Integrated Landslide Risk Assessment via a Landslide Susceptibility Model Based on Intelligent Optimization Algorithms Article Swipe
Xin Dai
,
Jianping Chen
,
Tianren Zhang
,
Chenli Xue
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17030545
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17030545
Accurate and objective regional landslide risk assessment is crucial for the precise prevention of regional disasters. This study proposes an integrated landslide risk assessment via a landslide susceptibility model based on intelligent optimization algorithms. By simulating the process of rime frost formation, it effectively selects features and assigns weights, overcoming the overfitting issue faced by XGBoost in handling high-dimensional features. By integrating the concepts of landslide susceptibility, dynamic landslide factors, and social vulnerability, an integrated landslide risk index was developed. Further investigation was conducted on how landslide susceptibility results influence risk, identifying regions with varying levels of landslide risk due to spatial heterogeneity in geological background, natural environment, and socio-economic conditions. This study’s results demonstrate that the RIME-XGBoost landslide susceptibility model exhibits superior stability and accuracy, achieving an AUC score of 0.947, which represents an improvement of 0.064 compared to the unoptimized XGBoost model, while the accuracy shows a maximum increase of 0.15 relative to other models. Additionally, an analysis using cloud theory indicates that the model’s expectation and hyper-entropy are minimized. High-risk-level areas, constituting only 1.26% of the total area, are predominantly located in densely populated, economically developed urban regions, where roads and rivers are the key influencing factors. In contrast, low-risk areas, which cover approximately 72% of the total area, are more broadly distributed. The landslide susceptibility predictions notably influence high-risk regions with concentrated populations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17030545
- OA Status
- gold
- Cited By
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- References
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4407223498Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17030545Digital Object Identifier
- Title
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Integrated Landslide Risk Assessment via a Landslide Susceptibility Model Based on Intelligent Optimization AlgorithmsWork 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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2025-02-05Full publication date if available
- Authors
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Xin Dai, Jianping Chen, Tianren Zhang, Chenli XueList of authors in order
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https://doi.org/10.3390/rs17030545Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/rs17030545Direct OA link when available
- Concepts
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Landslide, Computer science, Algorithm, Geology, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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74Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.1.26% | 176 |
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| abstract_inverted_index.roads | 192 |
| abstract_inverted_index.score | 129 |
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| abstract_inverted_index.while | 144 |
| abstract_inverted_index.0.947, | 131 |
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| abstract_inverted_index.model, | 143 |
| abstract_inverted_index.rivers | 194 |
| abstract_inverted_index.social | 71 |
| abstract_inverted_index.theory | 162 |
| abstract_inverted_index.Further | 80 |
| abstract_inverted_index.XGBoost | 55, 142 |
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| abstract_inverted_index.spatial | 101 |
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| abstract_inverted_index.concepts | 63 |
| abstract_inverted_index.exhibits | 121 |
| abstract_inverted_index.factors, | 69 |
| abstract_inverted_index.factors. | 199 |
| abstract_inverted_index.features | 45 |
| abstract_inverted_index.handling | 57 |
| abstract_inverted_index.increase | 150 |
| abstract_inverted_index.low-risk | 202 |
| abstract_inverted_index.proposes | 18 |
| abstract_inverted_index.regional | 3, 14 |
| abstract_inverted_index.regions, | 190 |
| abstract_inverted_index.relative | 153 |
| abstract_inverted_index.superior | 122 |
| abstract_inverted_index.weights, | 48 |
| abstract_inverted_index.accuracy, | 125 |
| abstract_inverted_index.achieving | 126 |
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| abstract_inverted_index.contrast, | 201 |
| abstract_inverted_index.developed | 188 |
| abstract_inverted_index.features. | 59 |
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| abstract_inverted_index.indicates | 163 |
| abstract_inverted_index.influence | 89, 221 |
| abstract_inverted_index.landslide | 4, 21, 26, 65, 68, 75, 86, 97, 118, 217 |
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| abstract_inverted_index.study’s | 112 |
| abstract_inverted_index.assessment | 6, 23 |
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| abstract_inverted_index.disasters. | 15 |
| abstract_inverted_index.formation, | 41 |
| abstract_inverted_index.geological | 104 |
| abstract_inverted_index.integrated | 20, 74 |
| abstract_inverted_index.minimized. | 171 |
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| abstract_inverted_index.populated, | 186 |
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| abstract_inverted_index.represents | 133 |
| abstract_inverted_index.simulating | 35 |
| abstract_inverted_index.algorithms. | 33 |
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| abstract_inverted_index.conditions. | 110 |
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| abstract_inverted_index.improvement | 135 |
| abstract_inverted_index.influencing | 198 |
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| abstract_inverted_index.predictions | 219 |
| abstract_inverted_index.unoptimized | 141 |
| abstract_inverted_index.RIME-XGBoost | 117 |
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| abstract_inverted_index.economically | 187 |
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| abstract_inverted_index.hyper-entropy | 169 |
| abstract_inverted_index.investigation | 81 |
| abstract_inverted_index.predominantly | 182 |
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| abstract_inverted_index.vulnerability, | 72 |
| abstract_inverted_index.High-risk-level | 172 |
| abstract_inverted_index.susceptibility, | 66 |
| abstract_inverted_index.high-dimensional | 58 |
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| institutions_distinct_count | 4 |
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| citation_normalized_percentile.is_in_top_10_percent | True |