赵忠国, 张峰, 郑江华. 多元自适应回归样条法的滑坡敏感性评价[J]. 武汉大学学报 ( 信息科学版), 2021, 46(3): 442-450. DOI: 10.13203/j.whugis20190136
引用本文: 赵忠国, 张峰, 郑江华. 多元自适应回归样条法的滑坡敏感性评价[J]. 武汉大学学报 ( 信息科学版), 2021, 46(3): 442-450. DOI: 10.13203/j.whugis20190136
ZHAO Zhongguo, ZHANG Feng, ZHENG Jianghua. Evaluation of Landslide Susceptibility by Multiple Adaptive Regression Spline Method[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 442-450. DOI: 10.13203/j.whugis20190136
Citation: ZHAO Zhongguo, ZHANG Feng, ZHENG Jianghua. Evaluation of Landslide Susceptibility by Multiple Adaptive Regression Spline Method[J]. Geomatics and Information Science of Wuhan University, 2021, 46(3): 442-450. DOI: 10.13203/j.whugis20190136

多元自适应回归样条法的滑坡敏感性评价

Evaluation of Landslide Susceptibility by Multiple Adaptive Regression Spline Method

  • 摘要: 针对一般滑坡敏感性评价方法不能有效筛选滑坡条件因子的问题,以中国新疆维吾尔自治区新源县为研究区,基于15个滑坡敏感性条件因子,利用多元自适应回归样条法构建了滑坡敏感性指数预测模型,并自动筛选出研究区滑坡敏感性条件因子,在此基础上,实现了新源滑坡敏感性制图。此外,使用逻辑回归方法与多元自适应回归样条法进行精度对比分析。结果显示,采用多元自适应回归样条法构建的滑坡敏感性模型精度优于逻辑回归,其成功率曲线的精度为0.945 4,预测率曲线的精度为0.923 8。同时,模型还筛选出新源县滑坡重要影响条件因子(高程、坡度、降雨量、距断层距离、归一化差分植被指数、平面曲率、岩组)。研究表明,利用多元自适应回归样条构建的新源县滑坡敏感性模型是滑坡预测的有效方法,可为防灾减灾提供决策支持。

     

    Abstract:
      Objectives  According to general landslide susceptibility evaluation methods, landslide condition factors cannot be effectively selected.
      Methods  The prediction model of landslide susceptibility index (LSI) is constructed by multiple adaptive regression spline (MARS), and the landslide susceptibility condition factors are automatically selected, and the landslide susceptibility map is produced by 15 landslide susceptibility factors. In addition, the accuracy of the model is compared between logistic regression (LR) and MARS.
      Results  The results show that the accuracy of landslide susceptibility model constructed by MARS is better than LR. The accuracy of MARS success curve is 0.945 4, and the accuracy of MARS prediction rate curve is 0.923 8. At the same time, the model also selects the important influencing factors of landslide (elevation, slope angle, rainfall, distance to faults, NDVI, plan curvature, geological petrofabric).
      Conclusions  Research suggests that the MARS is an effective method for landslide prediction in study area and can provide decision support for reducing nature disaster.

     

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