刘强 讲师(高校)
  • 入职时间:2023-10-09
  • 学历:博士研究生毕业
  • 学位:工学博士学位
  • 性别:
  • 在职信息:在岗
  • 主要任职:助理教授
  • 毕业院校:哈尔滨工业大学
  • 所在单位:城市轨道交通学院(智慧城市与交通学院)
  • 学科:防灾减灾工程及防护工程
    地质工程
论文成果
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  • 发表刊物:Natural Hazards
  • 关键字:Machine learning · Factor importance · Map appearance · Heterogeneity investigation
  • 摘要:This study reported an application of the tree-based models to landslide susceptibility. The landslide inventory and ten conditioning factors were first constructed, based on data availability and climate. Subsequently, three tree-based models, decision tree (DT), DTBoosting, and random forest (RF), were established and compared with the support vector machine (SVM) to analyze the difference in model prediction. Finally, the effect and causes of tree-based algorithms on prediction results were explored based on the working mechanism of the susceptibility model. Results show that there is no multicollinearity among the conditioning factors. The predicted results produced by the tree-based model display the discontinuous distribution compared with the SVM, not only presented in the point-based prediction but the surface-based heterogeneity. Moreover, heterogeneity on the susceptibility map relates to the tree-based algorithm and factor grading, especially the classification of important factors. Besides, DT-Boosting appears the highest numerical features, with large values of AUC (0.981), specificity (0.960), sensitivity (0.956) and accuracy (0.958) in the training phase, and high prediction of AUC (0.862), specificity (0.759), sensitivity (0.843) and accuracy (0.801) in the validation phase. In terms of fluctuation, the RF is smaller than that of DT-Boosting. Further, the susceptibility map generated by RF, with the largest D-value of 7.81, can well capture the difference in landslide susceptibility. This study provides a deep understanding for the application of tree-based machine learning models to landslide susceptibility.
  • 第一作者:Qiang Liu et al
  • 论文类型:SCI
  • 卷号:113
  • 是否译文:
  • 发表时间:2022-04-23

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