Landslide risk assessment of Beiliu City, Guangxi Zhuang Autonomous Region based on a fully connected neural network method
Author:
Clc Number:

P694

  • Article
  • | |
  • Metrics
  • |
  • Reference
  • |
  • Related [4]
  • | | |
  • Comments
    Abstract:

    [Objective] A comprehensive risk assessment system for collapses and landslides in the karst regions of the Guangxi Zhuang Autonomous Region was established, in order to offerscientific support for early warning, disaster prevention, and mitigation in the area. [Methods] Beiliu City was selected as the study area, and a database of collapses and landslides was constructed. Slope units were used as the basis for the evaluation, with multisource data systematically collected and analyzed. Key evaluation indicators, including groundwater type and runoff intensity index, were identified, and a fully connected neural network model was employed to assess the susceptibility to collapses and landslides. Given the region’s vulnerability to rainfall and karst erosion, the soil erosion modulus was incorporated into the hazard assessment. Finally, a risk evaluation model for collapses and landslides in Beiliu City was developed by integrating vulnerability assessments of the exposed elements. [Results] The findings revealed that high- and very high-risk zones covered 252.22 km2, accounting for 10.27% of Beiliu City’s total area. These zones are primarily located in Longsheng Town, Xinfeng Town, Pingzheng Town, and Liujing Town, which are characterized by eroded and denuded hills and tectonic erosion of low mountains. Factors such as loose geotechnical body, high soil erosion modulus, dense population, and concentrated buildings significantly heightened the collapse and landslide risks, resulting in a high-risk classification. [Conclusion] Validation through ROC curves and field investigations showed an accuracy of 0.966 4 for susceptibility evaluation and 89.3% for risk assessment in Beiliu City. These results demonstrate the high precision and practical applicability of the constructed model, which aligns closely with real-world scenarios.

    Reference
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

何娜,朱习松,吴福,刘昶,吴秋菊,黄希明,蒋力,肖吉贵,文海涛,何添杰,常鸣.基于全连接神经网络的广西北流市崩塌滑坡风险评价[J].水土保持通报英文版,2025,45(1):127-136

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:June 20,2024
  • Revised:October 09,2024
  • Online: February 22,2025