基于确定性系数与支持向量机的滑坡易发性评价
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西安科技大学

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P642.22

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国家自然科学(41674013,41874012)


Landslide susceptibility evaluation based on Certainty Factor and support vector machines
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Xi’an University of Science and Technology

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    准确的滑坡易发性评价对防灾减灾具有重大意义。以略阳县为研究区,在确定性系数模型(CF)易发性分区的基础上,剔除极高和高易发区后选取非滑坡点,提取CF值为支持向量机模型(SVM)的输入值,采取灰狼优化算法得到最优参数建立CF-SVM模型对研究区进行预测,同时与随机选取的非滑坡点SVM模型进行对比。结果表明:CF-SVM模型在极高和高易发区涵盖了74.2%的历史滑坡点,且AUC达到0.95,均高于SVM模型,由此说明CF-SVM模型具有更高的准确率,并且证明了在CF模型基础上选取非滑坡单元的可行性。可为该区域的风险管理提供科学依据。

    Abstract:

    Accurate landslide susceptibility evaluation is of great significance for disaster prevention and mitigation. Taking Loyang County as the study area, based on the Certainty Factor model (CF) susceptibility zoning, non-landslide points were selected after excluding the very high and high susceptibility zones, and the CF value was extracted as the input value of the support vector machine (SVM) model, and the optimal parameters were obtained by the Gray Wolf optimization algorithm to establish the CF-SVM model for predicting the study area, and also compared with the SVM model of randomly selected non-landslide points. The results show that the CF-SVM model covers 74.2% of the historical landslide points in the very high and high susceptibility areas, and the AUC reaches 0.95, both of which are higher than the SVM model, thus indicating that the CF-SVM model has a higher accuracy rate and proving the feasibility of selecting non-landslide units based on the CF model. It can provide a scientific basis for risk management in this region.

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陈芯宇,师芸. 基于确定性系数与支持向量机的滑坡易发性评价[J]. 科学技术与工程, , ():

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  • 收稿日期:2022-06-28
  • 最后修改日期:2022-09-30
  • 录用日期:2022-09-30
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