ISSN 1003-8035 CN 11-2852/P
    臧烨祺,郭永刚,苏立彬,等. 基于多种模型的藏东南地区滑坡易发性评价[J]. 中国地质灾害与防治学报,2024,35(0): 1-12. DOI: 10.16031/j.cnki.issn.1003-8035.202309021
    引用本文: 臧烨祺,郭永刚,苏立彬,等. 基于多种模型的藏东南地区滑坡易发性评价[J]. 中国地质灾害与防治学报,2024,35(0): 1-12. DOI: 10.16031/j.cnki.issn.1003-8035.202309021
    ZANG Yeqi,GUO Yonggang,SU Libin,et al. Assessment of landslide susceptibility in southeast Tibet region based on multiple models[J]. The Chinese Journal of Geological Hazard and Control,2024,35(0): 1-12. DOI: 10.16031/j.cnki.issn.1003-8035.202309021
    Citation: ZANG Yeqi,GUO Yonggang,SU Libin,et al. Assessment of landslide susceptibility in southeast Tibet region based on multiple models[J]. The Chinese Journal of Geological Hazard and Control,2024,35(0): 1-12. DOI: 10.16031/j.cnki.issn.1003-8035.202309021

    基于多种模型的藏东南地区滑坡易发性评价

    Assessment of landslide susceptibility in southeast Tibet region based on multiple models

    • 摘要: 藏东南地区地质环境复杂、灾害频发,滑坡给区内工程建设和人财安全造成较大威胁。为选出藏东南地区区域滑坡预测精较高的模型,本文通过实地调研修正滑坡点数据,结合地形地貌因子、地质因子、地表覆盖因子和诱发因子并通过主成分分析法进行因子筛选,采用频率比模型、BP神经网络模型以及两种模型耦合来进行藏东南地区区域滑坡预测,最后进行ROC曲线来检验模型精度。结果表明:经过因子筛选后的频率比模型对藏东南地区预测精度最高(AUC=0.889),通过主成分分析剔除因子的模型精度高于未剔除因子模型精度且藏东南地区滑坡主要沿水系分布,多分布在雅鲁藏布江、达曲、藏曲、怒江、澜沧江、伟曲、詹曲和扎曲两侧分布。利用所得模型对研究区灾害进行预测,得出滑坡点均处于高易发区及易发区内,研究得出的模型可为藏东南地区工程建设提供技术参考。

       

      Abstract: The geological environment in southeast Tibet is complicated and disasters occur frequently. Landslides pose a great threat to engineering construction and human and financial safety in the region. In order to select models with higher precision for regional landslide prediction in southeast Tibet, this paper used modified landslide point data through field investigation, combined with topographic and geomorphic factors, geological factors, land cover factors and induced factors, and screened the factors through principal component analysis. Frequency ratio models, BP neural network models, and a combination of the two models were used for regional landslides prediction in southeast Tibet. Finally, ROC curves were used to evaluate the model accuracy. The results showed that the frequency ratio model after factor selection had the highest prediction accuracy for southeast Tibet (AUC=0.889). Models with factors removed through principal component analysis had higher accuracy than those without removal, and landslides in southeast Tibet were mainly distributed along river systems, including the Yarlung Zangbo River, Daqu River, Zangqu River, Nujiang River, Lancang River, Weiqu River, Janqu River and Zhaqu River. The models were used to predict the disaster in the study area, revealing that landslide points were located in high susceptibility and susceptibility zones. The models developed in this study can provide technical reference for engineering construction in southeast Tibet.

       

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