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

He Na; Zhu Xisong; Wu Fu; Liu Chang; Wu Qiuju; Huang Ximing; Jiang Li; Xiao Jigui; Wen Haitao; He Tianjie; Chang Ming

doi:10.13961/j.cnki.stbctb.2025.01.014

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

- Landslide risk assessment of Beiliu City, Guangxi Zhuang Autonomous Region based on a fully connected neural network method
- Bulletin of Soiland Water Conservation Vol. 45, Issue 1, Pages: 127-136(2025)
- 作者机构：
  
  1. 广西壮族自治区地质环境监测站,广西,南宁,530200
  2. 成都理工大学地质灾害防治与地质环境保护国家重点实验室,四川,成都,610059
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2025.01.014
  CLC： P694
- Published：2025
- 稿件说明：
移动端阅览
He Na, Zhu Xisong, Wu Fu, et al. Landslide risk assessment of Beiliu City, Guangxi Zhuang Autonomous Region based on a fully connected neural network method[J]. Bulletin of Soiland Water Conservation, 2025, 45(1): 127-136.
DOI：

He Na, Zhu Xisong, Wu Fu, et al. Landslide risk assessment of Beiliu City, Guangxi Zhuang Autonomous Region based on a fully connected neural network method[J]. Bulletin of Soiland Water Conservation, 2025, 45(1): 127-136. DOI： 10.13961/j.cnki.stbctb.2025.01.014.

摘要

[目的

]

建立适用于广西岩溶地区的崩塌滑坡风险评价体系，为该地区崩塌滑坡的早期预警与防灾减灾提供科学依据。[方法

]

以北流市为研究区，构建崩塌滑坡数据库，采用斜坡单元为评价基础，系统收集并分析多源数据，选取包括地下水类型、径流强度指数在内的关键评价指标，利用全连接神经网络模型开展崩塌滑坡易发性评价。鉴于岩溶地区易受降雨和岩溶侵蚀的影响，研究引入土壤侵蚀模数进行危险性评价，最后结合承灾体易损性，构建北流市崩塌滑坡风险评价模型。[结果

]

风险评价结果表明，高和极高风险区的面积为252.22 km

，占北流市总面积的10.27%。这些高风险区主要分布在隆盛镇、新丰镇、平政镇和六靖镇一带，属于侵蚀剥蚀丘陵和构造侵蚀低山地区，岩土体松散，土壤侵蚀模数较大、人口密度高、建筑物集中，极易受到崩塌滑坡威胁，风险等级高。[结论

]

经过ROC曲线和野外调查验证，北流市崩塌滑坡易发性评价精度达0.966 4，风险评价准确率为89.3%。验证结果表明，所构建的模型具有较高的精度和实际适用性，评价结果与实际情况相符。

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.

关键词

Keywords

references

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