收藏切换
Landslides Hazard Assessment Based on SVM-GBDT and Gradient Boosting Decision Tree Model Supported by Information Quantity
收藏切换
PDF
Zhao XING1, Xiao-jun MENG2, Jing-jing YUAN3, Di ZHANG3, Li LIU3, Yan-mei CHEN1, *
Science Technology and Engineering | 2025, 25(7) : 2712 - 2720
Less
收藏切换
Science Technology and Engineering | 2025, 25(7): 2712-2720
Papers·Astronomy and Geosciences
Landslides Hazard Assessment Based on SVM-GBDT and Gradient Boosting Decision Tree Model Supported by Information Quantity
Full
Zhao XING1, Xiao-jun MENG2, Jing-jing YUAN3, Di ZHANG3, Li LIU3, Yan-mei CHEN1, *
Affiliations
  • 1 Resource and Environmental Engineering College, Yangtze University, Wuhan 430000, China
  • 2 School of Environmental Studies, China University of Geosciences(Wuhan)/Wuhan Zhongdi Huanke Water Engineering Technology Consulting Co., Ltd., Wuhan 430000, China
  • 3 The Seventh Geological Brigade of the Hubei Geological Bureau, Yichang 443000, China
Published: 2025-03-08 doi: 10.12404/j.issn.1671-1815.2403088
Outline
收藏切换

Machine learning methods have been employed in the study area of Changyang Tujia Autonomous County for landslide hazard assessment, it could provide a scientific basis for geological disaster prevention and control efforts. Through the correlation analysis of 12 evaluation indicators (planar curvature, terrain undulation, surface roughness, slope, vegetation coverage, engineering lithology, distance to fault zone, distance to water system, rainfall, land use type, distance to buildings, and distance to roads) in the study area selected by historical landslide points, they were selected. And the evaluation model of the study area was constructed by calculating the information content of factors and integrate support vector machine (SVM) and gradient boosting decision tree (GBDT) models. The hazard of the study area was classified into four levels: extreme high, high, medium, and low, to generate hazard zoning. Subsequently, an assessment of the evaluation model was conducted. The results indicated that the very high hazard zone was mainly distributed in the southwest, central, and eastern parts of the research area. The distribution percentages of very high, high, medium, and low hazard zones predicted by the I-SVM and I-GBDT models were 15.86%, 21.29%, 33.51%, 28.68%, and 30.08%, 7.41%, 13.28%, 49.22%, respectively. The prediction of hazard zones by the I-SVM model aligned more closely with reality. The AUC values for the I-SVM and I-GBDT models were 0.859 and 0.829, respectively. The prediction of risk zones by the I-SVM model is deemed more reasonable and reliable.

landslides  /  information quantity  /  hazard assessment  /  support vector machine  /  gradient boosting decision tree
Zhao XING, Xiao-jun MENG, Jing-jing YUAN, Di ZHANG, Li LIU, Yan-mei CHEN. Landslides Hazard Assessment Based on SVM-GBDT and Gradient Boosting Decision Tree Model Supported by Information Quantity[J]. Science Technology and Engineering, 2025 , 25 (7) : 2712 -2720 . DOI: 10.12404/j.issn.1671-1815.2403088
Year 2025 volume 25 Issue 7
PDF
118
45
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2403088
  • Receive Date:2024-04-26
  • Online Date:2026-03-30
  • Published:2025-03-08
Article Data
Affiliations
History
  • Received:2024-04-26
  • Revised:2024-07-30
Funding
Affiliations
    1 Resource and Environmental Engineering College, Yangtze University, Wuhan 430000, China
    2 School of Environmental Studies, China University of Geosciences(Wuhan)/Wuhan Zhongdi Huanke Water Engineering Technology Consulting Co., Ltd., Wuhan 430000, China
    3 The Seventh Geological Brigade of the Hubei Geological Bureau, Yichang 443000, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2403088
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT