地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (12): 1699-1709.doi: 10.12082/dqxxkx.2018.180349

• 地球信息科学理论与方法 • 上一篇    下一篇

基于确定性系数和支持向量机的地质灾害易发性评价

李远远(), 梅红波*(), 任晓杰, 胡旭东, 李梦迪   

  1. 中国地质大学(武汉)资源学院,武汉 430074
  • 收稿日期:2018-07-27 出版日期:2018-12-25 发布日期:2018-12-20
  • 通讯作者: 梅红波 E-mail:1425694503@qq.com;hbmei@cug.edu.cn
  • 作者简介:

    作者简介:李远远(1992-),男,河南驻马店,硕士生,主要从事机器学习和地质灾害评价方面的研究。E-mail: 1425694503@qq.com

  • 基金资助:
    云南省级地质灾害防治项目(2016025007)

Geological Disaster Susceptibility Evaluation Based on Certainty Factor and Support Vector Machine

LI Yuanyuan(), MEI Hongbo*(), REN Xiaojie, HU Xudong, LI Mengdi   

  1. Faculty of Earth Resources, China University of Geosciences, Wuhan 430074, China
  • Received:2018-07-27 Online:2018-12-25 Published:2018-12-20
  • Contact: MEI Hongbo E-mail:1425694503@qq.com;hbmei@cug.edu.cn
  • Supported by:
    Geological Hazards Investigation Project of Yunnan Province, No.2016025007.

摘要:

确定性系数(Certainty Factor,CF)是经典的地质灾害影响因子敏感性分析方法;支持向量机(Support Vector Machine, SVM)作为机器学习的代表方法,能够综合各个影响因子的关系,对地质灾害易发性进行评价。本文以云南省怒江州泸水县为研究区,将高程、坡度、坡向、剖面曲率、距断裂的距离、距河网的距离、距路网的距离、地貌类型、岩土体类型、土地利用类型作为该区域地质灾害影响因子,依据各影响因子灾害面积比和分级面积比曲线对影响因子的状态进行分级。根据381个地质灾害隐患点,采用CF方法计算的各个影响因子的敏感性值,作为SVM的分类数据,建立基于CF-SVM的易发性评估模型,同时与单独SVM模型的评价结果进行对比分析。结果表明,CF-SVM模型得到的极高和高易发区主要分布在怒江两岸河谷地带,涵盖了89.76%的地质灾害隐患点,比单独SVM模型具有更高的成功率;利用ROC曲线和P-R曲线对两个模型进行检验,CF-SVM模型的评价精度分别达到92%和88%,均高于单独的SVM。由此说明,CF-SVM模型对地质灾害易发性评价有较高的预测价值,可以为地质灾害风险评估和管理提供依据。

关键词: 地质灾害, 确定性系数, 支持向量机, 易发性评价, 泸水县

Abstract:

The Certainty Factor (CF) is a classical method of sensitivity analysis for geological hazards, which can be used to quantify complex multi-factors in the same range, and CF value directly represents the contribution of each factor to geological hazards. The Support Vector Machine (SVM) is a representative method for machine learning, which can be used to evaluate the susceptibility of geological hazards on the basis of various impact factors. The main purpose of this paper is to combine CF method with SVM method called CF-SVM model for developing geological hazards susceptibility model which is applied to Lushui County of Yunnan Province. To assess geological hazards susceptibility, the paper selects ten impact factors, including the elevation, slope angle, slope aspect, profile curvature, distance to faults, distance to rivers, distance to roads, rock soil mass types, geomorphologic type, the land use, which were calculated as the most important impact factors. The state of each impact factor was graded based on the geological hazards area ratio curve and the grading area ratio curve. The CF method is applied to calculated the sensitivity values of each factor on the basis of 381 geological hazards, and the under-sampling method was used to select 381 non-geological hazards in Lushui county. The 381 geological hazards and 381 non-geological hazards hazards are the classification data of SVM method and geological hazards susceptibility maps were produced. Research shows that the extremely high and the high susceptibility areas are mainly distributed on the both sides of the Nujiang River. The comparision of geological hazards susceptibility map and actual geological hazards distribution showed that extremely high and the high susceptibility areas can account for about 89.76% of the actual geological hazards for CF-SVM model which is better than the single SVM model. The model performance was evaluated by the receiver operation characteristic (ROC) and precision recall (P-R) in the two models. Through ROC and P-R of the results, the prediction power of the CF-SVM model is superior to the single SVM model, and prediction accuracy of the CF-SVM model reached 92% and 88% respectively. This indicates that the combination of the CF and SVM has a higher predictive value. The research provides guidance and technical reference for the nationwide county geological hazards evaluation, and the proposed model can be used assess and manage the risk of geological hazard.

Key words: geological hazard, certainty factor, support vector machine, susceptibility evaluation, Lushui County