Landslide susceptibility mapping using an entropy index-based negative sample selection strategy: A case study of Luolong county

Abstract

Landslides constitute a significant geological hazard in China, particularly in high-altitude regions like the Himalayas, where the challenging environmental conditions impede field surveys. This research utilizes the IOE model to refine non-landslide samples and integrates it with multiple machine learning models to conduct a comprehensive assessment of landslide susceptibility in Luolong County, Tibet. The IOE model objectively assigns weights to conditioning factors based on the degree of data dispersion, thereby enhancing the predictive accuracy when combined with machine learning models. This research employed Google Earth satellite imagery to construct a comprehensive database comprising 2517 landslide debris in Luolong County. Twelve conditioning factors were identified, encompassing geological environment, topography, meteorology, hydrology, vegetation, soil, and human activities. The IOE model was integrated with SVC, MLP, LDA, and LR models to systematically evaluate landslide susceptibility in Luolong County. The results demonstrate that, after optimizing the non-landslide samples, the coupled models significantly outperformed the unoptimized models in terms of AUC, accuracy, precision, and F1 score. The ranking of classification performance and effect among the four coupled models is IOE-MLP > IOE-SVC > IOE-LR > IOE-LDA. Notably, the AUC value of the IOE-MLP coupled model increased from 0.8172 to 0.9747. Moreover, in the extremely high susceptibility zones, the IOE-MLP model had the highest landslide frequency ratio among the four coupled models, demonstrating the optimal classification performance and the best classification effect. The study identifies land use, elevation, and slope as the predominant controlling factors conditioning landslides in Luolong County. The regions with the highest susceptibility to landslides in Luolong County are predominantly situated in the central areas near rivers and roads, whereas the areas with the lowest susceptibility are largely located in the southwestern, northern, and certain central regions at elevations above 4500 m, which are consistently shrouded in snow and ice. This comprehensive method effectively resolves the challenge of selecting non-landslide samples, thereby improving the predictive accuracy of the landslide susceptibility model. The results of this study offer significant insights for disaster prevention, mitigation, and land use planning in analogous geological settings.

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PLOS One | https://doi.org/10.1371/journal.pone.0322566 May 9, 2025 1 / 27
OPEN ACCESS
Citation: Yuzhong K, Hua W, Chong X, Jingjing
S, Kangcheng Z, Chenguang Z, et al. (2025)
Landslide susceptibility mapping using an
entropy index-based negative sample selection
strategy: A case study of Luolong county. PLoS
One 20(5): e0322566. https://doi.org/10.1371/
journal.pone.0322566
Editor: Trung Van Nguyen, Hanoi University of
Mining and Geology, VIET NAM
Received: October 24, 2024
Accepted: March 24, 2025
Published: May 9, 2025
Copyright: © 2025 Yuzhong et al
.
This is an
open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution,
and reproduction in any medium, provided the
original author and source are credited.
Data availability statement: All the data sourc-
es used in this paper are publicly available,with
no copyright issues and no need for authori-
zation from the data owners. All data used in
this study is publicly available on the Figshare
platform. The dataset is shared at “https://
RESEARCH ARTICLE
Landslide susceptibility mapping using an
entropy index-based negative sample selection
strategy: A case study of Luolong county
Kong Yuzhong 1,2, Wu Hua 1,2*, Xu Chong3,4, Sun Jingjing3, Zhu Kangcheng1,2,
Zhang Chenguang1,2, Zhou Jianwei1,2, Xu Tong1,2, Su Taijin1,2, Zhang Zelin1,2, Kong Hui5
1 Tibet University, Lhasa, Tibet, China, 2 Bomi Geological Hazards Field Scientific Observation and
Research Station of the Ministry of Education, Bomê, Nyingchi, Tibet, China, 3 National Institute of Natural
Hazards, Ministry of Emergency Management of China, Beijing, China, 4 Key Laboratory of Compound
and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing, China,
5 Hunan Provincial Institute of Land and Space Survey and Monitoring, China
* xzwhua@163.com (WH)
Abstract
Landslides constitute a significant geological hazard in China, particularly in high-
altitude regions like the Himalayas, where the challenging environmental conditions
impede field surveys. This research utilizes the IOE model to refine non-landslide
samples and integrates it with multiple machine learning models to conduct a com-
prehensive assessment of landslide susceptibility in Luolong County, Tibet. The IOE
model objectively assigns weights to conditioning factors based on the degree of data
dispersion, thereby enhancing the predictive accuracy when combined with machine
learning models. This research employed Google Earth satellite imagery to construct
a comprehensive database comprising 2517 landslide debris in Luolong County.
Twelve conditioning factors were identified, encompassing geological environment,
topography, meteorology, hydrology, vegetation, soil, and human activities. The IOE
model was integrated with SVC, MLP, LDA, and LR models to systematically eval-
uate landslide susceptibility in Luolong County. The results demonstrate that, after
optimizing the non-landslide samples, the coupled models significantly outperformed
the unoptimized models in terms of AUC, accuracy, precision, and F1 score. The
ranking of classification performance and effect among the four coupled models is
IOE-MLP > IOE-SVC > IOE-LR > IOE-LDA. Notably, the AUC value of the IOE-MLP
coupled model increased from 0.8172 to 0.9747. Moreover, in the extremely high
susceptibility zones, the IOE-MLP model had the highest landslide frequency ratio
among the four coupled models, demonstrating the optimal classification perfor-
mance and the best classification effect. The study identifies land use, elevation,
and slope as the predominant controlling factors conditioning landslides in Luolong
County. The regions with the highest susceptibility to landslides in Luolong County
are predominantly situated in the central areas near rivers and roads, whereas the

PLOS One | https://doi.org/10.1371/journal.pone.0322566 May 9, 2025 2 / 27
areas with the lowest susceptibility are largely located in the southwestern, north-
ern, and certain central regions at elevations above 4500 m, which are consistently
shrouded in snow and ice. This comprehensive method effectively resolves the chal-
lenge of selecting non-landslide samples, thereby improving the predictive accuracy
of the landslide susceptibility model. The results of this study offer significant insights
for disaster prevention, mitigation, and land use planning in analogous geological
settings.
Introduction
As per the “14th Five-Year Plan for the Prevention and Control of Geological Disas-
ters” issued by the Ministry of Land and Resources of China, geological disasters in
our country are highly susceptible, frequent, and widespread. By the end of 2020,
a total of 328,654 registered geological disaster hazard points were documented
nationwide, posing a potential threat to the safety of 13.99 million people and
605.3 billion yuan in property[1]. Landslides are the most extensively distributed
and numerous types of geological disaster in China, with the Himalayan region of
Tibet being a focal area for the prevention and control of high-position remote chain
landslides [2]. The Himalayan region is marked by steep mountains, high altitudes,
thin air, and perennial snow and ice cover, leading to extremely adverse geologi-
cal conditions. Many areas are inaccessible to human efforts, making it crucial to
employ integrated remote sensing techniques to pre-evaluate landslide susceptibility
and delineate different levels of targeted areas [3].
At present, the primary models employed in landslide susceptibility analysis
are experience-driven models, physics-driven models, and data-driven models.
Experience- driven models, which depend heavily on expert experience, are signifi-
cantly influenced by subjective factors, thereby restricting their applicability.
Physics-driven models necessitate comprehensive hydrological and geotechnical
data, rendering them unsuitable for large-scale landslide susceptibility assessments.
Owing to their objectivity and broad applicability, data-driven models have garnered
favor among numerous scholars and experts.
Data-driven models can be further categorized into traditional statistical analyses
and a variety of machine learning methods [4].Statistical models investigate the
relationships between landslide debris and their conditioning factors, emphasizing
the derivation and interpretability of the models, thereby more accurately capturing
the spatial relationships of landslide samples. Commonly utilized statistical models
include the Analytic Hierarchy Process (AHP) [5,6], Frequency Ratio Method [7], and
the Index of Entropy (IOE) [8]. The AHP is highly susceptible to subjective biases,
whereas the Frequency Ratio Method, while capable of objectively characterizing
the relationships between conditioning factors and landslides, does not yield weight
values for each conditioning factor. The IOE method objectively determines attribute
weights based on the inherent dispersion of the data. It automatically normalizes
the data during computation, obviating the need for preliminary standardization or
figshare.com/articles/figure/Landslides_of_
the_LuoLong_county_/28200587” All relevant
data are available without restrictions, ensuring
reproducibility of the study.
Funding: This study was supported by the
National Natural Science Foundation of
China in the form of a grant awarded to WH
(U23A2047), and by the Tibet Autonomous
Region Science and Technology Department in
the form of salaries for WH (XZ202401YD0028,
XZ202401ZY0057, XZ202402ZD0001). The
specific roles of this author are articulated in
the “Author Contributions” section. The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have
declared that no competing interests exist.

PLOS One | https://doi.org/10.1371/journal.pone.0322566 May 9, 2025 3 / 27
normalization, thereby streamlining the calculation process and rendering it ideal for large datasets. Consequently, inte-
grating the IOE model with other models can effectively minimize the impact of subjective biases and mitigate overfitting in
machine learning. Machine learning models emphasize the exploration of relationships and structures inherent in the data,
prioritizing predictive accuracy and optimization performance, thereby achieving superior predictive outcomes. Presently,
models that have demonstrated notable success include Random Forests (RF) [9], Multilayer Perceptron (MLP) [10,11],
Support Vector Classification (SVC) [12,13], Linear Discriminant Analysis (LDA) [14], and Logistic Regression (LR) [15].
Each of these models has its own strengths and weaknesses, yet all demonstrate strong generalization capabilities. How-
ever, individual models exhibit limited capacity to handle non-linear relationships, are unable to fully capture the complex-
ity of landslide susceptibility and are prone to overfitting. Coupling statistical models with machine learning models can
balance the spatial representation of landslides, more comprehensively capture data characteristics, reduce the risk of
overfitting, and enhance prediction accuracy [16,17]. The Random Forest (RF) model is particularly sensitive to noisy data
and may overfit the noise. Therefore, this study selects the Multi-Layer Perceptron (MLP) and Support Vector Classifier
(SVC) models, which perform well with non-linear data, and the Linear Discriminant Analysis (LDA) and Logistic Regres-
sion (LR) models, which perform well with linear data, to be coupled with the IOE model to improve model accuracy and
performance.
The expression of landslide susceptibility is influenced by the type of mapping unit, the algorithm type and its parame-
ter optimization, and the choice of samples [18]. The more rational and accurate the mapping unit, the more effectively it
can capture the terrain and geological conditions, thereby improving the precision of landslide susceptibility evaluations.
The two predominant mapping units in use are grid cells and slope units. While slope units can partially represent ter-
rain and geological environments, they may manifest as jagged or linear shapes, necessitating adjustments based on
actual topography and geomorphology. This adjustment process is labor-intensive and susceptible to errors. Although the
grid cell method does not capture terrain and environmental nuances, it provides extremely straightforward and efficient
calculations, enabling the delineation of extensive regions, which is currently the most prevalent method[19]. When the
grid cell size is reduced to less than 2000m, machine learning techniques applied to landslide susceptibility assessments
exhibit enhanced predictive accuracy [20]. Various algorithm types of process data differently and have distinct applica-
tions. Logistic regression, linear regression, and linear discriminant analysis are well-suited for linear relationships, while
support vector machines, decision trees, and neural networks are adept at handling nonlinear relationships. Algorithmic
parameter optimization enhances model performance, reduces computational resource consumption, and improves model
interpretability. Utilizing Bayesian algorithms for parameter optimization of logistic regression and random forest models
can markedly improve the accuracy of landslide susceptibility predictions. The optimization of the AdaBoost model through
recursive feature elimination and particle swarm optimization can significantly enhance the predictive effectiveness of
landslide susceptibility [21]. The application of Bayesian optimization techniques for hyperparameter tuning of logistic
regression and random forest models has resulted in substantial improvements in their predictive performance for land-
slide susceptibility [22].
While algorithms and mapping units are significant, in machine learning, the selection and quality of samples are
paramount. This is because, regardless of the algorithm’s sophistication, issues with the sample data (such as noise,
bias, imbalance, etc.) can adversely impact the model’s performance and predictive accuracy. Furthermore, the quality
of samples directly influences the model’s learning effectiveness and generalization capabilities, whereas algorithms and
mapping units primarily dictate how information is extracted from the samples and how the model is constructed. Conse-
quently, landslide samples should fully encapsulate the diverse conditions and characteristics associated with landslide
events. These samples can be derived from remote sensing image analysis or field investigations, thereby ensuring a high
degree of accuracy. Non-landslide samples should be carefully selected to minimize the inclusion of potential landslide
sites and are generally obtained indirectly through various methods, as direct acquisition is not feasible. Researchers
have yet to establish a standardized approach for selecting non-landslide samples. Presently, three primary methods

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are employed [23]. The first is random sampling, which is extensively utilized. Some researchers directly choose non-
landslide points outside the landslide areas, which may share similar geological characteristics with the landslide points
[24]. Furthermore, other scholars create buffer zones around landslide points and select non-landslide points beyond
these zones. While random sampling is straightforward, it can lead to an uneven distribution of non-landslide samples,
thereby compromising the model’s accuracy. The determination of buffer distances is highly subjective, and the non-
landslide points selected may lack precision[25,26]. The second category involves selecting non-landslide samples based
on factor constraints, which involves choosing non-landslide samples outside the range of environmental factors asso-
ciated with landslide occurrences. For example, non-landslide points are selected in low-slope areas[27,28], using the
target space outward sampling method[29], and selecting non-landslide points through the optimization of environmental
factors[30]. When employing this method, selecting only one environmental factor may overemphasize its role, result-
ing in unreliable outcomes. Conversely, selecting multiple or all environmental factors can introduce noise, which also
compromises the accuracy of the results. The third category involves selecting non-landslide samples through coupled
models, which represents one of the more sophisticated approaches to negative sample selection. Utilizing random
selection and information value methods to identify non-landslide samples, it has been demonstrated that the information
value method can markedly enhance sample quality and model accuracy [31]. When employing random selection, buffer
zone, frequency ratio, and analytic hierarchy methods to select non-landslide samples, it has been noted that the analytic
hierarchy method yields the best results, followed by the frequency ratio method, with the random selection method being
the least effective [32]. Integrating the certainty coefficient method with multi-kernel support vector machines for landslide
susceptibility evaluation can produce outstanding results [33]. In conclusion, the adoption of a coupled model to identify
non- landslide samples yields higher-quality and more diverse samples, consequently leading to a notable increase in the
model’s accuracy[34].
This study initially utilizes satellite imagery from the Google Earth platform, employing a human-computer interactive
visual interpretation technique to analyze landslide debris in Luolong County and create a comprehensive landslide relic
database. It then integrates the advantages of the IOE model with those of machine learning models, applying IOE-LDA,
IOE-MLP, IOE-SVC, and IOE-LR coupled models to perform a detailed comparative analysis of the landslide debris in
Luolong County. Subsequently, it employs the area under the Receiver Operating Characteristic Curve (AUC), accu-
racy (Acc), precision, F1 score, and landslide frequency ratio to assess the overall effectiveness of each model, thereby
developing a more appropriate method for selecting non-landslide samples and evaluating landslide susceptibility in high-
altitude cold regions.
Materials and methods
Study area
Luolong County is situated in the northeastern part of the Tibet Autonomous Region and the southwestern part of
Changdu City, at the southeastern extremity of the Nyenchen Tanglha Mountains and the upper reaches of the Nu River
(Fig 1). It is positioned between 30°10’ N and 30°50’ N latitude, and 95°10’ E and 95°50’ E longitude, encompassing a
total area of 8098.4 km² and an average elevation of approximately 3859 m [35].
Luolong County predominantly features high mountain and canyon topography, with lower elevations in the central area
and significant variations in terrain. The region is interlaced with numerous rivers and abundant lakes, including the Nu
River, the second-largest river in Tibet, which traverses the central and northern parts of the county. The southern part of
the county is dominated by the Nyenchen Tanglha Mountains, which extend in a northwest-southeast direction, spanning
over 100 km and are perpetually covered with snow and ice. Luolong County lies within a plateau temperate semi-arid
monsoon climate zone, characterized by extended daylight hours, low temperatures, substantial diurnal temperature vari-
ations, minimal annual temperature differences, distinct dry and rainy seasons, and a lengthy, cold winter. Annual rainfall
ranges from 372 to 559 mm, predominantly occurring from June to September, with the maximum daily rainfall varying

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between 14.0 and 39.2 mm. Luolong County is situated at the convergence of the Gondwana and Cathaysian ancient
continents, within the Cathaysian Tethyan tectonic domain, specifically in the Gangdise-Nyainqentanglha mountain sheet.
The county hosts several fault zones, such as the Shuoban Duo Fault Zone, the Chada-Baqu Fault Zone, the Xinben
Fault Zone, and the Baqu Dongcun Fault Zone. The exposed geological formations include the Carboniferous, Paleogene,
and Quaternary systems. The exposed bedrock comprises the Paleogene Zongbai Group, the Upper Carboniferous and
Lower Permian Laigu Group, and the Lower Cretaceous granite, with predominant lithologies of siltstone, black slate, gray
metamorphic sandstone, conglomerate-bearing slate, and a minor amount of mafic volcanic rock [36].
Luolong County is typified by a classic “V” shaped deeply incised canyon landscape, with elevations descending
from 5476 m to 3153 m, resulting in a vertical drop exceeding 2300 m. This steep gradient creates conditions that are
highly conducive to landslides. The area is situated in the globally recognized seismic active zone (The Mediterranean-
Himalayan seismic belt) and is traversed by multiple fault zones, causing rock fragmentation and facilitating the formation
of landslides. Furthermore, Luolong County serves as a vital corridor for China’s second railway to Tibet (the Sichuan-
Tibet Railway) and a key transportation route in southeastern Tibet. It is also a significant region for hydropower
development, hosting projects such as the Xinrong, Maquyong, Zongzha Cuo, and Zhongyi hydropower stations. The
development of these human-engineered projects may elevate the risk of landslides. In recent years, influenced by global
climate warming, the freeze-thaw processes in the region have become more pronounced, thereby augmenting the poten-
tial for landslides. Consequently, undertaking a comprehensive investigation of landslides in Luolong County is crucial for
safeguarding the life and property of local inhabitants and fostering regional economic growth.
Data sources
Source of landslide relic database. Landslide relic data is of paramount importance for the study and mitigation
of landslide disasters. These data not only reveal the spatial distribution characteristics of landslides but also provide
essential support for predicting the likelihood of landslides and assessing their risks. In landslide disaster research, the
Fig 1. Location of the study area.
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precision and comprehensiveness of data are critical. Therefore, effectively collecting and analyzing landslide relic data
has become a key issue in research. Presently, there are two predominant methods for obtaining landslide relic data:
automated landslide extraction and human-computer interactive visual interpretation [37]. Automated landslide extraction
employs machine learning algorithms to discern the features of sample data, enabling automatic extraction [38]. However,
its precision is contingent upon the type of remote sensing imagery, regional topographic characteristics, and the nature
of the landslide, rendering it more appropriate for post-earthquake emergency activities. Visual interpretation harnesses
human and temporal resources to precisely identify targets in complex topographic regions, yielding a comprehensive and
accurate database [39,40]. Through visual interpretation, researchers can meticulously analyze each topographic feature,
ensuring that every detail within the landslide area is accurately documented and categorized. While visual interpretation
achieves relatively high accuracy in landslide identification, it is constrained by the interpreter’s professional knowledge
and experience, introducing considerable subjectivity. Additionally, it is characterized by low automation, inefficiency,
and high costs. The landslide identification method leveraging human-computer interactive interpretation technology
can markedly improve the efficiency of automatic landslide identification compared to traditional visual interpretation. It
minimizes human intervention, rendering landslide identification more intelligent and efficient [41,42].
This study employs remote sensing imagery from the Google Earth platform, segmenting the research area into smaller
grid cells and utilizing a human-computer interactive visual interpretation technique for frame-by-frame analysis. This
approach resulted in the creation of a landslide relic dataset containing 2517 landslide points, as depicted in Fig 2. The
interpretation of landslides is primarily based
on the differences in spectral characteristics, shape, and texture relative to surrounding features, following this gen-
eral procedure: Initially, objects with higher brightness are identified based on hue. Subsequently, regular artificial objects
among the high-bright features are filtered out based on morphological characteristics. Next, interference from features
such as agricultural land is eliminated using texture information. Finally employing manual methods to accurately deter-
mine the correctness of the identified landslides. During manual interpretation, the approximate location of the landslide
is ascertained based on its morphological features and topographical context. Subsequently, the landslide boundary is
delineated by contrasting the landslide with its surrounding environment. Ultimately, the precise location of the landslide
center is established using the scarp and the landslide deposit [23,43]. Overall, the landslide debris in Luolong County are
Fig 2. Landslide Relic Dataset.
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predominantly distributed along a northwest-southeast orientation, with a higher incidence of landslides in areas where
rivers and roads are more concentrated, and relatively fewer landslides in regions with significantly higher surrounding
elevations.
Data sources for landslide conditioning factors. Building upon the foundational work of previous researchers, this
study identifies 12 conditioning factors that are closely associated with landslides across five key dimensions: geological
environment, topography, meteorology, hydrology, vegetation and soil, and human engineering activities [44] (Fig 3). It
establishes a comprehensive landslide susceptibility evaluation index system for Luolong County, with the data sources
for each conditioning factor detailed as follows (Table 1).
Conditioning factors. In this study, all conditioning factors are uniformly projected using the Mercator projection with a
3-degree zone method for the 47N zone. Given that the resolution of the DEM, rainfall, NDVI, and land use data employed
in this study is 30 meters, it is most appropriate to process the mapping units into 30m × 30m grids.
Elevation: Variations in elevation denote the terrain’s ridges or valleys, and changes in height result in alterations in
slope. Rainfall and land use are also closely linked to elevation. The elevation data for Luolong County was derived from
the national DEM digital elevation model with a spatial resolution of 30 m. The elevation spans from 3153 to 5476 m and
is categorized into five levels: 3153–3500 m, 3500–4000 m, 4000–4500 m, 4500–5000 m, and > 5000 m, with areas above
4000 m comprising 85%.
Slope: Characterizes the inclination of the surface, indicating the steepness of the slope. A higher slope indicates a
steeper surface, greater shear force in the rock mass, reduced stability of the slope body, and increased likelihood of
landslides. The slope data are derived from the DEM with a spatial resolution of 30 m and are categorized into five levels:
0°-15°, 15°-30°, 30°-45°, 45°-60°, and > 60°, with the 15°-30° range comprising 49%.
Aspect: Broadly categorized into sunny and shady slopes, where slopes facing the sun are designated as sunny
slopes, and those facing away from the sun are designated as shady slopes. Aspect indicates the accumulation and dis-
tribution of moisture on sunny and shady slopes, with sunny slopes typically being more susceptible to landslides. Aspect
data are derived from the DEM with a spatial resolution of 30 m and are divided into nine categories: flat, north, northeast,
east, southeast, south, southwest, west, and northwest.
Profile curvature: Profile curvature denotes the degree of curvature of the slope surface in the direction perpendicular to
the slope, influencing the stress distribution and water accumulation on the slope, and thus indirectly affecting the risk of
landslides. Profile curvature data are derived from the DEM with a spatial resolution of 30 m and are categorized into five
levels: 0–0.5, 0.5–1, 1–1.5, 1.5–2, and 2–4.3, with the range of 0–1.5 comprising 82%.
Geological lithology: Geological lithology is a key internal factor in landslide development. The mechanical properties of
different rock groups differ markedly, resulting in varying degrees of slope stability. Geological lithology is derived from the
1:500,000 geological map of the Tibetan Plateau, categorizing rock types into five groups: hard rock groups, moderately
hard rock groups, moderately weak rock groups, weak rock groups, and loose rock groups. The moderately weak rock
group covers the largest area, comprising 39%.
Distance to Fault (DTF): The fault structures within Luolong County predominantly develop in a north-south orientation,
exerting a significant impact on the development of joints and fractures in geological bodies. The presence of faults results
in rock fragmentation, destabilizing their structure and facilitating landslide formation. The degree of rock fragmentation is
directly proportional to the distance from faults and fractures; the closer the distance to faults and fractures, the less stable
the rocks become, and the greater the likelihood of landslides. Fault information is derived from geological maps, and
buffer zones are established at 1000-meter intervals, categorized into five levels.
Rainfall: While the primary controlling factors for the formation of landslide geological hazards are the geological envi-
ronment and topography, the intensity of rainfall is also a significant triggering factor for landslides. Rainfall increases the
self-weight of the soil and the saturation of soil moisture, reducing the shear strength of the rock and soil mass, thereby
elevating the risk of landslides. Moreover, rainfall can lead to increased surface runoff and rising groundwater levels,

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Fig 3. Grading diagram of conditioning factors.
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impacting the stability of the rock and soil mass. This study utilizes national annual average rainfall data from 1991 to
2020, categorized into five levels using the natural breaks method: 575–599mm, 599–611mm, 611–625mm, 625–644mm,
and 644–688mm, with the 575–625mm range comprising 79%.
Distance to River (DTS): The proximity to rivers is a significant factor influencing landslides. Firstly, slopes adja-
cent to rivers are more prone to erosion and water level fluctuations, which can compromise slope stability. River
erosion may diminish slope stability, and fluctuations in water levels can cause soil saturation, thereby elevating the
risk of landslides. Secondly, regions near rivers are generally moist, facilitating the penetration of water into the slope,
resulting in soil saturation and decreased stability, thus increasing the potential for landslides. This study derives river
vector data from fundamental geographic data, creates buffer zones at 1000-meter intervals, and categorizes them
into five levels.
Normalized Difference Vegetation Index (NDVI): Indicates the growth status of vegetation in a region. Lower NDVI
values suggest sparser vegetation, characterized by shallow and sparse plant roots, which makes it challenging for veg-
etation to anchor the local soil, thus increasing the potential for landslides. This study employs national NDVI data with
a 30-meter spatial resolution from 2020, spanning from -9999–9999. Dividing this data by 10000 results in NDVI values
ranging from -1–1. The data is subsequently categorized into five levels using the natural breaks method: 0–0.18, 0.18–
0.33, 0.33–0.45, 0.45–0.57, and 0.57–0.92, with the 0.33–0.57 range comprising 50%.
Topographic Wetness Index (TWI): TWI serves as a physical indicator of the influence of regional terrain on runoff
direction and accumulation. A higher TWI value signifies a greater water content or potential soil moisture in the area.
Increased soil moisture not only augments the self-weight of the slope but also diminishes the soil’s cohesion and angle
of internal friction, thereby reducing the soil’s shear strength, enhancing the slope’s instability, and consequently elevat-
ing the risk of landslides. TWI is derived from the DEM with a spatial resolution of 30 m and is categorized into five levels
using the natural breaks method: 5.10–8.70, 8.70–10.59, 10.59–13.75, 13.75–18.16, and 18.16–28.17, with the range of
5.1–10.59 comprising 81%.
Land use: Forests with high vegetation cover exhibit superior soil shear strength, thereby stabilizing slopes. In contrast,
slopes resulting from excavated roads and mines, due to their low vegetation cover, have reduced soil shear strength, increas-
ing the likelihood of landslides. This study employs land cover product data from the Qinghai-Tibet Plateau with a 30-meter
spatial resolution from 2020, categorizing land use into nine classes based on original land categories: forests, shrublands,
grasslands, wetlands, farmlands, built-up areas, glaciers, bare land, and water bodies, with grasslands comprising 57%.
Distance to Road (DTR): Slopes adjacent to roads are more susceptible to influences from road construction, drain-
age systems, increased loads, and traffic activities, potentially elevating the risk of landslides. This study derives road
Table 1. Data sources of landslide points and impact factors.
conditioning factor Name of the data Data type Data source
Distance to faults 1:250,000 geological map of China Raster https://www.resdc.cn
Lithology distribution Database of engineering geological rock groups
on the Tibetan Plateau
Vector https://data.tpdc.ac.cn
Elevation、Slope、Aspect、Profile
curvature、TWI
30 m spatial resolution DEM data for China Raster http://www.gscloud.cn
Distance to roads and Distance to rivers Basic geographic data of river systems, roads,
and administrative boundaries
Vector http://www.gscloud.cn
Rainfall Multi-year average rainfall data for China Raster http://www.gisrs.cn
NDVI 30 m spatial resolution NDVI data for Luolong
County in 2020
Raster http://www.nesdc.org.cn
Land use Land cover data of the Tibetan Plateau with a
spatial resolution of 30 m (2000–2020)
Raster https://data.tpdc.ac.cn
https://doi.org/10.1371/journal.pone.0322566.t001

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vector data from fundamental geographic data, creates buffer zones at 1000-meter intervals, and categorizes them into
five levels.
Data correlation analysis method
The landslide process is a gradual change process, influenced by a multitude of factors. Conditioning factors may
sometimes display collinearity and strong correlation, resulting in data redundancy that impacts the accuracy of the
evaluation model. Therefore, it is crucial to choose an appropriate method to detect correlations among factors and
minimize data redundancy. Collinearity diagnostics are employed to identify severe collinearity issues among multiple
independent variables in regression analysis. Typically, a Variance Inflation Factor (VIF) exceeding 5 and a tolerance
below 0.2 signify the presence of multicollinearity [45]. Pearson correlation analysis is mainly utilized to evaluate the
correlation between two variables. The greater the absolute value of the correlation coefficient (r), the stronger the lin-
ear relationship between the variables. Generally, an absolute value of r greater than 0.8 indicates a strong correlation
between factors [46].
Landslide susceptibility assessment method
(1) IOE model. The IOE model [8] is a statistical approach that objectively quantifies the significance of each
conditioning factor in relation to the indicator, determining the differences among the conditioning factors and their
contributions to the evaluation index. The calculation steps and formulas are as follows:
FR
ij =
a
b.
(1)
P
ij =
FR
ij
s
∑
j=1
FRij (2)
H
i=–
s
∑
j=1
Pij ×log2P
ij
(3)
Himax =log2S
(4)
I
i=
H
imax
–H
i
Himax
(5)
P
i=
1
S
s
∑
j=1
FR
ij
(6)
Wi=Ii×Pi
(7)
In the formula, FRij denotes the frequency ratio of landslides for each conditioning factor across various interval ranges; a
and b respectively indicate the ratio of landslide points and the area ratio for the conditioning factor within these intervals;
Pij signifies the probability density; S represents the count of intervals for each conditioning factor; Hi and Hi,max respec-
tively signify the entropy value and the maximum entropy value; Ii denotes the information rate of each conditioning factor;
Wi denotes the comprehensive weight of each conditioning factor.

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LSI
=
n
∑
i=1
FR
×
W
i
(8)
In the equation, LSI denotes the landslide susceptibility index.
(2) MLP model. The MLP model [47] is a type of multilayer feed forward network within artificial neural networks,
capable of highly parallel processing, exhibiting excellent fault tolerance and robust adaptive and self-learning capabilities.
It primarily comprises an input layer made up of a set of perceptron units, one or more hidden layers composed of
computational nodes, and an output layer consisting of computational nodes. Each layer of nodes is fully interconnected
with the subsequent layer, such that any neuron in the preceding layer is connected to all neurons in the following layer.
Each hidden layer node incorporates an activation function, and through the application of multiple layers of activation
functions, it can transform linear rules, thereby enabling the recognition of nonlinear data. Consequently, it can address
many nonlinear classification problems.
(3) SVC model. The SVC model [48] is a robust machine learning algorithm designed for classification tasks. Its
fundamental concept involves identifying an optimal hyperplane that segregates samples from different classes. This
hyperplane not only aims to maximize classification accuracy but also seeks to maximize the margin between classes,
which is the distance from the support vectors to the hyperplane. By locating this optimal hyperplane within the feature
space, SVC enhances its generalization capability for new data. It can address nonlinear classification problems by
employing kernel functions to project data into a higher-dimensional space.
(4) LDA model. The LDA model [49] is a supervised learning technique utilized in statistics and machine learning,
primarily for addressing classification and dimensionality reduction tasks. It utilizes the Fisher linear discriminant criterion
to maximize the between-class scatter and minimize the within-class scatter, thereby identifying the optimal projection
direction. This method ensures that data from different classes are maximally separated, while data from the same class
are closely clustered, thereby facilitating classification.
(5) LR model. The LR model [50] is a predictive technique employed in statistics and machine learning to tackle
classification problems. Its principal entails utilizing a logistic function to convert the output of linear regression into values
ranging from 0 to 1, thereby enabling binary classification. It is primarily composed of three steps:
(a) Employ a linear regression equation to estimate the value Z.
Z=β0+β1X1+β2X2+···+βnXn
(9)
In the formula, β is the coefficient, and X represents the feature.
(b) The Z value derived from the linear equation is converted into a value P ranging from 0 to 1 using the logistic function
(Sigmoid function).
P
=
1
1+e
–z (10)
(c) Classify according to the value of P. If P exceeds 0.5, predict it as the positive class; otherwise, predict it as the nega-
tive class.
(6) Coupled model. The objective IOE model is distinguished by its high stability and consistency, allowing it to
holistically account for the interrelations and impacts among various indicators. The IOE models integrated with SVC,
MLP, LDA, and LA, through their optimization of non-landslide samples, decrease the likelihood of landslide samples
being misclassified as non-landslide samples, thus improving the precision of the predictive models. The process is

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divided into two stages: The initial stage employs the landslide relic dataset as landslide samples, creates a no-landslide
area by buffering the dataset by 1km, and subsequently selects an equivalent number of non-landslide samples from
this area as non-landslide samples. The landslide and non-landslide samples are merged into sample set 1, and are
analyzed using the SVC, MLP, LDA, and LA models individually, which are considered single models. In the second
part, the landslide scar dataset was used as the landslide samples. An IOE model was employed to generate landslide
susceptibility maps with five distinct susceptibility levels. The extremely low and low susceptibility zones were selected
from these maps as non-landslide areas. Non-landslide samples, equal in number to the landslide samples, were
randomly selected from these non-landslide areas with a minimum spacing of 500 meters to serve as non-landslide
samples. The positive and non-landslide samples were then combined to form Sample Set 2, which was analyzed using
the SVC, MLP, LDA, and LA models, thereby constituting the coupled models.
Validation metrics
The ROC curve is frequently employed to assess the performance of classification models. The horizontal and vertical
axes of the ROC curve represent specificity and sensitivity, respectively, indicating the proportions of correctly predicted
non-landslide and landslide samples. The closer the ROC curve is to the upper left corner, the greater the area under the
curve (AUC), signifying superior classification performance of the model, thus rendering the model more effective and pre-
cise. Furthermore, the confusion matrix in the field of machine learning is commonly utilized to compare classification out-
comes with actual measured values. Each column corresponds to the predicted class, while each row corresponds to the
true class. Acc, Precision, and F1 can be derived from the confusion matrix, where Acc denotes the proportion of correctly
identified samples relative to the total samples, Precision denotes the proportion of truly landslide samples among those
classified as positive by the model, and F1 signifies the harmonic meaning of precision. The formulas are as follows [51]:
Accuracy =(
TP
+
TN
)
(TP+TN+FP+FN) (11)
Pr ecision
=
TP
(TP+FP
)
(12)
F
1=
2TP
(2TP+FP+FN
)
(13)
Landslide susceptibility assessment workflow
(1) Methodological Approach. The methodological flowchart for this study is presented in Fig 4.
(2) Assessment Procedure.
(a) Construction of a landslide inventory dataset. Landslide remnants were identified through human–computer interactive
visual interpretation of Google Earth satellite imagery and then integrated with historical landslide records and field
survey data to establish a comprehensive landslide inventory dataset.
(b) Identification of landslide conditioning factors: Twelve factors related to geological environment, geomorphology, mete-
orology and hydrology, vegetation and soil, and human engineering activities were selected to construct a landslide
influencing factor system.
(c) Assessing the correlation of landslide conditioning factors: The potential multicollinearity among landslide conditioning
factors was evaluated using the variance inflation factor (VIF) and tolerance, followed by Pearson correlation analysis
to assess the strength of the correlation between each pair of variables.

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(d) Constructing the modeling dataset: Datasets 1 and 2 were constructed according to the coupling method. Subse-
quently, each dataset was randomly split into training (70%) and testing (30%) subsets using the Python 3.11 pro-
gramming language.
(e) Model development and evaluation: The attribute values of landslide conditioning factors were extracted into datasets
1 and 2. Using the Python 3.11 programming language, the SVC, MLP, LDA, and LR models were employed to train
and test the data in datasets 1 and 2 at a 7:3 ratio. Model performance was assessed using AUC, accuracy (Acc),
precision, and F1 score.
(f) Landslide susceptibility mapping and evaluation: The vector data generated in the previous step were converted
to raster data using ArcGIS 10.8 software and classified into five categories using the natural breaks classification
Fig 4. Flow chart of technical research.
https://doi.org/10.1371/journal.pone.0322566.g004

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method. The classification performance and effect of each model and landslide susceptibility map were assessed
using ROC curves and landslide frequency ratios to determine the most suitable model and optimal landslide suscep-
tibility map for the study area.
Results
Multicollinearity diagnostics and pearson correlation analysis
Utilize ArcGIS 10.8 software to extract the attribute values of the 12 factors into the point attributes of the landslide dataset
and export the attribute table. Perform collinearity diagnosis analysis among the factors using the linear regression anal-
ysis module in SPSS 27.0 software, as presented in Table 2. Conduct Pearson correlation analysis between each pair of
factors using the correlation analysis module in SPSS 27.0, as illustrated in Fig 5.
In the multicollinearity analysis of the data, the tolerance values for all 12 conditioning factors exceed 0.2, and the VIF
values are below 5, suggesting that there is no multicollinearity among the factors.
In the Pearson correlation analysis, the Pearson correlation coefficients between all factors are below 0.8, suggesting
that there is no strong correlation between any pair of conditioning factors.
The relationship between conditioning factors and landslide relics
Utilize the “Extract Multi Values to Points” functionality in ArcGIS 10.8 to extract the attribute values of each conditioning
factor to the landslide relic data points. Subsequently, tally the number of disaster points and grid cells for each level of the
conditioning factors. Finally, employ an Excel spreadsheet to compute the values of a, b, FRij, Pij, Hi, Hi,max, Ii, Pi, and Wi
based on formulas (1) to (7) (Table 3).
The weight value (Wi) of the entropy index reflects the relationship between each conditioning factor and landslide relic
data. A larger weight value signifies a more substantial contribution of the factor to landslides, and conversely, a smaller
weight value indicates a lesser contribution. The calculation results demonstrate that in the entropy index model, the con-
ditioning factors contributing to landslides in descending order are land use > slope > DEM > TWI > lithology > rainfall > dis-
tance to road > NDVI > aspect > distance to river > distance to fault > profile curvature. Land use, slope, and elevation
significantly influence landslides, resulting in lower entropy values and higher weight values; in contrast, profile curvature
and distance to fault have a minimal impact, leading to higher entropy values and lower weight values.
In terms of land use, the density of landslide debris is greatest in grasslands and farmlands, followed by shrublands
and bare land. In high-altitude regions characterized by very high elevations, the proportion of farmland area is minimal,
thus necessitating a focus on landslides in grasslands, shrublands, and bare land. Regarding slopes, areas with slopes
of 15–30° exhibit the highest density of landslides and are most susceptible to landslides, whereas areas with slopes
exceeding 60° experience virtually no landslides. In terms of elevation, within the study area, lower elevations are associ-
ated with higher landslide densities, while higher elevations are linked to lower densities.
Table 2. VIF and TOL of all conditioning factors.
Factors Tolerances VIF Factors Tolerances VIF
Elevation 0.277 3.612 Rainfall 0.657 1.521
Slope 0.722 1.384 Distance to rivers 0.469 2.131
Aspect 0.987 1.013 NDVI 0.464 2.153
Profile curvature 0.904 1.106 TWI 0.750 1.334
Lithology 0.837 1.195 Land use 0.418 2.391
Distance to faults 0.846 1.182 Distance to roads 0.567 1.763
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The highest density of landslide debris in the study area is found between 3153 and 4000 m, largely due to the concen-
tration of human activities in areas below 4000 m in elevation, which also align with regions where rivers and roads are
densely located. Concerning the three distance parameters (distance to road, distance to fault, and distance to river), they
demonstrate an inverse correlation with landslide debris, indicating that the closer the distance, the greater the propensity
for landslides, with the highest incidence occurring within a 1km radius. The density of landslide debris is higher on slopes
facing southeast, south, and southwest, as the study area is situated in the Northern Hemisphere, where south-facing
slopes are sunny and more susceptible to landslides. Regarding rock strength, areas with weak rock, moderately weak
rock, and moderately hard rock are more prone to landslides compared to areas with hard rock.
Model evaluation
Accuracy analysis of single and coupled models. We employed the scikit-learn library in the Python 3.11
programming language to construct machine learning models. The datasets were divided into training and testing sets in a
7:3 ratio, and the SVC, MLP, LDA, and LR models were trained and tested on each dataset respectively. Precision of the
models was validated by plotting the ROC curves, as shown in Fig 6. During the actual model construction process, using
the default parameters may lead to some models failing to converge. Following extensive parameter tuning, the primary
settings for each model were established as follows: For the SVC model, probability = True and random state = 42; for
the MLP model, random state = 42, max iter = 1000, early stopping = True, and validation fraction = 0.1; for the LR model,
max iter = 1000 and random state = 42; the LDA model used the default values. To avoid bias caused by imbalanced data
distribution and to enhance the precision of model training, we employed the StratifiedKFold function for 5-fold cross-
validation with the following parameters: n splits = 5, shuffle = True, and random state = 42. During the model training
process, we calculated the 95% confidence intervals of AUC, Accuracy, Precision, and F1 values to evaluate the stability
and reliability of the models, as presented in Table 4.
Fig 5. Correlation coefficient matrix.
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Table 3. IOE model parametercal culation.
Factors Classes No. of landslides NO.Raster a b FRij Pij HiHi,max IiPiWi
DEM 3153-3500 107 203879 0.0425 0.0227 1.8715 0.3156 0.5251 2.3219 0.0912 1.186 0.1082
3500-4000 563 1126865 0.2238 0.1256 1.7816 0.3004 0.5212
4000-4500 933 2916445 0.3708 0.3251 1.1408 0.1924 0.4575
4500-5000 808 3301978 0.3211 0.3681 0.8726 0.1472 0.4068
>5000 105 1422193 0.0417 0.1585 0.2634 0.0444 0.1995
Slope 0-15 395 2134018 0.157 0.2379 0.6601 0.1829 0.4483 2.3219 0.1536 0.7218 0.1109
15-30 1476 4428231 0.5866 0.4936 1.1886 0.3293 0.5277
30-45 600 2195962 0.2385 0.2448 0.9744 0.27 0.51
45-60 45 204160 0.0179 0.0228 0.786 0.2178 0.4789
60-79 0 8989 0 0.001 0.0001 0 0.0004
Aspect Flat 4 24349 0.0016 0.0027 0.5859 0.0682 0.2643 3.1699 0.0263 0.9542 0.0251
North 240 1308011 0.0954 0.1458 0.6544 0.0762 0.283
Northeast 316 1249772 0.1256 0.1393 0.9017 0.105 0.3414
East 232 1005584 0.0922 0.1121 0.8228 0.0958 0.3242
Southeast 365 1049069 0.1451 0.1169 1.2407 0.1445 0.4033
South 500 1198900 0.1987 0.1336 1.4872 0.1732 0.4381
Southwest 440 1145541 0.1749 0.1277 1.3697 0.1595 0.4224
West 258 940678 0.1025 0.1049 0.9781 0.1139 0.357
Northwest 161 1049456 0.064 0.117 0.5471 0.0637 0.2531
Profile Curvature 0-0.5 584 2145494 0.2321 0.2391 0.9707 0.1872 0.4525 2.3219 0.0018 1.0372 0.0019
0.5-1 860 3209089 0.3418 0.3577 0.9557 0.1843 0.4497
1-1.5 570 2008226 0.2266 0.2238 1.0122 0.1952 0.4601
1.5-2 291 968956 0.1157 0.108 1.071 0.2065 0.47
2-4.3 211 639595 0.0839 0.0713 1.1764 0.2268 0.4855
Lithology Hard Rock 164 1554255 0.0652 0.1732 0.3763 0.0878 0.3082 2.3219 0.0487 0.857 0.0417
Loose Rock 5 31239 0.002 0.0035 0.5708 0.1332 0.3874
Harder Rock 500 1673218 0.1987 0.1865 1.0656 0.2487 0.4993
Weaker Rock 1195 3528036 0.475 0.3933 1.2079 0.2819 0.515
Weak Rock 652 2184612 0.2591 0.2435 1.0643 0.2484 0.4991
Distance to faults 0-1000 730 2164114 0.2901 0.2412 1.2029 0.2453 0.4973 2.3219 0.0072 0.9808 0.0071
1000-2000 558 1792466 0.2218 0.1998 1.1101 0.2264 0.4852
2000-3000 354 1393814 0.1407 0.1554 0.9057 0.1847 0.4501
3000-4000 228 1015109 0.0906 0.1131 0.801 0.1633 0.427
>4000 646 2605857 0.2568 0.2905 0.8841 0.1803 0.4456
Distance to rivers 0-1000 334 782554 0.1328 0.0872 1.522 0.2701 0.5101 2.3219 0.011 1.1268 0.0124
1000-2000 236 729452 0.0938 0.0813 1.1537 0.2048 0.4685
2000-3000 215 701359 0.0855 0.0782 1.0932 0.194 0.459
3000-4000 182 678651 0.0723 0.0756 0.9564 0.1698 0.4343
>4000 1549 6079344 0.6157 0.6776 0.9086 0.1613 0.4245
Rain fall 574.9-598.5 684 1850462 0.2719 0.2063 1.3181 0.289 0.5176 2.3219 0.0351 0.9122 0.032
598.5-610.5 912 2863946 0.3625 0.3192 1.1356 0.249 0.4994
610.5-624.8 557 2391226 0.2214 0.2665 0.8307 0.1821 0.4475
624.8-644.2 280 1205430 0.1113 0.1344 0.8284 0.1816 0.447
644.2-688.4 83 660296 0.033 0.0736 0.4483 0.0983 0.329
(Continued)

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As depicted in Fig 6, the AUC, Accuracy, Precision, and F1 values of the IOE and machine learning coupled models
are significantly higher than those of the individual models. The AUC values increased from a range of 0.7822 to 0.8508
to a range of 0.9378 to 0.9747, the precision increased from a range of 0.7176–0.7682 to a range of 0.8942–0.9836,
accuracy increased from a range of 0.722–0.777 to a range of 0.8848–0.9385, and F1 scores increased from a range of
0.7248–0.7807 to a range of 0.88–0.9343. Among the individual models, SVC and MLP exhibited superior performance,
with the SVC model performing the best. Among the coupled models, all four demonstrated excellent performance. The
performance ranking from best to worst is IOE-MLP, IOE-SVC, IOE-LR, and IOE-LDA. Notably, the IOE-MLP and IOE-
SVC models significantly outperformed the IOE-LR and IOE-LDA models.
As shown in Table 4, the relatively narrow confidence intervals of all models indicate good stability and reliability.
Compared to individual models, the coupled models exhibit narrower confidence intervals, indicating superior stability and
reliability.
Comparative analysis of model performance. Given that individual models perform worse than coupled models,
only the performance of coupled models is compared and analyzed in this section. Using the Python 3.11 programming
language and the SVC, MLP, LDA, and LR models with dataset 2, we conducted a landslide susceptibility analysis for
Luolong County and generated a landslide susceptibility map. Subsequently, the susceptibility map was classified into five
levels—extremely high, high, moderate, low, and very low susceptibility—using the natural breaks classification method
in ArcGIS 10.8, as shown in Fig 7. The original landslide points were overlaid on the susceptibility map to statistically
Factors Classes No. of landslides NO.Raster a b FRij Pij HiHi,max IiPiWi
NDVI 0.01-0.18 244 1884824 0.097 0.2101 0.4617 0.0918 0.3163 2.3219 0.0273 1.0056 0.0275
0.18-0.33 384 1245576 0.1526 0.1388 1.0994 0.2187 0.4796
0.33-0.45 685 2110228 0.2723 0.2352 1.1576 0.2302 0.4878
0.45-0.57 754 2365126 0.2997 0.2636 1.1368 0.2261 0.485
0.57-0.92 449 1365606 0.1785 0.1522 1.1725 0.2332 0.4898
TWI 5.10-8.70 725 3382073 0.2882 0.377 0.7645 0.1856 0.451 2.3219 0.0599 0.8236 0.0493
8.70-10.59 1265 3856751 0.5028 0.4299 1.1696 0.284 0.5158
10.59-13.75 466 1282035 0.1852 0.1429 1.2962 0.3147 0.5249
13.75-18.16 52 378884 0.0207 0.0422 0.4895 0.1189 0.3652
18.16-28.17 8 71617 0.0032 0.008 0.3984 0.0967 0.326
Land use Forest 254 1644262 0.101 0.1833 0.5509 0.1114 0.3527 3.1699 0.2449 0.5496 0.1346
Shrub 45 161355 0.0179 0.018 0.9945 0.201 0.4653
Grass 1994 5099231 0.7925 0.5684 1.3944 0.2819 0.515
Wetland 0 6036 0 0.0007 0.0001 0 0.0003
Cropland 1 2974 0.0004 0.0003 1.1991 0.2424 0.4956
Buildup 0 369 0 0 0.0001 0 0.0003
Snow/lce 68 1113569 0.027 0.1241 0.2178 0.044 0.1984
Bareground 154 931430 0.0612 0.1038 0.5896 0.1192 0.3658
Waterbody 0 12134 0 0.0014 0.0001 0 0.0003
Distance to roads 0-1000 875 1946415 0.3478 0.217 1.6031 0.31 0.5238 2.3219 0.0279 1.0342 0.0289
1000-2000 456 1584458 0.1812 0.1766 1.0263 0.1985 0.463
2000-3000 366 1351537 0.1455 0.1507 0.9657 0.1867 0.4521
3000-4000 291 1095728 0.1157 0.1221 0.9471 0.1831 0.4485
>4000 528 2993222 0.2099 0.3336 0.6291 0.1217 0.3697
https://doi.org/10.1371/journal.pone.0322566.t003
Table 3. (Continued)

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analyze the number of landslide points and grid cells at each level, and to calculate the landslide density and landslide
frequency ratio (the proportion of landslide points at each level divided by the proportion of the area at each level), as
presented in Table 5.
As indicated by the results in Table 4, the landslide frequency ratios of the four coupled models decrease progres-
sively from the extremely high susceptibility zones to the very low susceptibility zones. This suggests that the classifica-
tion of landslide susceptibility by each coupled model is rational. Specifically, in the extremely high susceptibility zones,
the ranking of landslide frequency ratios is as follows: IOE-MLP > IOE-SVC > IOE-LR > IOE-LDA. This indicates that the
Fig 6. ROC curves of different models.
https://doi.org/10.1371/journal.pone.0322566.g006
Table 4. The confidence intervals of AUC, Accuracy, Precision and F1 values for different models.
Model AUC Accuracy Precision F1
Value CI. Value CI. Value CI. Value CI.
IOE-SVC 0.9733 [0.9708, 0.9804] 0.9385 [0.9200, 0.9488] 0.9836 [0.9464, 0.9823] 0.9343 [0.9174, 0.9470]
IOE-MLP 0.9747 [0.9567, 0.9735] 0.9365 [0.9126, 0.9316] 0.9655 [0.9283, 0.9517] 0.9332 [0.9106, 0.9299]
IOE-LDA 0.9378 [0.9274, 0.9442] 0.8882 [0.8730, 0.8950] 0.8942 [0.8675, 0.8929] 0.8851 [0.8724, 0.8955]
IOE-LR 0.9408 [0.9350, 0.9475] 0.8848 [0.8751, 0.9014] 0.9024 [0.8811, 0.8988] 0.88 [0.8726, 0.9020]
SVC 0.8508 [0.8539, 0.8838] 0.777 [0.7771, 0.8113] 0.7682 [0.7676, 0.8080] 0.7807 [0.7812, 0.8142]
MLP 0.8172 [0.8136, 0.8728] 0.7545 [0.7421, 0.8004] 0.7427 [0.7341, 0.7907] 0.7605 [0.7449, 0.8038]
LDA 0.7822 [0.7774, 0.7996] 0.722 [0.7076, 0.7321] 0.7176 [0.6992, 0.7245] 0.7251 [0.7073, 0.7396]
LR 0.7828 [0.7776, 0.8001] 0.722 [0.7048, 0.7340] 0.7182 [0.6956, 0.7250] 0.7248 [0.7058, 0.7432]
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classification performance of the four models, from best to worst, is IOE-MLP, IOE-SVC, IOE-LR, and IOE-LDA. Overall,
the coupled models outperform the individual models. The IOE-MLP and IOE-SVC models perform better than the IOE-LR
and IOE-LDA models. The IOE-MLP model outperforms the IOE-SVC model.
Therefore, considering both model performance and classification effectiveness, the IOE-MLP model is the best.
Spatial distribution of landslide susceptibility
As depicted in Fig 7, the spatial distribution of the evaluation results for the four coupled models (IOE-MLP, IOE-LR, IOE-
SVC, and IOE-LDA) exhibit significant similarities. The very high susceptibility zones for landslides in Luolong County
Fig 7. Vulnerability level diagram of different coupling models.
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are predominantly aligned with river and road networks, particularly extending from the northwest to the southeast of the
county, with additional concentrations observed in the western and southern regions. Several factors contribute to this
spatial pattern: Firstly, the Nu River, which is the second largest river in Tibet, flows through the county from northwest
to southeast. Additionally, numerous rivers such as Zhuomalongcuoqu and Daqu course through the western part, while
Tongcuoqu, Dongcuoqu, Kangyucuo, and Baqu traverse the southern part. These riverine areas possess a high topo-
graphic wetness index, which facilitates landslide development. Secondly, Luolong County features prominently incised
canyon topography, where rivers have deeply incised the landscape. In these areas, the rocks are soft, the elevation is
low, and there is a prevalence of faults and well-developed fractures, all of which facilitate the formation of landslides.
Thirdly, the average elevation of Luolong County is approximately 3859 m, which is quite high. Human settlements are
typically located in the relatively lower-elevation river valleys, and most roads within the county are constructed along
these valley areas. The frequent human engineering activities in these regions contribute to the occurrence of landslides.
Fourthly, the southwestern part of Luolong County is dominated by the high-altitude Nyenchen Tanglha Mountains, while
the northern part is characterized by the Tengri nor Mountain. These areas are primarily composed of high-altitude gla-
ciers, where landslides are relatively rare. In contrast, the central region, which is predominantly grassland, is a zone
where landslides are highly developed.
Conclusion and discussion
Discussion
Characteristics of landslide conditioning factors in Luolong county. This study utilized imagery from the Google
Earth platform, employing a human-computer interactive visual interpretation method to identify historical landslides
in Luolong County, revealing a total of 2517 landslide scars of varying sizes. Landslides in this region are primarily
Table 5. Classification performance of different coupling models.
Model Landslide Suscep-
tibility Zone Levels
Number of
Grid Cells
Area Pro-
portion
Number of
Landslides
Landslide
number ratio
Landslide Fre-
quency Ratio
IOE-LDA Very low 1968682 0.22 179 0.07 0.32
low 455727 0.05 81 0.03 0.63
middle 634570 0.07 154 0.06 0.87
high 1238941 0.14 372 0.15 1.07
Very high 4673440 0.52 1731 0.69 1.32
IOE-LR Very low 1528129 0.17 117 0.05 0.27
low 994456 0.11 149 0.06 0.53
middle 1019448 0.11 264 0.10 0.92
high 1937187 0.22 584 0.23 1.07
Very high 3492140 0.39 1403 0.56 1.43
IOE-SVC Very low 2086945 0.23 150 0.06 0.26
low 517791 0.06 64 0.03 0.44
middle 558101 0.06 50 0.02 0.32
high 930956 0.10 101 0.04 0.39
Very high 4877567 0.54 2152 0.85 1.57
IOE-MLP Very low 1813910 0.20 93 0.04 0.18
low 710494 0.08 99 0.04 0.50
middle 764341 0.09 100 0.04 0.47
high 1248072 0.14 236 0.09 0.67
Very high 4434543 0.49 1989 0.79 1.60
https://doi.org/10.1371/journal.pone.0322566.t005

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influenced by geological settings, geomorphological background, and human engineering activities, representing the
combined effect of multiple factors. The relative importance of the 12 factors investigated in this study is ranked as follows:
land use type > slope > DEM > TWI > lithology > rainfall > distance to roads > NDVI > aspect > distance to rivers > distance to
faults > profile curvature. Among these, land use type, slope, and elevation are identified as the dominant factors. Firstly,
land use is one of the main causes of the high incidence of landslides in Luolong County. Due to the high altitude and cold
climate of Luolong County, large trees with extensive root systems cannot thrive, and only a limited variety of grasses with
shallow roots can adapt to the local environment. This results in sparse vegetation and a relatively fragile ecosystem[52].
Consequently, the vegetation types in Luolong County are distributed as follows: grasslands account for 79.25%, forests
for 10.10%, glaciers for 2.70%, bare land for 6.12%, and other types for 1.83% of the total area. Secondly, slope is also
one of the significant factors contributing to the susceptibility of landslides in Luolong County. The elevation in Luolong
County ranges from 5476 m to 3153 m, with a difference of over 2000 m, and areas above 4000 m account for 85% of the
total area. The greater the slope gradient and elevation difference, the more efficiently the potential energy of a landslide
mass is converted into kinetic energy during movement, thereby increasing the velocity of the landslide[53]. Within the
study area, regions with slopes between 15° and 30° comprise 58.66% of the area, while those between 30° and 45°
account for 23.85%. This range of slopes is considered the most sensitive for landslides. Consequently, the significant
elevation differences and the large proportion of areas prone to landslides are key reasons for the frequent occurrence of
landslides, including high-velocity and long-runout landslides, in Luolong County. Thirdly, elevation is one of the primary
factors promoting the development of landslides in Luolong County. The impact of elevation on landslides is not only
evident in its influence on the stability of slopes through topography and geomorphology [54], but it also reflects the
distribution characteristics of factors such as land use, distance to rivers and roads, and topographic wetness index.
Characteristics of landslide spatial distribution in Luolong county. Spatial analysis reveals that landslides in
Luolong County are extensively and densely distributed. The areas of very high and high susceptibility are predominantly
located in the central part of the county, characterized by relatively low elevations, proximity to roads and rivers, and high
soil moisture content. The susceptibility and density of landslides decrease with increasing distance from faults, rivers, and
roads. Slopes facing southwest, south, and southeast are more prone to landslides, which is consistent with the findings
of previous researchers[55,56]. High susceptibility areas are characterized by elevations below 4000 m, steep slopes,
dense faulting, and weak to moderately weak geological lithology. They experience moderate rainfall, have a high density
of human engineering activities, and are predominantly covered by grasslands. Luolong County, characterized by high
elevation and significant topographic relief, is traversed by the Nu River and its tributaries, resulting in the formation of
typical incised valley landforms [57]. The weak rock layers in Luolong County cover 73.61% of the total area and exhibit low
strength and high deformability. They primarily consist of sandstone, siltstone, shale, conglomerate, and slate, which are
susceptible to longitudinal and lateral erosion by rivers. This results in deep surface incision, significant elevation changes,
and a high topographic wetness index. These regions often experience more intense tectonic activity, with frequent faults
and earthquakes, leading to rock fragmentation and facilitating the development of landslides. Additionally, it is commonly
accepted that elevations above 4000 m are unsuitable for long-term human habitation. With an average elevation of 3859
m in Luolong County, people typically reside along rivers, constructing their homes, roads, and living facilities on relatively
lower-elevation terraces and gentle valley areas. Due to the inherent advantages of rivers with significant elevation drops
for hydroelectric power generation, hydropower stations are also constructed in valley areas. Human engineering activities
increase the susceptibility of these areas to landslides, resulting in high landslide susceptibility zones being located
close to and along rivers. Additionally, these areas are also near roads and have relatively lower elevations. The very low
susceptibility areas are primarily located in the Tengnate Mountain Range in the southwestern part of Luolong County, the
Nyenchen Tanglha Mountain Range in the northern part, and some areas with relatively low slopes in the central region.
These areas are characterized by elevations above 4500 m, perennial snow and ice cover, minimal human presence, and
predominantly hard and moderately hard rock types, which are not conducive to the development of landslides.

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Comparison with other studies. Situated in a high-altitude and cold region, the southeastern Tibetan Plateau is
characterized by high elevation, thin air, and sparse population, rendering geological surveys extremely challenging and
the acquisition of samples highly difficult. Many researchers have focused on using algorithms and mapping units to
enhance the accuracy of landslide susceptibility assessments. For instance, Mao Yimin et al. integrated SVM, RF, DT
(Decision Tree), LR, FL (Fuzzy Logic), and TOPSIS to improve landslide susceptibility assessments in the Urmia Lake
basin [13]. Yu Lanbing et al. applied LR, RF, SVC, and DL models to evaluate landslide-prone areas in the mid-to-upper
reaches of the Three Gorges Reservoir region in Chongqing, China, achieving satisfactory results [45]. However, these
studies often overlook the completeness and quality of samples. Incomplete samples can lead to insufficient information
for training machine learning models, while poor-quality samples may cause misclassification, thereby affecting the
final prediction outcomes. This study established a relatively comprehensive landslide database for Luolong County
using remote sensing interpretation techniques and coupled the IOE model with machine learning models, optimizing
the non-landslide samples during the coupling process. On the one hand, this approach enhanced the completeness of
landslide samples and the quality of non-landslide samples, thereby improving prediction accuracy. On the other hand, the
statistical learning model retained the spatial distribution characteristics of landslides, while the coupled model enhanced
both prediction accuracy and precision.
Located in the Mediterranean-Himalayan seismic belt, the landslide influencing factors in Luolong County differ from
those in inland China. Inland China is predominantly characterized by rainfall-induced landslides, with rainfall, elevation,
and slope being the dominant factors. Areas with high susceptibility are typically found in mountainous and hilly regions
with high rainfall, elevation, and slope [17,58,59]. In Luolong County, landslides are induced by non-rainfall. Among the
12 factors investigated in this study, the contribution of rainfall ranks sixth. The northern and northwestern regions, which
receive the highest rainfall, are categorized as low and very low susceptibility zones. Conversely, some eastern areas with
low rainfall are classified as high susceptibility zones. Rainfall increases soil moisture content, which can destabilize rock
masses and promote landslide occurrence. However, it also supports the growth of water-demanding arboreal vegetation.
The extensive root systems of these trees can stabilize slopes and reduce landslide likelihood. As a result, areas with
dense forests in the northern and northwestern regions exhibit lower landslide susceptibility. Landslides in Luolong County
are more earthquake-induced. The southeastern Tibetan Plateau, located at the collision zone between the Eurasian and
Indian Ocean plates, is one of the most tectonically active and geologically complex regions globally [60,61]. Seismic
factors significantly influence landslides in the southeastern Tibetan Plateau [62]. Over 50% of large- to mega-scale land-
slides along the Tibetan Plateau margin are triggered by paleo earthquakes or historical earthquakes [63]. The combined
effect of heavy rainfall and strong earthquakes further increases the likelihood of landslide occurrence [64]. Faults are the
primary sources of seismic activity, and fault density often indirectly reflects the frequency of earthquakes. The eastern
region, with low rainfall, dense faults, low elevation, and intensive human engineering activities, exhibits high landslide
susceptibility.
Future research directions
Machine learning is data-driven and relies on large-scale training samples to support model training. In machine learning,
small-sample issues are typically addressed through oversampling, which can, however, strengthen the information of a
single class and lead to model overfitting. Sample imbalance primarily manifests as discrepancies between sample quanti-
ties and training data [65]. This issue can be addressed through oversampling or under sampling, both of which can either
enhance or diminish certain class information, potentially leading to loss of information and exacerbating sample imbal-
ance if not handled properly. This study employs the IOE model to optimize the sampling of non-landslide samples; how-
ever, it does not address the issue of sample imbalance. In recent years, researchers have explored various strategies to
address sample imbalance. For instance, Yang Yajie et al. employed a sampling method based on Frequency Ratio (FR)
and SBAS-InSAR interpretation results to improve sample quality [66]. Wang Lixia et al. utilized a Faster R-CNN landslide

PLOS One | https://doi.org/10.1371/journal.pone.0322566 May 9, 2025 23 / 27
target detection method with multi-source imbalanced samples to address sample imbalance [65]. Liu Mengmeng et al.
optimized samples using a fuzzy C-means method, thereby enhancing the training accuracy and reliability of landslide
susceptibility models [67]. Wu Hongyang et al. effectively resolved sample imbalance in their study area and improved
landslide prediction accuracy by optimizing samples using a hybrid sampling method coupled with semi-supervised
classification and deep neural network models [68]. Given the diverse types and characteristics of samples, optimization
methods vary accordingly. Future research can explore additional approaches to optimize samples, address small-sample
and imbalance issues, and thereby enhance the prediction accuracy of machine learning models.
Landslide occurrence is closely related to the mechanical properties of rock masses, such as strength, deformation
characteristics, and fracture development. Traditional landslide susceptibility assessments predominantly rely on empir-
ical or physically-based mechanical models, which often face challenges such as difficulties in parameter acquisition
and computational complexity in complex geological conditions. Yang Liu utilized rainfall data, topographic parameters,
and hydrological parameters to determine the slope safety factor under rainfall conditions, thereby selecting safer non-
landslide samples. This approach, which couples the TRIGRS physical model with the Random Forest algorithm, offers a
novel perspective for predicting the susceptibility of shallow rainfall-induced landslides [69]. Integrating physically-based
mechanical models with machine learning models to develop landslide susceptibility assessment methods that balance
mechanical mechanisms and interpretability represents a promising direction for future research.
High-altitude cold regions are characterized by extensive deep permafrost layers, with some areas covered by snow
and ice. Repeated freeze-thaw cycles lead to the destruction of soil structure and the degradation of mechanical prop-
erties, thereby triggering landslides. In recent years, some researchers have focused on this process. For instance, Guo
Yanchen employed a Random Forest model to investigate the impact of freeze-thaw cycles on landslides in the Nanqian
area of Qinghai, China, achieving satisfactory results [70]. With the rise in global temperatures, the accelerated melting
of snow and ice, the degradation of permafrost, and the expansion of seasonal frozen ground have made the study of the
relationship between freeze-thaw cycles and landslides in high-altitude cold regions an urgent research direction.
Conclusion
The southeastern Tibet region is not only a crucial passage for the Sichuan-Tibet and Yunnan-Tibet railways but also a
significant area for major water conservancy projects in China. It is part of the “Asian Water Tower” and the Three Riv-
ers Source region and serves as a natural museum of geological disasters in China. This study takes Luolong County in
southeastern Tibet as the research area, employing an optimized sampling strategy for non-landslide samples. It inte-
grates the IOE model from statistics with various machine learning models to conduct landslide susceptibility mapping.
The precision and effectiveness of each model are comprehensively assessed using AUC, Acc, Precision, and F1 scores.
The conclusions drawn are as follows:
(1) Optimizing non-landslide samples using the IOE model reduces the probabilistic misclassification issues during the
selection of non-landslide samples in machine learning models. Consequently, the IOE-coupled machine learning
models (IOE-SVC, IOE-MLP, IOE-LDA, and IOE-LR) not only preserve the spatial characteristics of landslide evalua-
tion inherent in statistical methods but also enhance prediction accuracy. The AUC, Acc, Precision, and F1 values of
the four coupled models optimized with non-landslide samples are all higher than those of the single models before
optimization. Specifically, the AUC values increased from a range of 0.7822 to 0.8508 to a range of 0.9378 to 0.9747,
the precision increased from a range of 0.71760.7682 to a range of 0.8942–0.9836, accuracy increased from a range
of 0.722–0.777 to a range of 0.8848–0.9385, and F1 scores increased from a range of 0.72480.7807 to a range of
0.88–0.9343. Among the four coupled models, the IOE-MLP model demonstrated superior performance, achieving
the best overall performance and effectiveness. It provided a rational classification of landslide susceptibility zones,
achieved the highest AUC value, and exhibited the best classification performance. Moreover, it had the highest land-
slide frequency ratio in the extremely high susceptibility zone, achieving the best classification effect.

PLOS One | https://doi.org/10.1371/journal.pone.0322566 May 9, 2025 24 / 27
(2) In terms of conditioning factors, landslides in Luolong County are influenced by a combination of geological environ-
ment, topography and geomorphology, and human engineering activities. They result from the interaction of multiple
factors, with land use, elevation, and slope being the primary controlling factors.
(3) At the spatial scale, the areas of very high susceptibility to landslides in Luolong County are distributed around the
Nu River and its tributaries, extending from the northwest to the southeast. This distribution aligns with the orientation
of the Nu River, faults, and the trends of hard and weak rocks. The areas of very low susceptibility are predominantly
found in the high-altitude, snow-covered regions surrounding the county.
In summary, optimizing non-landslide samples using the IOE model and subsequently employing machine learning
coupled models for landslide susceptibility assessment yields superior results. We should focus on the impact of key con-
ditioning factors such as land use, elevation, and slope, particularly in high mountain gorge areas that are closer to rivers
and roads and have dense human engineering activities. In these areas of very high landslide susceptibility, it is crucial to
enhance community-based prevention and control efforts, invest in advanced prediction and early warning hardware, and
improve the capacity to respond to landslide disasters.
Acknowledgments
This research was carried out under the professional guidance of Professor Gao Mingxing from Xinjiang University and
Professor Xu Chong from the National Institute of Natural Hazards, Ministry of Emergency Management of China.
Author contributions
Conceptualization: Kong Yuzhong, Zhu Kangcheng, Zhang Chenguang.
Data curation: Kong Yuzhong.
Formal analysis: Kong Yuzhong, Wu Hua, Zhu Kangcheng.
Funding acquisition: Wu Hua.
Investigation: Kong Yuzhong, Xu Chong, Sun Jingjing, Zhu Kangcheng.
Methodology: Kong Yuzhong, Zhang Chenguang, Zhou Jianwei, Xu Tong, Su Taijin, Zhang Zelin, Kong Hui.
Project administration: Kong Yuzhong, Wu Hua, Zhang Chenguang, Zhou Jianwei.
Resources: Kong Yuzhong, Wu Hua, Zhang Chenguang, Zhou Jianwei.
Software: Kong Yuzhong, Xu Chong, Sun Jingjing, Zhu Kangcheng.
Supervision: Kong Yuzhong, Wu Hua, Xu Chong, Sun Jingjing, Zhang Chenguang, Zhou Jianwei, Xu Tong, Su Taijin,
Zhang Zelin, Kong Hui.
Validation: Kong Yuzhong, Xu Chong, Sun Jingjing.
Visualization: Kong Yuzhong, Zhu Kangcheng.
Writing – original draft: Kong Yuzhong.
Writing – review & editing: Kong Yuzhong, Xu Chong.
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