基于样本优化与深度特征提取的滑坡易发性评价

徐金鸿; 李清泉; 韦春桃; 赵芹

doi:10.13961/j.cnki.stbctb.2025.02.020

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基于样本优化与深度特征提取的滑坡易发性评价

试验研究

- 基于样本优化与深度特征提取的滑坡易发性评价
- Landslide susceptibility evaluation based on sample optimization and deep feature extraction
- 水土保持通报 2025年45卷第2期页码：190-200
- 作者机构：
  
  重庆交通大学智慧城市学院,重庆,402247
- 作者简介：
- 基金信息：
  
  重庆市自然科学基金面上项目“遥感知识图谱引导的耕地智能提取与监测”(CSTB2023NSCQ-MSX0781)
- DOI：10.13961/j.cnki.stbctb.2025.02.020
  中图分类号： P642.22
- 网络首发：2025-05-16，
  
  纸质出版：2025
- 稿件说明：
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徐金鸿, 李清泉, 韦春桃, 等. 基于样本优化与深度特征提取的滑坡易发性评价[J]. 水土保持通报, 2025,45(2):190-200.

Xu Jinhong, Li Qingquan, Wei Chuntao, et al. Landslide susceptibility evaluation based on sample optimization and deep feature extraction[J]. Bulletin of Soiland Water Conservation, 2025, 45(2): 190-200.
徐金鸿, 李清泉, 韦春桃, 等. 基于样本优化与深度特征提取的滑坡易发性评价[J]. 水土保持通报, 2025,45(2):190-200. DOI： 10.13961/j.cnki.stbctb.2025.02.020.

Xu Jinhong, Li Qingquan, Wei Chuntao, et al. Landslide susceptibility evaluation based on sample optimization and deep feature extraction[J]. Bulletin of Soiland Water Conservation, 2025, 45(2): 190-200. DOI： 10.13961/j.cnki.stbctb.2025.02.020.

摘要

[目的

]

探究滑坡易发性评价中准确的非滑坡样本采样方法和特征提取优异的评价模型，为区域滑坡防控工作提供理论支持和科学指导。[方法

]

在缓冲区采样策略的基础上提出了一种基于卷积自编码器(convolutional auto-encoder，CAE)的非滑坡样本优化方法。该方法通过学习滑坡样本的特征，利用重构误差筛选和优化非滑坡样本。在评价模型方面，引入卷积注意力模块(convolutional block attention module， CBAM)到残差网络(ResNet)中，构建ResNet-CBAM滑坡易发性评价模型，以捕捉更深层次、更复杂且更具代表性的特征。试验以三峡库区重庆市万州区为研究区域，选取高程等12个影响因子，采用SVM，DNN，CNN和ResNet-CBAM 4种模型，对缓冲区采样和基于CAE优化采样的评价精度和结果进行对比分析。[结果

]

在相同评价模型下，基于CAE优化的非滑坡样本采样策略具有更高的可靠性与准确性；在相同采样策略下，ResNet-CBAM模型在准确率、精确率、召回率、F

分数和AUC等指标上均优于其他模型；各模型的评价结果具有相似性，高易发区和极高易发区主要分布在长江沿岸等植被覆盖度低、人类活动频繁的区域，使用了基于CAE优化采样的ResNet-CBAM模型表现出更优的预测效果，更适宜于该区域的滑坡易发性评价研究。[结论

]

万州区滑坡易发性指数较高，区域内存在大量潜在滑坡风险区。基于CAE优化的非滑坡样本采样策略和ResNet-CBAM评价模型能有效提高滑坡易发性评价的精度。

Abstract

[Objective] The efficacy of the non-landslide sampling method and a model with excellent feature extraction in evaluating landslide susceptibility were explored

so as to provide theoretical support and scientific guidance for regional landslide prevention and control work. [Methods] A non-landslide sample optimization method based on Convolutional Auto-Encoder (CAE) was proposed

which was built based on the buffer sampling strategy. This method optimizes non-landslide samples by learning the features of landslide samples and using reconstruction errors. For the evaluation model

the Convolutional Block Attention Module (CBAM) was integrated into the Residual Network (ResNet) to construct the ResNet-CBAM landslide susceptibility model

which captures deeper

and more complex and representative features. Taking the Wanzhou District

Chongqing City in the Three Gorges reservoir area as the study area

12 influencing factors (e.g.

such as elevation) were selected. Four models

namely

SVM

DNN

CNN

and ResNet-CBAM

were used to compare and analyze the evaluation accuracy and results of buffer zone sampling versus CAE-based optimized sampling. [Results] Under the same evaluation model

the CAE-based optimization sampling strategy for non-landslide samples yielded higher reliability and accuracy. Under the same sampling strategy

the ResNet-CBAM model outperformed the other models in terms of accuracy

precision

recall

F1 score

and area under the curve (AUC) values. The evaluation results were similar across models

with higher and very higher susceptibility areas predominantly located in regions with lower vegetation cover and frequent human activity

such as along the Yangtze River. Moreover

the ResNet-CBAM model with CAE-based optimized sampling demonstrated superior prediction results and was more suitable for landslide susceptibility evaluation in this area. [Conclusion] Wanzhou District exhibits a high landslide susceptibility index

with numerous potential landslide risk zones identified within the area. The non-landslide sampling strategy and ResNet-CBAM evaluation model based on CAE optimization can effectively improve the accuracy of landslide susceptibility evaluations.

关键词

Keywords

references

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