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基于贝叶斯优化机器学习超参数的滑坡易发性评价

杨灿 刘磊磊 张遗立 朱文卿 张绍和

杨灿, 刘磊磊, 张遗立, 朱文卿, 张绍和. 基于贝叶斯优化机器学习超参数的滑坡易发性评价[J]. 地质科技通报, 2022, 41(2): 228-238. doi: 10.19509/j.cnki.dzkq.2022.0059
引用本文: 杨灿, 刘磊磊, 张遗立, 朱文卿, 张绍和. 基于贝叶斯优化机器学习超参数的滑坡易发性评价[J]. 地质科技通报, 2022, 41(2): 228-238. doi: 10.19509/j.cnki.dzkq.2022.0059
Yang Can, Liu Leilei, Zhang Yili, Zhu Wenqing, Zhang Shaohe. Machine learning based on landslide susceptibility assessment with Bayesian optimized the hyperparameters[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 228-238. doi: 10.19509/j.cnki.dzkq.2022.0059
Citation: Yang Can, Liu Leilei, Zhang Yili, Zhu Wenqing, Zhang Shaohe. Machine learning based on landslide susceptibility assessment with Bayesian optimized the hyperparameters[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 228-238. doi: 10.19509/j.cnki.dzkq.2022.0059

基于贝叶斯优化机器学习超参数的滑坡易发性评价

doi: 10.19509/j.cnki.dzkq.2022.0059
基金项目: 

国家自然科学基金青年基金项目 41902291

湖南省自然科学基金项目 2020JJ5704

湖南省研究生创新基金项目 CX20200236

中南大学中央高校基本科研业务费专项资金项目 2020zzts651

详细信息
    作者简介:

    杨灿(1998—),男,现正攻读地质资源与地质工程专业硕士学位,主要从事地质灾害防治与风险控制方面的研究工作。E-mail: nyangcan@csu.edu.cn

    通讯作者:

    刘磊磊(1987—),男,副教授,主要从事地质灾害防治与风险控制方面的研究工作。E-mail: csulll@foxmail.com

  • 中图分类号: P642.22

Machine learning based on landslide susceptibility assessment with Bayesian optimized the hyperparameters

  • 摘要: 利用机器学习模型进行滑坡易发性评价时, 不同的超参数设置往往会导致评价结果的不同。采用贝叶斯算法对4种常见机器学习模型(逻辑回归LR、支持向量机SVM、人工神经网络ANN和随机森林RF)的超参数进行了优化, 探索了该算法对滑坡易发性机器学习模型的优化效果。以湘中地区4县(安化县、新华县、桃江县和桃源县)滑坡易发性评价为例说明该算法的可行性与适用性。基于滑坡历史编录, 确定研究区内1 017个滑坡点, 并选定15个滑坡影响因子, 以此构建滑坡易发性模型的训练集和测试集。利用贝叶斯优化算法对4种机器学习模型的主要超参数进行了优化, 依据优化后的超参数建立了4种优化模型, 并使用AUC值等指标来比较其预测能力。结果表明: 经超参数优化后的4种机器学习模型预测性能均有所提高, 且基于贝叶斯优化的随机森林模型表现最好。

     

  • 图 1  研究区位置示意图

    Figure 1.  Location of the study area

    图 2  滑坡影响因子图(一)

    Figure 2.  Landslide conditioning factors (one)

    图 2  滑坡影响因子图(二)

    Figure 2.  Landslide conditioning factors (two)

    图 3  滑坡易发性评价流程图

    Figure 3.  Flow chart of the landslide susceptibility assessment

    图 4  贝叶斯算法优化超参数流程图

    Figure 4.  Flowchart for optimization of hyperparameters by using the Bayesian algorithm

    图 5  超参数优化后的最优模型的ROC曲线

    Figure 5.  ROC curves of models after hyperparameter optimization

    图 6  4种最优模型的滑坡易发性图

    Figure 6.  Landslide susceptibility maps from four optimal models

    表  1  研究区基于MIC的滑坡影响因子重要性排序

    Table  1.   Importance ranking of landslide conditioning factors based on MIC

    滑坡影响因子 MIC
    距道路距离 0.040
    距河流距离 0.031
    土地利用类型 0.028
    高程 0.025
    NDVI 0.018
    地形湿度指数 0.018
    地层岩性 0.017
    年汛期降雨量 0.017
    微地貌 0.015
    坡度 0.013
    坡位 0.010
    距断层距离 0.008
    剖面曲率 0.006
    坡向 0.006
    平面曲率 0.003
    下载: 导出CSV

    表  2  4种机器学习模型中主要优化的超参数

    Table  2.   Main optimized hyperparameters in the four machine learning models

    模型 超参数 解释
    LR solver 损失函数最小化算法包括:sag, Newton CG, lbfgs和liblinear
    penalty 正则化方法包括:L1和L2
    C 正则化系数
    SVM kernel 核函数类型包括:linear, poly, rbf, sigmoid和precomputed
    C 正则化系数
    gamma rbf, poly和sigmoid的核函数系数
    ANN hidden_layer_sizes 隐藏层层数以及每层的神经元个数
    solver 权重求解器包括:lbfgs, sgd和adam
    alpha L2的正则化系数
    RF max_depth 单个决策树的深度
    max_features 单个决策树划分时考虑的最大特征数
    min_samples_split 分裂一个内部节点(非叶子节点)需要的最小样本数
    n_estimators 森林中决策树的个数
    下载: 导出CSV

    表  3  优化前后4种机器学习模型与贝叶斯算法耦合的AUC值比较

    Table  3.   Comparison of AUC values of the four models before and after optimization by Bayesian

    模型类型 LR SVM ANN RF
    AUC 无超参数优化 0.661 0.701 0.705 0.745
    超参数优化 0.708 0.739 0.741 0.771
    提升幅度/% 7.1 5.4 5.1 3.5
    下载: 导出CSV

    表  4  超参数优化后的模型性能统计指标

    Table  4.   Statistical indicators of model performance after hyperparameter optimization

    统计指标 LR SVM ANN RF
    TP 188 188 190 197
    TN 192 200 204 221
    FP 114 115 105 93
    FN 113 104 108 96
    敏感度/% 62.46 64.38 63.76 67.24
    特异度/% 62.75 63.49 66.02 70.38
    准确度/% 62.60 63.92 64.91 68.86
    下载: 导出CSV

    表  5  历史滑坡在各易发性等级的分布情况

    Table  5.   Distribution of historical landslides in different susceptibility classes

    模型 易发性等级 面积/km2 面积占比/% 历史滑坡数 历史滑坡数占比/% 历史滑坡数/面积比值
    LR 1 212.830 8.05 85 8.36 0.070 1
    较低 2 683.407 17.82 68 6.69 0.025 3
    4 495.363 29.85 239 23.50 0.053 2
    较高 4 281.440 28.43 320 31.47 0.074 7
    2 387.226 15.85 305 29.99 0.127 8
    SVM 1 909.117 12.68 29 2.85 0.015 2
    较低 4 118.570 27.35 150 14.75 0.036 4
    4 770.588 31.68 309 30.38 0.064 8
    较高 2 611.681 17.34 278 27.34 0.106 4
    1 650.310 10.96 251 24.68 0.152 1
    ANN 1 777.697 11.80 93 9.14 0.052 3
    较低 3 226.806 21.43 104 10.23 0.032 2
    4 726.266 31.38 281 27.63 0.059 5
    较高 3 783.506 25.12 324 31.86 0.085 6
    1 551.991 10.31 215 21.14 0.138 5
    RF 3 114.347 20.68 71 6.98 0.022 8
    较低 3 761.539 24.98 158 15.54 0.042 0
    3 198.268 21.24 204 20.06 0.063 8
    较高 2 792.637 18.54 258 25.37 0.092 4
    2 193.475 14.56 326 32.06 0.148 6
    下载: 导出CSV
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