基于极端梯度提升算法的滑坡易发性评价模型研究
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大连大学建筑工程学院

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中图分类号:

X915.5

基金项目:

国家自然科学基金(51374046);浙江丽水地区灾害地质灾害调查项目(DD20190648)


Study on landslide susceptibility Evaluation model based on XGBoost
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Affiliation:

1.College of Civil Engineering &2.Architecture, Dalian University

Fund Project:

National Natural Science Foundation of China, No.51374046; Geological Disaster Investigation Project of Lishui District, Zhejiang Province, No.DD20190648

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    摘要:

    机器学习用于地质灾害的易发性评价分析是当前研究的热点之一,不同的学习模型其效果不尽相同。为合理有效地评价滑坡地质灾害的易发性,依托浙江省温州市飞云江流域地质灾害的调查数据,应用地理信息系统(Geographic Information System,GIS)技术提取坡度、坡向、坡形、地表覆盖、地形湿度指数(Topographic Wetness Index,TWI)、极端小时降雨量、内摩擦角、黏聚力、容重与风化层厚度10个滑坡致灾因子,基于极端梯度提升算法(eXtreme Gradient Boosting,XGBoost)构建模型用于滑坡地质灾害的易发性多分类评价。模型结果通过多分类混淆矩阵进行评价,并与支持向量机(Support Vector Machine,SVM)模型进行精度比对分析。研究结果显示,训练后的XGBoost 算法模型对测试集中极高易发区识别的召回率和精确率分别达到了97.92%和98.06%,F1值达到97.99%,均优于SVM,可为研究地区的滑塌地质灾害易发性评价提供模型支持。

    Abstract:

    The applications on the assessment and analysis of the susceptibility of geohazards using machine learning is one of the hotspots in current researches. To evaluate the geohazards susceptibility reasonably and effectively, the model of eXtreme Gradient Boosting (XGBoost) is used by coupling with geographic information system (GIS) technology to carry out multi-classification evaluation of landslide geohazards susceptibility, which is based on the geological disaster survey data of Feiyun River basin in Wenzhou, Zhejiang Province. The input data include slope, slope aspect, slope shape, landcover type, Topographic Wetness Index (TWI), extreme hourly rainfall, internal friction angle, cohesion, bulk density and regolith thickness 10 landslide hazard factors. The model results were evaluated by multi-classification confusion matrix and compared with Support Vector Machine (SVM) model. The results show that the recall rate and accuracy rate of the trained XGBoost algorithm model for the identification of extremely high risk areas in the test set have reached 97.92% and 98.06%, respectively, and the F1 value has reached 97.99%, which are both better than SVM model. Therefore, the proposed and trained model can provide model support for the susceptibility evaluation of landslide geological disasters in the studied area.

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赵晓东,徐振涛,刘福,等. 基于极端梯度提升算法的滑坡易发性评价模型研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2022-01-21
  • 最后修改日期:2022-04-01
  • 录用日期:2022-04-24
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