ISSN 1003-8035 CN 11-2852/P
    刘福臻, 王灵, 肖东升. 机器学习模型在滑坡易发性评价中的应用[J]. 中国地质灾害与防治学报, 2021, 32(6): 98-106. DOI: 10.16031/j.cnki.issn.1003-8035.2021.06-12
    引用本文: 刘福臻, 王灵, 肖东升. 机器学习模型在滑坡易发性评价中的应用[J]. 中国地质灾害与防治学报, 2021, 32(6): 98-106. DOI: 10.16031/j.cnki.issn.1003-8035.2021.06-12
    Fuzhen LIU, Ling WANG, Dongsheng XIAO. Application of machine learning model in landslide susceptibility evaluation[J]. The Chinese Journal of Geological Hazard and Control, 2021, 32(6): 98-106. DOI: 10.16031/j.cnki.issn.1003-8035.2021.06-12
    Citation: Fuzhen LIU, Ling WANG, Dongsheng XIAO. Application of machine learning model in landslide susceptibility evaluation[J]. The Chinese Journal of Geological Hazard and Control, 2021, 32(6): 98-106. DOI: 10.16031/j.cnki.issn.1003-8035.2021.06-12

    机器学习模型在滑坡易发性评价中的应用

    Application of machine learning model in landslide susceptibility evaluation

    • 摘要: 机器学习在滑坡的易发性评价中面临两个难点,一是评价指标的客观量化,二是训练样本的选择。鉴于此,采用频率比法实现了评价指标的客观量化,利用k均值聚类算法实现了非滑坡样本数据的筛选。结果表明,以k均值聚类算法筛选非滑坡为前提,神经网络的训练精度由73%提升到了97%,支持向量机的训练精度由75%提升到了96%。基于GIS平台,将神经网络和支持向量机模型计算的全区易发性指数按自然断点法分为五个区域,分区图与历史灾害点的叠加分析统计结果显示,神经网络在全局范围内的评价结果优于支持向量机模型,全局精度分别为76%和74%。研究结果可为南江县的防灾减灾工作提供参考。

       

      Abstract: Machine learning faces two difficulties in the evaluation of landslide susceptibility. One is the objective quantification of evaluation index, and the other is the selection of training sample-0.5pts. For that reason, the frequency ratio method is used to achieve the objective quantification of evaluation index, and the k-means clustering algorithm is used to achieve the selection of non-landslide sample data. The results show that based on the premise that the k-means clustering algorithm selects non-landslides, the training accuracy of the neural network has increased from 73% to 97%, and the training accuracy of the support vector machine has increased from 75% to 96%. Based on the GIS platform, the susceptibility index calculated by the neural network and support vector machine model is divided into five regions according to the natural break point method. The statistical results of the overlay analysis of the zoning map and the historical disaster points show that the evaluation result of the neural network is better than the support vector machine model in the global scope, and the global accuracy is 76% and 74%, respectively. The research results can provide reference for disaster prevention and mitigation in Nanjiang County of China.

       

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