基于I-PSO-BP模型的黄河南岸康店镇黄土地质灾害易发性评价

景斐媛; 陈婕; 杨泽强; 杨文涛; 包峻帆; 袁野; 种凯琳; 陈恪; 杨明权; 刘哲; 张媛媛

doi:10.13928/j.cnki.wrahe.2025.S2.100

水利水电技术（中英文） ›› 2025, Vol. 56 ›› Issue (S2) : 643 -655. DOI: 10.13928/j.cnki.wrahe.2025.S2.100

工程地质

基于I-PSO-BP模型的黄河南岸康店镇黄土地质灾害易发性评价

景斐媛 ¹^,² ,
陈婕 ³ ,
杨泽强 ¹^,² ,
杨文涛 ⁴ ,
包峻帆 ¹^,² ,
袁野 ⁵ ,
种凯琳 ¹^,² ,
陈恪 ¹^,² ,
杨明权 ¹^,² ,
刘哲 ¹^,² ,
张媛媛 ¹^,²

作者信息 +

Assessment of the susceptibility of loess geological hazards in Kangdian Town on the south bank of the Yellow River based on the I-PSO-BP model

Feiyuan JING ¹^,² ,
Jie CHEN ³ ,
Zeqiang YANG ¹^,² ,
Wentao YANG ⁴ ,
Junfan BAO ¹^,² ,
Ye YUAN ⁵ ,
Kailin ZHONG ¹^,² ,
Ke CHEN ¹^,² ,
Mingquan YANG ¹^,² ,
Zhe LIU ¹^,² ,
Yuanyuan ZHANG ¹^,²

Author information +

文章历史 +

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摘要

黄河流域黄土分布区地质灾害高发频发，仅郑州“7·20”特大暴雨期间，豫西黄河流域黄土分布区发生了数百起地质灾害，造成了重大的人员和财产损失，开展易发性评价研究是防控黄土地质灾害的关键。共选取坡度、地貌、距构造距离、距水系距离、土地利用类型、距建筑距离等13个评价因子，采用基于信息量(information, I)、BP神经网络(back propagation neural network, BP)、粒子群算法(particle swarm optimization, PSO)构建I模型、I-BP模型、I-PSO-BP模型，对黄河流域南岸郑州“7·20”特大暴雨期间地质灾害最严重的巩义市康店镇开展地质灾害易发性评价研究。采用AUC值、频率比和野外验证对不同模型的准确度、预测能力及合理性进行对比分析。结果表明：三者耦合模型(I-PSO-BP)的精度高于其他模型(I和I-BP),三种模型的AUC值分别为0.968 3、0.976 4、0.978 6,准确率分别为59.14%、92.00%、92.60%,单一模型精度最低，I-PSO-BP具有更高的精度和更强的预测能力，与野外验证实际最为相符。I-PSO-BP模型的高、中、低、极低易发区域面积占比分别为5.09%、2.25%、7.52%、85.15%,中、高易发区集中在人类工程活动频繁的冲沟沿线，人工切坡等人类工程活动是康店镇地质灾害发生的重要因素。

Abstract

Geological disasters occur frequently in the loess distribution area of the Yellow River basin. Only during the "7·20" extremely heavy rainstorm in Zhengzhou, hundreds of geological disasters occurred in the loess distribution area of the Yellow River basin in western Henan, resulting in significant losses of personnel and property. Carrying out vulnerability assessment research is the key to prevent and control geological disasters of loess. 13 evaluation factors are selectd including slope, landform, distance from structure, distance from water system, land use type, and distance from buildings. Based on information, I, BP neural network, and particle swarm optimization(PSO), I model, I-BP model, and I-PSO-BP model are constructed to evaluate the vulnerability of geological disasters in Kangdian Town, Gongyi City, which suffered the most serious geological disasters during the "7·20" extremely heavy rainstorm in Zhengzhou on the south bank of the Yellow River basin. Compare and analyze the accuracy, predictive ability, and rationality of different models using AUC values, frequency ratios, and field validation. The result show that the accuracy of the three coupled models(I-PSO-BP) is higher than that of other models(I and I-BP). The AUC values of the three models are 0.968 3, 0.976 4, and 0.978 6, respectively, and the accuracy rates are 59.14%, 92.00%, and 92.60%, respectively. The single model has the lowest accuracy. I-PSO-BP has higher accuracy and stronger prediction ability, which is most consistent with the field verification practice. The proportion of high, medium, low, and extremely low prone areas in the I-PSO-BP model is 5.09%, 2.25%, 7.52%, and 85.15%, respectively. The medium and high prone areas are concentrated along the gullies where human engineering activities are frequent, and human engineering activities such as slope cutting are important factors in the occurrence of geological disasters in Kangdian Town.

关键词

黄河流域黄土地质灾害 / 易发性 / 信息量 / BP神经网络模型 / 粒子群算法 / 康店镇

Key words

geological hazards of loess in the Yellow River Basin / susceptibility / amount of information / BP neural network model / particle swarm optimization algorithm / Kangdian Town

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景斐媛,陈婕,杨泽强,杨文涛,包峻帆,袁野,种凯琳,陈恪,杨明权,刘哲,张媛媛. 基于I-PSO-BP模型的黄河南岸康店镇黄土地质灾害易发性评价[J]. 水利水电技术（中英文）, 2025, 56(S2): 643-655 DOI:10.13928/j.cnki.wrahe.2025.S2.100