文章摘要
张庭瑜, 孙增慧, 程 杰, 刘金宝, 石 磊, 孔 辉, 杨亮彦, 罗 丹.IOE模型在延安市宝塔区碾庄沟流域滑坡易发性分区中的应用Journal of Water Resources and Water Engineering[J].,2021,32(1):205-212
IOE模型在延安市宝塔区碾庄沟流域滑坡易发性分区中的应用
Application of IOE model for landslide susceptibility mapping in Nianzhuanggou Watershed, Yan’an City
  
DOI:10.11705/j.issn.1672-643X.2021.01.30
中文关键词: 滑坡  易发性分区  熵指数模型(IOE)  黄土沟壑区  碾庄沟流域  延安市
英文关键词: landslides  susceptibility mapping  index of entropy model(IOE)  loess gully area  Nianzhuanggou Watershed  Yan’an City
基金项目:陕西省自然科学基础研究计划项目(2019JQ-945); 陕西省土地工程建设集团有限责任公司内部科研项目(DJNY2021-10); 长安大学中央高校基本科研业务费专项资金项目(300102351502)
Author NameAffiliation
ZHANG Tingyu1,2,3,4, SUN Zenghui1,2,3,4, CHENG Jie1,2,3,4, LIU Jinbao1,2,3,4, SHI Lei1,2,3,4, KONG Hui1,2,3,4, YANG Liangyan1,2,3,4, LUO Dan5 (1.陕西地建土地工程技术研究院有限责任公司陕西 西安 710075 2.陕西省土地工程建设集团有限责任公司陕西 西安 710075 3.自然资源部退化及未利用土地整治工程重点实验室陕西 西安 710075 4.陕西省土地整治工程技术研究中心陕西 西安 710075
5. 陕西地建土地勘测规划设计研究院有限责任公司
陕西 西安 710075) 
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中文摘要:
      将延安市宝塔区碾庄沟流域作为研究区,在野外调查以及遥感解译的基础上,得到了73个滑坡点数据,其中70%的滑坡点被当作训练样本,剩余的30%的滑坡点被当作测试样本。选取坡度、坡向、高程、归一化植被指数(NDVI)、岩土体类型、土地利用类型、平面曲率和剖面曲率作为滑坡易发性分区建模的解释变量。利用熵指数模型(IOE)计算研究区的滑坡易发性指数(LSI),得到了研究区滑坡易发性分区图(LSM)。最后利用准确率和接受者操作特征曲线下的面积(AUC)对分区结果进行评价。结果表明:训练样本集和测试样本集的准确率均大于0.8,且测试样本集的AUC值为0.964 1,说明研究区的滑坡易发性分区结果可信度高,且IOE模型具有较强的泛化能力。研究结果也可以为当地的滑坡防治工作提供参考。
英文摘要:
      The Nianzhuanggou watershed of Yan’an City was regarded as the study area. Based on the field survey and remote sensing image, the data of 73 landslide points were obtained. 70% of the points were used as training dataset, and the remaining were used for validation purpose. Slope, aspect, elevation, normalized difference vegetation index (NDVI), lithology, land use, plan curvature and profile curvature were selected as explanatory variables for landslide susceptibility modeling. The landslide susceptibility index (LSI) of the study area was calculated using the index of entropy model (IOE), and a landslide susceptibility map (LSM) was generated. Finally, the results were evaluated using the accuracy and area under the receiver operating characteristic curve (AUC). The results show that the accuracy of the training dataset and the validation dataset are both greater than 0.8, and the AUC value of validation dataset is 0.964 1, indicating that the LSM is highly reliable, and the IOE model has strong generalization ability. The results can also provide some reference for the local prevention and control of landslides.
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