A Method for Identifying Landslide-Prone Areas Using Multiple Factors and Adaptive Probability Thresholds: A Case Study in Northern Tongren, Longwu River Basin, Qinghai Province Article Swipe
Jiawen Bao
,
Xiaojun Luo
,
Yueling Shi
,
Mingyue Hou
,
Jichao Lv
,
Guoxiang Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17081380
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17081380
Early and accurate identification of landslide-prone areas is critical for monitoring and early-warning systems, forming the foundation of disaster prevention and mitigation. However, current landslide susceptibility assessment methods often rely on arbitrary probability classification thresholds, leading to subjective and regionally non-adaptive results that neglect low-susceptibility areas, thereby limiting their practical utility in disaster management. To address these limitations, this study proposes a novel method for identifying landslide-prone areas by integrating multi-factor analysis with adaptive probability thresholds. The methodology combines landslide catalog data with key landslide influencing factors, including geology, topography, precipitation, surface deformation, and human activities. The gradient boosting decision tree (GBDT) algorithm is employed to estimate landslide susceptibility probabilities, while an adaptive threshold criterion—based on minimizing the Jensen–Shannon (JS) divergences weighted sum between landslide-prone areas and positive samples—is established to objectively classify regions. Validation experiments were conducted in the northern Tongren region of the Longwu River Basin, Qinghai Province, China. Historical landslides (February 2016–June 2017) were used for model training, and subsequent landslides (June 2017–November 2022) served as validation data. The results demonstrate exceptional performance: the susceptibility model achieved an AUC value of 0.99, with 94.07% accuracy in classifying landslides positive samples. Furthermore, 77.78% of post-2017 landslides occurred within the identified prone areas, yielding a 22.22% omission rate. These findings highlight the method’s ability to dynamically adapt to regional characteristics, balance sensitivity and specificity, and provide actionable insights for landslide risk management.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17081380
- https://www.mdpi.com/2072-4292/17/8/1380/pdf?version=1744450490
- OA Status
- gold
- Cited By
- 1
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409440027
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409440027Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17081380Digital Object Identifier
- Title
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A Method for Identifying Landslide-Prone Areas Using Multiple Factors and Adaptive Probability Thresholds: A Case Study in Northern Tongren, Longwu River Basin, Qinghai ProvinceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-12Full publication date if available
- Authors
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Jiawen Bao, Xiaojun Luo, Yueling Shi, Mingyue Hou, Jichao Lv, Guoxiang LiuList of authors in order
- Landing page
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https://doi.org/10.3390/rs17081380Publisher landing page
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https://www.mdpi.com/2072-4292/17/8/1380/pdf?version=1744450490Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/17/8/1380/pdf?version=1744450490Direct OA link when available
- Concepts
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Structural basin, Landslide, Geology, Physical geography, Hydrology (agriculture), Environmental science, Remote sensing, Geomorphology, Geography, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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