Evaluation of landslide susceptibility of mountain highway based on RF and SVM models
Abstract Geological complexities along mountain highways frequently trigger landslides, posing significant threats to transportation safety and infrastructure. This study evaluates landslide susceptibility along the Lizha-Jiezi section of China’s G345 national highway using Random Forest (RF) and Su...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-08774-w |
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| author | Qingfeng He Shoulong Wu Xia Zhao Zhankun Hui Zhengang Wang Paraskevas Tsangaratos Ioanna Ilia Wei Chen Yixin Chen Yiheng Hao |
| author_facet | Qingfeng He Shoulong Wu Xia Zhao Zhankun Hui Zhengang Wang Paraskevas Tsangaratos Ioanna Ilia Wei Chen Yixin Chen Yiheng Hao |
| author_sort | Qingfeng He |
| collection | DOAJ |
| description | Abstract Geological complexities along mountain highways frequently trigger landslides, posing significant threats to transportation safety and infrastructure. This study evaluates landslide susceptibility along the Lizha-Jiezi section of China’s G345 national highway using Random Forest (RF) and Support Vector Machine (SVM) models. Eleven conditioning factors including altitude, slope, aspect, plan curvature, profile curvature, lithology, distance to fault, rainfall, distance to river, normalized difference vegetation index (NDVI), and distance to road were analyzed using remote sensing and field surveys. A landslide inventory of 67 events was divided into training (70%) and validation (30%) datasets, with non-landslide samples selected at least 100 m away from landslide locations to minimize spatial overlap. Factor contribution analysis identified distance to road as the most significant predictor, highlighting anthropogenic impacts on slope destabilization. Model validation via receiver operating characteristic (ROC) curves demonstrated RF’s superior performance (AUC = 0.887) over SVM (AUC = 0.735). The RF-derived susceptibility map classified five risk levels, revealing high-risk zones concentrated within 200 m of roads, consistent with field observations. Results emphasize the necessity of integrating anthropogenic factors into landslide risk management for mountainous infrastructure. This study provides actionable insights for mitigation strategies and land-use planning, offering a scalable framework adaptable to similar regions. |
| format | Article |
| id | doaj-art-c6b894ea52b545b59c16cd2faeafc55f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-c6b894ea52b545b59c16cd2faeafc55f2025-08-20T03:04:38ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-08774-wEvaluation of landslide susceptibility of mountain highway based on RF and SVM modelsQingfeng He0Shoulong Wu1Xia Zhao2Zhankun Hui3Zhengang Wang4Paraskevas Tsangaratos5Ioanna Ilia6Wei Chen7Yixin Chen8Yiheng Hao9College of Geology and Environment, Xi’an University of Science and TechnologyCollege of Geology and Environment, Xi’an University of Science and TechnologyCollege of Geology and Environment, Xi’an University of Science and TechnologyCollege of Geology and Environment, Xi’an University of Science and TechnologyChina Coal Science and Technology Ecological Environment Technology Co., LTDLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of AthensLaboratory of Engineering Geology and Hydrogeology, Department of Geological Sciences, School of Mining and Metallurgical Engineering, National Technical University of AthensCollege of Geology and Environment, Xi’an University of Science and TechnologyCollege of Geology and Environment, Xi’an University of Science and TechnologyCollege of Geology and Environment, Xi’an University of Science and TechnologyAbstract Geological complexities along mountain highways frequently trigger landslides, posing significant threats to transportation safety and infrastructure. This study evaluates landslide susceptibility along the Lizha-Jiezi section of China’s G345 national highway using Random Forest (RF) and Support Vector Machine (SVM) models. Eleven conditioning factors including altitude, slope, aspect, plan curvature, profile curvature, lithology, distance to fault, rainfall, distance to river, normalized difference vegetation index (NDVI), and distance to road were analyzed using remote sensing and field surveys. A landslide inventory of 67 events was divided into training (70%) and validation (30%) datasets, with non-landslide samples selected at least 100 m away from landslide locations to minimize spatial overlap. Factor contribution analysis identified distance to road as the most significant predictor, highlighting anthropogenic impacts on slope destabilization. Model validation via receiver operating characteristic (ROC) curves demonstrated RF’s superior performance (AUC = 0.887) over SVM (AUC = 0.735). The RF-derived susceptibility map classified five risk levels, revealing high-risk zones concentrated within 200 m of roads, consistent with field observations. Results emphasize the necessity of integrating anthropogenic factors into landslide risk management for mountainous infrastructure. This study provides actionable insights for mitigation strategies and land-use planning, offering a scalable framework adaptable to similar regions.https://doi.org/10.1038/s41598-025-08774-wRandom forestSupport vector machineLandslide susceptibility mappingMountain highway |
| spellingShingle | Qingfeng He Shoulong Wu Xia Zhao Zhankun Hui Zhengang Wang Paraskevas Tsangaratos Ioanna Ilia Wei Chen Yixin Chen Yiheng Hao Evaluation of landslide susceptibility of mountain highway based on RF and SVM models Scientific Reports Random forest Support vector machine Landslide susceptibility mapping Mountain highway |
| title | Evaluation of landslide susceptibility of mountain highway based on RF and SVM models |
| title_full | Evaluation of landslide susceptibility of mountain highway based on RF and SVM models |
| title_fullStr | Evaluation of landslide susceptibility of mountain highway based on RF and SVM models |
| title_full_unstemmed | Evaluation of landslide susceptibility of mountain highway based on RF and SVM models |
| title_short | Evaluation of landslide susceptibility of mountain highway based on RF and SVM models |
| title_sort | evaluation of landslide susceptibility of mountain highway based on rf and svm models |
| topic | Random forest Support vector machine Landslide susceptibility mapping Mountain highway |
| url | https://doi.org/10.1038/s41598-025-08774-w |
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