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1.三峡库区地质灾害教育部重点实验室,湖北 宜昌443002
2.三峡大学土木与建筑学院,湖北 宜昌443002
3.三峡大学水利与环境学院,湖北 宜昌443002
4.湖北省地质局第七地质大队,湖北 宜昌443100
5.长江水利委员会汉江流域治理保护中心,武汉430000
6.湖北工程学院新技术学院,湖北 孝感432000
Received:27 February 2026,
Revised:2026-03-29,
Accepted:05 April 2026,
Published:28 June 2026
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郭飞,杨亚会,张军伟,等.指标筛选方法与评价模型对崩岗易发性评价的影响[J].水土保持学报,2026,40(3):99-107., 40(3):99-107.
GUO Fei , YANG Yahui , ZHANG Junwei, et al. Influence of indicator screening methods and evaluation models on Benggang susceptibility evaluation[J]. Journal of Soil and Water Conservation, 2026
郭飞,杨亚会,张军伟,等.指标筛选方法与评价模型对崩岗易发性评价的影响[J].水土保持学报,2026,40(3):99-107., 40(3):99-107. DOI: 10.13870/j.cnki.stbcxb.2026.03.023. CSTR: 32310.14.stbcxb.2026.03.023.
GUO Fei , YANG Yahui , ZHANG Junwei, et al. Influence of indicator screening methods and evaluation models on Benggang susceptibility evaluation[J]. Journal of Soil and Water Conservation, 2026 DOI: 10.13870/j.cnki.stbcxb.2026.03.023. CSTR: 32310.14.stbcxb.2026.03.023.
目的
2
探究评价指标的不同筛选方法和机器学习模型对崩岗易发性评价结果的影响。
方法
2
以石城县崩岗为例,通过不筛选、地理探测器(GD)筛选和多重共线性(VIF)筛选等方法构建评价指标体系,采用随机森林(RF)和多层感知机(MLP)模型,开展不同评价指标和评价模型对崩岗易发性的适宜性研究。
结果
2
1)GD筛选的崩岗易发性评价结果优于VIF筛选评价结果,采用指标累计贡献率
q
值为90%的GD筛选的结果与不筛选的结果基本相同;2)RF模型对崩岗易发性的预测效果优于MLP模型,其中不筛选、GD筛选、VIF筛选3种方法下RF模型的AUC值分别为0.847、0.823、0.779;3)研究区高易发区、极高易发区主要分布于石城县西北部,与崩岗分布较为吻合。
结论
2
采用指标累计贡献率
q
值为90%的RF模型开展易发性评价具有较高的合理性与适用性。研究成果可为赣南地区的崩岗易发性评价提供科学参考。
Objective
2
This study aims to explore the influence of different screening methods of evaluation indicators and machine learning models on the evaluation results of Benggang susceptibility.
Methods
2
Taking Benggang in Shicheng County as a case study, three approaches-non-screening, geodetector (GD) screening, and variance inflation factor (VIF) screening-were employed to construct the evaluation indicator system. The random forest (RF) and multilayer perceptron (MLP) models were applied to conduct a suitability study comparing different indicator sets and models for Benggang susceptibility evaluation.
Results
2
1) The susceptibility evaluation results based on GD-screened indicators were superior to those obtained using VIF-screened indicators. The results of GD screening with a 90% cumulative
q
-value were basically the same as those of non-screening. 2) The RF model was better than MLP model in evaluating the vulnerability to Benggang. Under the three i
ndicator systems (non-screening, GD screening, and VIF screening), the AUC values for the RF model were 0.847, 0.823, and 0.779, respectively. 3) The high-susceptibility zones and very-high-susceptibility zones in the study area were mainly distributed in the northwest of Shicheng County, which was consistent with the distribution of Benggang.
Conclusion
2
Employing the RF model with indicators screened by GD at a cumulative
q
-value contribution rate of 90% provides a highly rational and applicable approach for susceptibility evaluation. The research findings can provide a scientific reference for the evaluation of Benggang susceptibility in the Gannan region.
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