
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13383-X
10.1016/j.heliyon.2024.e37352
e37352
Research Article
Landslide risk assessment combining kernel extreme learning machine and information value modeling-A case study of Jiaxian Country of loess plateau, China
Wang Youxiang wangyx_1986@163.com
⁎
Kang Liangqiang
Wang Jianping
Shaanxi institute of Geological Exploration, Sinochem general administration of geology and mines, Xi'an, 710000, China
⁎ Corresponding author. wangyx_1986@163.com
03 9 2024
15 9 2024
03 9 2024
10 17 e3735226 3 2024
1 9 2024
2 9 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Landslide risk mapping can be an effective reference for disaster mitigation and land use planning, but the modelling process involves multidisciplinary knowledge which leads to its complexity. In this study, Jiaxian County in Shaanxi Province on the Loess Plateau of China, served as the study area, primarily characterized by Quaternary loess-covered geomorphology, with an average rainfall of about 400 mm annually. Soil erosion and human engineering activities have contributed to significant slope failures, posing threats to local residents and infrastructure. A reasonable inventory of landslides in the region was established by field survey combined with aerial imagery, allowing for characterization of their development and spatial distribution. Nine thematic maps related to landslide occurring and three vulnerability maps were prepared as influencing factors for landslide risk assessment. Subsequently, landslide susceptibility and hazard were evaluated using a kernel extreme learning machine (KELM) and information value (IV) model, followed by map validation. A decision table was then employed to generate the landslide risk map. The results of landslide hazard mapping showed that the historical landslide events were mainly developed in the central part of the study area, particularly concentrated near the developed river network. Integration of overall risk elements suggested that landslide risks in the study area were generally at a low level. Besides, a total of 0.25 % and 2.05 % of the areas were classified as having very high and high landslide risk levels, respectively, where 65.11 % of inventory landslides occurred. Therefore, the proposed procedure is a valuable tool for assessing landslide risk in Jiaxian Country.

Keywords

Loess landslide
Landslide risk
Kernel extreme learning machine
Information value model
Spatial characteristics
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pmc1 Introduction

Landslides are one of the most common and costly geological events, causing suffering to many communities around the world [1,2]. External triggers that are known to trigger landslides include intense rapid rainfall events, seismic activity and human engineering activities [[3], [4], [5]]. In China, one-third of the geohazards are occurred on the Loess Plateau, among them, a total of 85 % are landslides [6]. Hence, the region of Loess Plateau is considered one of the most devastating geohazards in China. According to local government survey, over the past decade, several deadly landslides have been reported in this region and 293 people were killed or injured due to geological disasters in Shaanxi Province, and landslides were the most significant disaster causing casualties. Landslide risk prediction is an important tool for accurate positioning of potential landslides and risk reduction. Hence, in recent years, landslide risk assessment has become an essential means for landslide disaster risk management and control [7].

Landslide risk refers to the potential loss or damage to the exposed elements (e.g., population, infrastructure) resulting from landslide events [8,9] It consists of two components: the likelihood of a landslide occurring, and the potential consequences of a landslide. Regarding to likelihood of a landslide occurring, on the one hand, it contains the likelihood that a landslide will occur in a specific area based on inherent characteristics such as geological, topographical and land cover, which is commonly called landslide susceptibility [10,11]. On the other hand, based on the landslide susceptibility, the probability and potential intensity of landslide events occurring with a specified time frame, called landslide hazard [12]. Besides, the degree of vulnerability for exposure elements is also considered aspect for landslide risk assessment [13]. By assessing the degree of risk in an area, we can identify high-risk zones, prioritize mitigation measures, and support land-use planning decisions. A variety of techniques and models have been developed to assess landslide risk. These methods involve the integration of geospatial data, remote sensing, and geographical information systems (GIS) for modeling and mapping landslide risk areas [14,15]. Hence, the basic and first step in assessing landslide risk is to establish a complete and reliable landslide inventory, which can be typically grouped to historical and event-triggered landslides [3,16,17]. Techniques widely used for landslide investigation on a regional scale include manual field surveys and non-contact surveys based on remote sensing data [18,19]. Earlier landslide surveys were mainly manual, but they were time consuming, difficult to perform in practice, and sometimes life-threatening in certain high mountainous areas. In recent years, with the development of radar technology, machine vision and unmanned aerial vehicle (UAV) [20,21], some non-contact landslide survey methods have been widely used. Furthermore, several advanced semi-automated or automated landslide mapping and inventory tools have been developed based on machine learning or deep learning [21,22]. Nevertheless, the most widely accepted approach is the hybrid method, which ensures both accuracy and efficiency through a survey process that combines the interpretation of remote sensing data with field verification. For instance, Pei et al. [19] compiled a comprehensive landslides inventory in the Taxkorgan River Basin by multi-source remote images, UAV flights, and conducting field investigations.

Understanding the factors contributing to landslide occurrence and determining their hazard is crucial for effective risk management and mitigation strategies. Landslide hazard assessment aims to predict the spatial-temporal likelihood of landslide occurrence in a given area by analyzing various controlling factors and employing suitable methodologies [23,24]. There is rich literature on modelling hazard of geomorphic, e.g. since the early stages of inventory analysis [25] and, with technological advances, to deterministic [26], statistical [27], machine learning and deep learning [28,29] technology. It can be concluded that the last three categories of data-driven models now represent the vast majority and popular of methods in the field of hazard studies [30,31]. The statistical approaches such as weights of evidence and generalized additive models prioritize parameter estimation and inference, as their outcomes derive directly from empirical observations [29,32]. Statistical approaches can obtain relatively robust prediction results [33,34], but it is not easy to find new relationships between influencing factors and results. Machine learning and deep learning models, based on algorithms and data mining methods, can construct nonlinear relationships achieving competitive results compared to statistical methods [35]. Extreme learning machine (ELM) uses a single hidden layer feedforward neural network [36] with randomly assigned input weights and analytically determined output weights, and it has demonstrated good performance in landslide susceptibility assessment [37,38]. Kernel extreme learning machine (KELM) provides an additional advantage by integrating kernel functions [39]. In this study, we proposed a hybrid scheme for assessing landslide risk, including landslide susceptibility assessment based on KELM and landslide hazard assessment based on information value (IV) [40] model. Finally, the results of susceptibility and hazard assessments were combined to generate a landslide risk map for the study area using a decision table.

The present study purposes to generate a reliable landslide risk map at a regional scale of Jiaxian Country. Jiaxian County located on the Loess Plateau (China) has experienced various landslide hazards throughout the past few decades. Therefore, this study is of great practical significance for the systematic implementation of landslide risk procedures and their application, especially for the guidance of landslide disaster risk management and land use planning in Jiaxian County. Specifically, our objective mainly includes: (i) the field survey combined with multi-temporal remote sensing imagery to determine a detailed landslide inventory for the study area, (ii) the achievement of landslide susceptibility and hazard zonation by the KEL and IV algorithms, and (iii) calculating landslide risk by overlaying landslide hazard map and susceptibility map in Jiaxian Country.

2 Study area

Jiaxian Country is located in the northwestern of Shanxi Province, China, longitudes ranging from 110°00′ E to 110°45′E and 37°41′ to 38°23′ N latitude (Fig. 1a). The north-south length of the study area is approximately 85 km and the east-west length reaching 23.9 km, with a total area of 2030 km2 (Fig. 1b). Jiaxian Country is located in the middle reaches of the Yellow River Basin, and the rivers in its territory belong to the tributaries of the Yellow River system [41]. Jiaxian County is located in the hinterland of the Loess Plateau, with a high terrain in the northwest and a low terrain in the southeast, with an altitude between 675 and 1339.5 m. The relative height difference between the north and south terrain is about 664 m. The study area mainly develops the Middle and Cenozoic strata, including Triassic, Neoproterozoic and Quaternary, among which the Quaternary loess is the most widely distributed, almost covering whole of the region.Fig. 1 The location of the study area. (a) the location of the study area in China, (b) the remote sensing image of the study area.

Fig. 1

Jiaxian Country is a continental semi-arid monsoon climate zone, with long, cold winters and little rain, whereas short summers with large temperature differences and little precipitation. Severe land droughts last from late spring to early summer, but adequate precipitation occurrence in summer and autumn annually (Fig. 2). Specifically, heavy rainfall in a hydrological year is mainly concentrated from July to August, accounting for about 55 % of the cumulative annual precipitation (Fig. 2b). Multi-year climate statistics (from 1980 to 2020) show that the average annual temperature in Jiaxian Country is 10.4 °C, and the average annual rainfall is between 300 and 500 mm. The minimum annual precipitation of 273 mm was observed in 2005 and the maximum precipitation of 784 mm was recorded in 2012. During the period from 1980 to 2005, the annual precipitation was concentrated in the range of 300–500 mm/a (Fig. 2a). However, significant inter-annual fluctuations in precipitation have been observed since 2005. Besides, there has been an overall increasing trend in precipitation since 2005, with a trend slope of 8.16 mm per year. Furthermore, in terms of spatial distribution, the multi-year average annual precipitation in Jiaxian County decreases roughly from east to west and increases from north to south. In the northern, annual precipitation is generally below 400 mm, while this value above 400 mm in southern.Fig. 2 The spatial-temporal distribution of rainfall in the study area. (a) the average annual rainfall during 1980–2020, (b) the average monthly rainfall, (c) the spatial distribution of average annual rainfall in the study area.

Fig. 2

3 Material and methods

3.1 Landslide inventory

Landslide inventories are the basis for landslide hazard assessments and typically include the information of location, data of occurrence, landslide attributes (area, volume), and triggering factors [42]. This landslide investigation lasted a total of three months and used a combination of high-resolution optical remote sensing imagery and geo-geological base data to effectively interpret the potential deformation zone (Fig. 3a–c). Specifically, remote imagery between 2019 and 2021 were extracted from Google Earth, with spatial resolution of the ground is 0.14 m. The UAV images were captured on March 25, 2022, and May 4, 2022, with average ground sampling distance was about 4 cm per pixel. Subsequently, further investigations were conducted through detailed field surveys for poorly stabilized and highly hazardous hazard sites. Finally, 129 landslide sites were identified in the study area, including 36 (27.91 %) earth slide, 92 (71.32 %) avalanches, and 1 (0.77 %) earth flow (Fig. 3d) [43]. Then a spatial database of landslide hazards was established based on a GIS platform. In general, the landslide disasters in the study area are dominated by small loess landslides, of which 116 are small landslides and 13 are medium-sized landslides. Besides, this landslide inventory was randomly divided into training set (70 %) and testing set (30 %) for following research.Fig. 3 Preparation of the landslide inventory in the Jiaxian County. (a) and (b) loess landslides, (c) an avalanche disaster site, and (d) the types and proportion of landslide.

Fig. 3

3.2 Factors related to landslide hazards

Landslide disaster-prone zones are areas where there is a significant possibility of landslide disasters occurring. Therefore, aiming at evaluating of landslide risk, the selection of influencing factors affecting the occurrence and development of landslide disasters should refer the spatial distribution pattern of landslides as well as the conditions of disaster-prone geological environment in the study area. In this evaluation of the landslide hazard of Jiaxian County, based on the analysis of the conditions for the formation of geological hazards, the nine factors of slope, aspect, curvature, slope height, stratigraphy lithology, geomorphological unit type, the distance from the water system, rainfall and intensity of human activities were selected as the evaluation indexes for the landslide hazard zoning (Fig. 4a–i). These nine selected factors encompass critical aspects related to landslide occurrence, including geomorphological characteristics, geological, environmental conditions, hydrological, and human activities and are also commonly applied to loess landslide hazard assessment [44,45].Fig. 4 Maps of the nine selected evaluation factors for creation a landslide hazard zonation map. (a) slope, (b) aspect, (c) curvature, (d) slope height, (e) stratigraphy lithology, (f) geomorphological, (g) distance from rivers, (h) rainfall, (i) intensity of human activities.

Fig. 4

The slope is an important control factor for the occurrence of loess collapse and landslide disasters. On different slopes, the type, scale and degree of hazard of collapse and landslide are different. According to the results of previous research, the loess plateau area in northern shaanxi Province, more than 80 % of landslides occur in the slope angle greater than 30° of the slope section [46].

Slopes have regular differences in climatic, thermal and hydrological conditions due to their orientation. Sunny slopes are exposed to longer hours of sunshine than shady slopes, with strong solar radiation, higher air and soil temperatures, and larger daily differences in temperature [47]. The difference in water and heat conditions between the sun and the shade leads to differences in water content, weathering degree, slope gradient and other elements of the slope. Hence, the aspect can also affect the stability of slopes.

Slope type refers to the morphology of surface slopes and can be classified into three basic types: straight, convex and concave slopes [48]. The slope type can be described and quantified by the curvature P of the ground surface in the GIS model. The slope type is defined as a convex slope when the surface curvature values of P > 0; straight slope when the surface curvature P = 0; and concave slope when the surface curvature P < 0. In this study, the surface curvature was extracted by the digital elevation model (DEM). In order to make the calculation results easier to display, the definition of slope type is modified as follows after several trial calculations and combined with the actual survey data: when P ≥ 0.5, the slope type is defined as convex; when −0.5<P < 0.5, the slope type is straight; when P ≤ −0.5, the slope type is concave [49].

Slope height can have important effects on the stability of slopes [50]. Firstly, although slope height does not change the distribution of stress within a slope, it does control the magnitude of stress values within the slope. As the slope height increases, the stress values increase significantly. Secondly, different slope heights lead to different temperature distributions, which in turn affects the surface vegetation cover [51]. Different slope heights also indirectly affect the intensity of human activities. Another important role is that the slope height affects the potential energy of the slope, which has a significant impact on the occurrence of disasters and the degree of this hazard. The slope height of slopes over the study area is classified into 5 levels based on DEM (Fig. 4d), which are 0–10m, 10m–20m, 20m–30m, 30m–40m and >40m in that order.

In our study, we classified the rock masses into hard rock and weak rock groups following the engineering rock classification standards. We then determined the stratigraphic information based on the formation times of the strata. This process was supported by the analysis of the stratigraphy, sediment characteristics, and fossil content. The stratigraphy the study area (Fig. 4e) mainly consists of Quaternary wind-accumulated loess (Q3, Q2), Neoproterozoic red clay (N2), and medium-thickness layers of soft and hard interbedded clastic rock groups (T3h, T2t, T3y, T3w). Quaternary loess, which is closely related to geological hazards occurrence, and is widely distributed in the study area. The loess is homogeneous in texture, contains a lot of calcareous or loess nodules, is porous, has significantly developed vertical joints and has less laminations. The soil of loess is relatively hard when present dry state, and usually easy to spall and suffer from erosion, or even collapse, when it is soaked with running water.

Geomorphology is a decisive factor affecting the degree of development of geological hazards. The topography of the loess land formed by accumulation is the macroscopic factor that affects the location of geological hazards in the area; while the effective critical surface of the terrain formed by erosion is an important spatial condition for the generation of landslides and deformation and activities, that is to say, slope topography is a prerequisite for the generation of landslides. The geomorphological types in Jiaxian County can be divided into three types: wind and sand area, soil and stone mount area, and loess hilly area (Fig. 4f). Among them, loess hilly area is the most widely distributed geomorphological unit type in Jiaxian County, which is also the area with intensive distribution of landslides. Loess hills are universally distributed in the excessive zone of the transition from river valley terrace area to Liangshi mount area, which is the area with more serious disaster development.

Water is one of the important factors inducing landslides, and under the long-term hollowing out effect of rivers, critical surface will be generated at the foot of the slope, thus affecting the stability of the slope [52]. The river system map is extracted from the DEM map and four buffer zones are generated by the buffer function in GIS platform: 0–100m, 100–200m, 200–500m and >500m (Fig. 4g).

Intense or prolonged rainfall can saturate the soil, reducing its shear strength and increasing the likelihood of slope instability. Therefore, the average multi-year rainfall was used as one of the factors to assess landslide hazard (Fig. 4h).

Human activities such as excavation, slope modification, and construction can alter the natural slope stability conditions. Changes in the slope geometry, soil composition, and drainage patterns can increase the susceptibility of a slope to landslides. Additionally, the introduction of excess weight on a slope or the removal of vegetation can further contribute to instability. In this study, we evaluated building density, road density, and cropland, and then overlaid these factors in GIS. The intensity of human engineering activities has been qualitatively categorized as: strong, medium and weak (Fig. 4i) using the natural breaks method.

3.3 Vulnerability assessment of elements at risk

Vulnerability refers to the severity of damage that the elements at risk may suffer from geological hazards, reflecting the temporal and spatial probability of the elements at risk and the severity of the consequences of the damage (value size). In this study area, people, roads, buildings and other amenities that are significantly threatened by landslide hazards that are mainly used as elements for landslide risk assessment. Fig. 5 shows the spatial distribution of the elements at risk in the study area. A notable feature is that the landslide bearing objects in Jiaxian County are diverse, widely distributed, and have complex characteristics, thus making it difficult to obtain accurate vulnerability characteristics these elements at risk. Therefore, we preliminarily determined the vulnerability value of general the objects at risk objects by classifying and assigning values (Table 1).Fig. 5 The vulnerability distribution of roads, population and buildings in Jiaxian County.

Fig. 5

Table 1 Vulnerability values for elements at risk.

Table 1Type of objects	Levels	Values	
Number of people threatened by landslide directly	≥1000	0.8–1.0	
100∼1000	0.5–0.8	
10∼100	0.3–0.5	
<10	0∼0.3	
Transport facilities	Highways	0.8–0.9	
National roads	0.5–0.8	
Provincial roads	0.3–0.5	
City Roads	0.2–0.3	
General Highway	0.1–0.3	
High-speed railway	0.8–1.0	
General railway	0.3–0.6	
Other living facilities	Oil and Gas Lines	0.8–1.0	
Water Transmission Lines	0.4–0.7	
Power transmission lines	0.4–0.7	
Communication Lines	0.3–0.6	

3.4 Kernel extreme learning machine

The Kernel Extreme Learning Machine (KELM) is a kernel-based approach for efficient and accurate learning. KELM employs a two-layer feedforward neural network architecture which is similar with the artificial neural network [53]. The first layer, known as the hidden layer, consists of randomly initialized Gaussian radial basis function (RBF) neurons. These neurons serve as feature extractors, transforming the input data into a high-dimensional feature space. For a hidden layer with m input neurons and h hidden layer neurons, the output with n variables can be calculated using equations (1), (2):(1) H(x)=g(W1X+b1)

(2) Y(x)=W2H+b2

Where X is the input matric, g is a activate function, H is the results of the hidden layer, Y is the results of the output layer. W1 and b1 are the weights and bias between input and hidden layers, W2 and b2 are the weights and bias between hidden and output layers. Among of them, the W1 and b1 are generated randomly. The learning process of KELM involves finding the optimal weights for the connections between the hidden layer and the output layer. This is performed through a regularized least squares algorithm, which minimizes the difference between the desired outputs and the predicted outputs of the model. Since a key innovation of KELM lies in its use of kernel functions to replace the random feature mapping. These kernel functions enable KELM to implicitly map the input data into a high-dimensional feature space, where linear separability is more likely to be achieved. By exploiting the properties of these kernel functions, KELM can effectively capture complex patterns and relationships in the data. The output of KELM is as equations (3), (4), (5):(3) Yˆ=HW1=HHT(1C+HHT)−1Y

(4) HHT=[K(x,x1),…,K(x,xm)]

(5) K(x,xj)=exp(−‖x−xj‖2σ2)

where C is the regularization factor, K means kernel function. In this study, K is set to RBF function which is considered excel at capturing non-linear relationships [14,39] and suitable for our task that exhibits non-linear characteristics. σ represents kernel parameter. By leveraging the capabilities of kernel functions, KELM can effectively learn complex patterns and relationships, offering efficient and accurate solutions to various learning tasks.

3.5 Information value model

The formation of geological hazards is influenced by a variety of factors, and the informativeness model reflects the contribution of combinations of hazard-causing factors and their subdivided intervals to the occurrence of geological hazards in a given geological environment. In landslide hazard assessment, the informativeness model can be used for quantitative description based on historical landslide statistics and the landslide occurrence density under each influencing factor [54,55]. Equation (6) expresses the information content of geological hazards corresponding to a particular state of a factor:(6) IV(xi,B)=ln(Si/SNi/N)

where IV(xi, B) refers to the total amount of information corresponding to the occurrence of landslides in a given influencing factor of x, indicating the likelihood of landslides occurring, which can be used as a landslide susceptibility or hazard index. Ni is the area of landslide or the number of landslide points under the condition of i-th state (or interval) of the influencing factor x, N is the total area or the number of the landslide points. Si is the area of the influencing factor in the i-th state (or interval), S is the total area of the study area. When IV(xi, B) > 0, it reflects the fact that in the i-th state (or interval) of the corresponding factor x, there is a positive information about the propensity of landslides to occur. When IV(xi, B) < 0, it indicates that the condition of the i-th state (or interval) of the factor x is unfavorable to the occurrence of landslides. When IV(xi, B) = 0, it indicates that from the aspect of the density of landslide happens, the i-th state (or interval) of the factor x cannot provide any information about the occurrence or non-occurrence of landslides [40].

3.6 Risk assessment method

Landslide risk assessment combines hazard intensity with vulnerability to evaluate the potential impact of landslide. A quantitative description of landslide risk is the probability that a landslide hazard will cause harm to a given element [12]. The values of landslide risk can be calculated by equation (7):(7) R=P(H)×V

where R is the landslide risk value, P(H) is the assessed landslide hazard, V is the vulnerability. Furthermore, P(H)=P(t)×P(s|t) contains the probability of temporal impact (P(t)) given the probability of spatial impact (P(s|t)). We can consider the product of P(s|t) as ‘susceptibility’. The decision tables have used for combining temporal probabilities with landslide susceptibility to assess landslide hazard [56,57]. Landslide hazard and vulnerability are two separate components regarding landslide risk. Hence, we extend the hazard decision table to the landslide risk assessment, because the elements in both decision tables have the same combination way when used to assess landslide hazard and landslide risk, respectively. The corresponding combination results are shown in Table 2.Table 2 Landslide risk matrix.

Table 2

4 Results

4.1 Landslide failure modes

Landslides are formed in different geological environments and show different characteristics and failure modes. The purpose of classifying the failure modes of landslide deformation is to understand the development law of slope failure under various factors. The landslide failure modes in the study area can be roughly divided into three types, namely, shallow creep tensile failure, slip-compression induced tensile failure and slope toppling failure, which account for 26.35 %,37.20 % and 36.43 % of the total landslide in the study area, respectively.

Shallow creep mode (Fig. 6a): closely related to rainfall process. In the absence of dominant infiltration channels, rainfall infiltration depth in loess generally does not exceed 5 m. The occurrence of shallow loess landslide is mainly caused by the increase of soil moisture content and the decrease of slope strength index after rainfall. It mainly developed in Malan loess and slope deposits in the Upper Pleistocene. The length and width of the landslide are mostly within tens of meters, and the thickness is generally within 2 m.Fig. 6 Three main failure modes of landslide in the study area. (a) Shallow creep; (b) Slip-compression induced tensile; (c) Tipping.

Fig. 6

Slip-compression induced tensile mode (Fig. 6b): This model develops from the bottom up in the weak structural plane inside the slope, and the developed strata are mainly thick loess. The rapid infiltration of precipitation along the dominant channels such as joints, fissures and sinkholes leads to the decrease of soil strength index.

Tipping failure mode (Fig. 6c): it mostly occurs in the high and steep slope of loess formed by cutting slope and excavation, and mostly forms in the loess body of late Pleistocene, and the slope is generally above 60°. Due to the lack of vegetation protection, the unloading spalling of surface soil is easy to occur, and the toppling deformation and failure will occur in the direction of air.

4.2 Spatial characteristics

The development and formation of landslide hazards depend on the geological and environmental conditions and the combined effect of various internal and external stress factors. The formation conditions of landslides are extremely complex, therefore, to analyze the causes and mechanisms of landslide hazards in this area, and to grasp the development law are necessary. Moreover, the spatial characteristics analysis of landslides is also an essential step to taking correct preventive and curative measures, and to reducing the disaster losses. The environmental and geological conditions and influencing factors of landslide disasters in Jiaxian County are analyzed one by one.

Landslides are the main geological hazards developed in Jiaxian County, with the characteristics of wide distribution, large number, high activity and high destructiveness. They are mainly distributed in the central part of the study area, including Wuzhen, Tongzhen, Jiazhou Street, Zhuguanzhai Town and other (Fig. 7). The statistics show that the area with slope angle less than 25° covered about 85 % of the study area and 37 landslides occurred in this area, which is 28.68 % of the total number of investigations. The zone with steep slopes of 35–90° constitutes 0.58 % of the total area but the number of landslides recorded in this area is as high as 18.6 %. In addition, the landslide density values in different zones characteristized by low landslide density value of 0.0054/km2 at gently areas (slope angle low than 25°), and the density values increase significantly when the slope exceeds 35°. (Fig. 8a).Fig. 7 The statistics on density and duration of landslide episodes in the region.

Fig. 7

Fig. 8 Spatial distribution analysis of landslide inventory and some influencing factors: (a) slope, (b) slope height, (c) aspect, (d) distance from rivers.

Fig. 8

Landslides in Jiaxian County are concentrated in slopes with slope heights of 0–30 m, totalling 115, accounting for 89.15 % of the recorded landslide sites, and 14 developed above 30 m, accounting for 10.85 % of the total number of landslides (Fig. 8b). Most of the landslides in the area occurred in the upper and middle slopes, especially those that started sliding from near the top of the slopes. Besides, there is a good consistency between landslides density and slope height. The higher the slope height the more landslides density values to increase. The density value reaches to its maximum of 0.112/km2 at slope with 30–40 m height and experiences a slight decrease thereafter. Moreover, due to the fact that the collapse disaster is mostly developed in the high and steep side slopes formed by artificial cutting and breaking, whereas the height of these manual slopes in the study area is mostly less than 60 m, so it may limit the statistical results of the present investigation.

The results of the spatial analysis showed that the distribution of aspect in Jiaxian Country is related to the development of the river systems in Jiaxian Country. The overall direction of the main river trunks generally in a north-south direction, and its main tributaries mostly in directions of near-northeast to south-west and east-west, which determines the distribution of aspects on the two sides of the rivers in the ranges of 22.5–67.5°, 67.5–112.5°, 157.5–202.5°, and 202.5–247.5°. These four directions covered 14.12 %, 15.14 %, 12.35 % and 15.67 % of the total study area. Most of the landslides occurred on the south-facing aspect, specifically on aspect from 67.5 to 247.5°. The higher landslide density values in these directions also confirms that south-facing slopes are more prone to landslides occurrence (Fig. 8c). In contrast, the lowest landslide density with value of 0.022/km2 were observed in the northward direction slopes. According to the statistical results, it can be seen that landslides are prevalent on slopes in all directions, but overall, most landslides are located on east-west oriented slopes.

The slope profile of slopes in the survey area can be categorized into three basic types, namely convex, linear and concave. The first two types are positive and the latter negative. In this survey, 39 landslides occurred on convex slopes, accounting for 30.23 % of the total number of landslides; 56 landslides occurred on linear slopes, accounting for 43.42 % of the total number of landslides, and 34 landslides occurred on negative type slopes (accounting for 26.35 % of the total number of landslides). Additionally, convex slopes have higher landslide density value of 0.057/km2 compared with concave slopes (0.047/km2). This statistical result suggest that are convex slopes more prone to landslide disasters. This can be explained by the fact that concave type slopes are supported by the stress along the direction of slope strike, which reduces the degree of stress concentration and significantly increases stability. On the contrary, for convex slopes, the degree of stress concentration is markedly increased, leading to a significant decrease in stability. In other words, the slope type has a controlling effect on the stability and deformation damage pattern of slopes, and the positive types of convex and linear slopes are more prone to destabilization than the negative types of concave slopes.

Surface water has a close relationship with geological hazards, mainly in the regions with rivers and reservoirs. The influence of rivers on landslides is mainly manifested in the erosion of banks by flowing water, which leads to slope instability (Fig. 8d). The effect of rivers on slopes varies according to the development period of the river [52]. In the study area, the main streams of the Yellow River, the Baldy River, the Jialu River and the other larger first-grade tributaries have entered the old age, and the river valley has reached hundreds of meters wide. For these rivers, the valley tends to balance the siltation, the river foot of the slope is weakened. Hence, for these rivers, the impact on the formation of landslides is not obvious. For the second and third level tributaries, the river downstream erosion is limited, whereas the side erosion is stronger. So, the younger rivers, the stability of the valley slope suffers a significant impact. Within the third and fourth level or smaller gullies, mainly loess gullies, both downward and lateral erosion of the flowing water exist. For these regions, the valley slopes on both sides are commonly steeper, and are still suffering erosion of the flowing water, which is the most frequent section of landslides and avalanches. However, due to the topographic constraints in these areas and the relatively limited human activities, the probability of the formation of earth slides and avalanches is relatively low.

With reference to the basic quality grading of rocks, the stratigraphic lithology of Jiaxian County is classified into 2 types according to the degree of softness and hardness in accordance with different hardness structures, which are hard rock groups (e.g. Triassic sandstone, etc.) and soft rock groups (Quaternary strata). The soft rock group accounts for 82.84 % of the area of Jiaxian County, and 89.06 % of the disaster points, with a density of 0.068 points/km2, so the geological hazard potentials and actual geological hazards are significantly affected by the engineered geologic rock groups. Rock and soil bodies are the material basis for the development and formation of geological hazards. Stratigraphic rock groups influence the type and development and distribution characteristics of geological hazards, and there is a close relationship between geological hazard activities and the type and structure of geotechnical bodies. That is to say, geological hazards develop in loose and weak soil bodies. There is a potential relationship between engineering geological rock groups and the formation of geological hazards in Jiaxian County.

4.3 Landslide susceptibility and hazard assessment

In fact, the influence of factors on geological hazards is usually constant over a small range of quantities. So that, reclassification of the continuously distributed data in the factor layers is required. Another role of reclassification can simplify model calculations and obtain more reliable assessment results. The classification of each factor map and the amount of information values on the corresponding factor attributes are shown in Table 3, Table 4. Among the nine factors, the first seven only contained the spatial information were used to assessment the landslide susceptibility, whereas the last two factors (rainfall and human activities) both contained the spatial-temporal information were further used to conduct landslide hazard mapping. Subsequently, the first seven theme maps containing spatial information were used to assess the susceptibility via KELM model. According to the IV method (Eq. (6)), the remaining three temporal evaluation factors (Table 4) were regenerated into three information layers via spatial superposition analysis and obtain the total information map. Besides, Pearson correlation coefficients [58] was used to assess the independence of the nine influencing factors, conducted by the “Spatial Analyst” tool within a GIS platform. The maximum correlation coefficient value of 0.526 was between rainfall and distance from river, which is also less than the critical value of 0.7. Therefore, there were no strong correlations between these nine factors. Subsequently, we employed k-fold cross-validation (with k = 5) and grid search method on training dataset to optimize the regularization coefficient C and kernel parameter σ. The optimal combination for C and σ were 10 and 0.1, respectively. Similarity, the landslide hazard index was generated based on landslide susceptibility index and the last two factor maps. Using the functions of reclassification and natural breakpoint method in the GIS platform, the landslide susceptibility/hazard index of the whole area studied is divided into levels: very high (VH) susceptibility zone, high (H) susceptibility zone, medium (M) susceptibility zone, and low (L) susceptibility zone, so as to complete the susceptibility zoning and mapping of the study area.Table 3 The information value of each attribute (interval) within the influencing factor.

Table 3Factors	Attributes	Ni	N	Si	S	IV	
Slope	0–15°	5	129	1826103	3233675	−2.6789	
15–25°	32	129	1154739	3233675	−0.3643	
25–35°	26	129	234199	3233675	1.0235	
35–45°	42	129	17543	3233675	4.0946	
>45°	24	129	1091	3233675	6.3125	
Aspect	Flat (−1)	1	129	4360	3233675	1.7491	
337.5°–22.5°	4	129	295228	3233675	−1.0799	
22.5°–67.5°	12	129	456752	3233675	−0.4177	
67.5°–112.5°	19	129	489594	3233675	−0.0276	
112.5°–157.5°	18	129	387103	3233675	0.1532	
157.5°–202.5°	21	129	399303	3233675	0.2764	
202.5°–247.5°	29	129	506798	3233675	0.3607	
247.5°–292.5°	14	129	392729	3233675	−0.1125	
292.5°–337.5°	11	129	301808	3233675	−0.0903	
Curvature	<-0.5	34	129	764815	3248060	0.1127	
−0.5-0.5	56	129	1723091	3248060	−0.2005	
>0.5	39	129	760154	3248060	0.2560	
Slope height	0–10m	17	129	726676	3268464	−0.5230	
10–20m	59	129	1556497	3268464	−0.0404	
20–30m	39	129	759340	3268464	0.2634	
30–40m	13	129	186260	3268464	0.5700	
＞40m	1	129	39691	3268464	−0.4489	
Distance from rivers	0–100m	41	129	182039	3331200	1.7606	
100–200m	40	129	345106	3331200	1.0963	
200–500m	26	129	943395	3331200	−0.3401	
>500m	22	129	1860660	3331200	−1.1864	
Geomorphological	Wind sand area	1	129	115293	3247997	−1.5215	
Gravelly soil area	20	129	503524	3247997	0.0001	
Loess mounted gullies	108	129	2629180	3247997	0.0337	
Stratigraphy lithology	T3h	50	129	687505	3247941	0.6049	
Q2+3	39	129	1790110	3247941	−0.6005	
N2	8	129	218461	3247941	−0.0812	
T2t	16	129	282927	3247941	0.3534	
T3y-w	16	129	268938	3247941	0.4041	

Table 4 The information value of each attribute (interval) within the influencing factor.

Table 4Factors	Attributes	Nj	N	Sj	S	IV	
Rainfall	350–375 mm	21	129	279587	3247997	0.6372	
375–400 mm	63	129	1514679	3247997	0.04616	
400–425 mm	34	129	1316833	3247997	−0.4306	
425–450 mm	11	129	136898	3247997	0.7046	
Human activities	Strong	2	129	4322	3247997	2.4554	
Medium	20	129	59462	3247997	2.1364	
Weak	107	129	3184213	3247997	−0.1672	

We validated the performance of the KELM model in 30 % of the test set. The specific evaluation metrics include the accuracy (ACC) and he area under the receiver operating characteristic curve (AUC) [35]. In addition, two commonly used machine learning models, logiest regression (LR) and support vector machine (SVM) [32], are also used for comparison between model performance. Fig. 9 shows the subject acceptance curves of the three models, and the results show that the proposed KELM has the optimal prediction performance with AUC value of 0.853, followed by SVM (0.821) and LR (0.790). In addition, the ACC worthy comparison also yielded similar results, i.e., KELM obtained the highest prediction accuracy of 0.753, followed by SVM (0.747) and LR (0.728).Fig. 9 Receiver operating characteristic curves comparison between landslide susceptibility evaluation models.

Fig. 9

The visual assessment of landslide susceptibility and hazard shows that the map of landslide prone areas shows strong spatial distribution characteristics (Fig. 10). The VH susceptibility zone is mainly distributed in the central part of the study area, especially in areas close to the river network, where the largest river flows through and there are dense tributaries distributed (Fig. 10a). On the other hand, low susceptibility areas are more concentrated in the northern and southern of the study site. This is mainly due to the limited human activities in these areas, and the slopes are relatively intact combined with the high vegetation coverage except for the wind and sand area. The L susceptibility area mainly include Jinmingsi Town, Wangjiacheng Town, Keng Town, Hydra Town and Dian Town, which is far from urban areas and rarely experiences landslides. From the spatial distribution of landslide susceptibility zones, the model of KLEM can effectively evaluate the landslide susceptibility of the study area, and the mapping results are consistent with the historical landslide, especially in VH and H susceptibility areas.Fig. 10 The landslide susceptibility (a) and hazard (b) maps calculated by IV in the Jiaxian Country.

Fig. 10

For the landslide hazard map (LHM), the H and VH hazard areas are mainly located in the north-central part of the study area, while the L hazard is mainly distributed in the southern and sandy areas (Fig. 10b). In order to reveal more clearly the spatial distribution pattern of the susceptibility and hazard maps, the percentage of area occupied by each landslide susceptibility/hazard level and the percentage of landslides were calculated (Fig. 11a and b). These calculations showed that the landslide susceptibility and hazard maps both exhibit similar trends, that is, most areas of the study area are concentrated in L susceptibility or L hazard areas. As the susceptibility or hazard levels increases, the density of landslides significantly increases. A detailed explanation is taking based on the spatial distribution of the LHM (Fig. 10, Fig. 11b). The area of these two low levels (L and M) of the LHM mapping was 51.32 % and 37.90 %, respectively, with a cumulative area of 89.22 % of the total area of the study area. This indicates that with the informative multifactor hazard assessment model based on LHM, we can conclude that the overall landslide hazard of the entire study area is at a low level. In addition, there are limited historical landslide records belonging to these two hazard levels, only 13.95 % and 22.48 %, respectively. In contrast, the area covered by the VH and H hazard levels is relatively small compared to other areas labelled with different hazard levels. For the LSM map, the percentage of areas divided by VH and H levels were 2.28 % and 8.50 %, respectively. In contrast, these areas identified 35.66 % and 27.91 % of the historical landslides. Further, the calculation of landslide density for each hazard partition showed that the landslide density decreased significantly from 0.9948 for VH hazard zone to 0.0173 for L hazard zone. In addition, compared with the results of the landslide susceptibility assessment, additive temporal factors provide more effective in assessing landslide hazard, that is, the landslide density in the VH and H regions is higher. This indicates that the method coupling KELM and IV can generate reliable maps for hazard assessment of the study area as they are able to identify most of the historical landslides in the smaller areas of H hazard.Fig. 11 The distribution pattern of the landslide susceptibility (a) and hazard (b) maps and the historical landslides.

Fig. 11

4.4 Landslide risk assessment

According to this field survey, the number of threatening populations of 129 landslide sites and hidden hazards in Jiaxian County was obtained, and the vulnerability of people was obtained by using the kernel density algorithm in the GIS software, and then reclassified (Fig. 12a). The method of building area normalization was adopted taken as the building vulnerability map in the survey area (Fig. 12b). Determine the vulnerability of transport facilities based on the vector map of detailed roads distribution maps (Fig. 12c). To better understand the determination and validation of vulnerability values, we have included Table 5, which provides the specific vulnerability values assigned to different types of disaster-bearing elements. The building, residential and roads vulnerability were overlaid in the GIS software to obtain the comprehensive vulnerability of Jiaxian County, which is then normalized in Fig. 12d. Table 6 showed the landslide risk distribution in Jiaxian Country. The landslide risk in the study was majority presented in L or M level. The quantitative results of the hazard vulnerability results were overlaid on the raster to obtain the final results of the landslide risk evaluation (Fig. 13). Areas at H or VH risk were smaller, and specific areas of higher risk were shown in Fig. 13 detailed. Although, 2.30 % area were located in H or VH risk, 65.11 % of the inventory landslides occurred in these regions. Thus, it is shown that risk zoning is efficient in identifying potentially hazardous events and can demonstrate the reliability of this risk map. For, this risk map can provide an effective reference when implementing land use planning. The risk map can help stakeholders and policy makers manage risks from a holistic perspective and develop appropriate emergency response plans. By understanding the spatial distribution of risks, policymakers can allocate resources more effectively, prioritize areas for intervention, and enhance overall disaster preparedness and resilience.Fig. 12 Distribution of normalized vulnerability in Jiaxian Country. (a) residential vulnerability, (b) road vulnerability, (c) building vulnerability, (d) comprehensive vulnerability.

Fig. 12

Table 5 Vulnerability values of disaster-bearing elements.

Table 5Type of Disaster-bearing elements	Classification	Value	
Population	≥1000 people	0.8-1.0	
100-1000 people	0.5-0.8	
10-100 people	0.3-0.5	
<10 people	0.0–0.3	
Transportation Facilities	National Roads	0.5-0.8	
Provincial Roads	0.3-0.5	
Urban Roads	0.2-0.3	
High-speed Railways	0.8-1.0	
General Railways	0.3-0.6	
Other Living Facilities	Oil and Gas Pipelines	0.8-1.0	
Water Supply Pipelines	0.4.0.7	
Power Supply Lines	0.4-0.7	
Communication Lines	0.3-0.6	

Table 6 The spatial distribution of the landslide risk map and the historical landslides.

Table 6Landslide risk levels	Area (km2)	Rate of area (%)	Number of landslides	Rate of landslides(%)	Density of landslide (/km2)	
VH	4.99	0.25 %	16	29.45 %	3.2064	
H	41.72	2.05 %	48	35.66 %	1.1505	
M	490.50	24.16 %	44	29.45 %	0.0897	
L	1493.02	73.54 %	21	5.43 %	0.0141	

Fig. 13 The landslide risk zonation map in the Jiaxian Country.

Fig. 13

5 Limitation and uncertainties of proposed procedures

The potential limitations and uncertainties of our models arise primarily from data quality including landslide inventory and influencing factors. First, the quality of the data significantly impacts the accuracy of the results. Incomplete or inaccurate data can lead to incorrect assessments of hazard and vulnerability. We have made efforts to obtain a complete and reliable historical landslide record. Measures taken include: on-site surveys by personnel, multi-temporal and various resolutions remote sensing images, and additional surveys of key areas by unmanned aerial vehicle.

The factor inputs selection are the main uncertainties during model development [47]. We understand the importance of considering a wider range of influencing factors and the need for task- or environment-specific considerations in landslide susceptibility assessment. Currently, our various includes 9 commonly used influencing factors, which cover many aspects affecting slope stability. However, we recognize that this fixed set of factors may not fully address all scenarios, particularly those involving the aspect of the time probability of the actual landslide occurrence [10]. Where precipitation information is taken as input in the form of annual averages, which may ignore more time-varying information and may not fully capture the dynamic nature of geological hazards [56]. Therefore, introduce additional influencing factors may improve model performance. In particular, monthly precipitation or groundwater storage with time-varying effects, etc.

6 Conclusion

Assessing landslide risk on a regional scale is an essential tool for managing hazard risks and land-use planning. This involves spatial data analysis, modelling calculations, risk mapping, and many other techniques. The Jiaxian Country in the Loess Plateau was selected as the study area to conduct a complete landslide risk assessment procedure. Firstly, by combining remote sensing observations with ground verification techniques, we identified historical geological hazards in Jiaxian County, and a total of 129 landslides was detected. Among of them, over 70 % are avalanches, and most of the landslides were of small magnitude. The spatial distribution of landslides is heterogenous with the majority of landslides located within the densely populated central region of the area, which is characterized by adequate river networks. 53.13 % of the landslides are situated on slopes with gradients ranging from 26 to 45°, and over 89 % of the landslides occur on slopes of less than 30 m slope height. It should be noted that the construction of a large number of man-made side slopes and slope-cutting works in the study area may have contributed to the limited occurrence of landslides on the higher slopes. Moreover, according to the KELM and IV hybrid assessment model, over 89 % of the study area exhibits M or L hazard to landslides. This suggests that the overall hazard to landslides in Jiaxian Country is low level, although some localized areas show H hazard. Overall, the central part of the study area exhibits H and VH hazard, which is consistent with the surveyed landslide inventory. In addition, the landslide density decreased significantly from 3.2064 in the VH risk area to 0.0141 in the L risk area. This indicates that the IV model is reliable in evaluating the likelihood of landslides in Jiaxian County. The findings of the study contribute to comprehending the mechanism of landslide developments and offer a point of reference for managing landslide risks.

Funding

This research received no external funding.

Data availability statement

Data will be available on request.

CRediT authorship contribution statement

Youxiang Wang: Writing – review & editing, Validation, Supervision, Resources, Conceptualization. Liangqiang Kang: Writing – original draft, Software, Methodology, Formal analysis, Data curation. Jianping Wang: Writing – review & editing, Visualization, Investigation, Formal analysis, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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