地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (12): 1623-1633.doi: 10.3724/SP.J.1047.2017.01623

• 山洪/泥石流灾害风险评价 • 上一篇    下一篇

支持向量机与Newmark模型结合的地震滑坡易发性评估研究

林齐根(), 刘燕仪, 刘连友, 王瑛*()   

  1. 1. 北京师范大学 环境演变与自然灾害教育部重点实验室,北京 100875
    2. 北京师范大学减灾与应急管理研究院,北京 100875
  • 收稿日期:2017-07-21 修回日期:2017-08-31 出版日期:2017-12-25 发布日期:2017-12-25
  • 通讯作者: 王瑛 E-mail:linqigen@mail.bnu.edu.cn;wy@bnu.edu.cn
  • 作者简介:

    作者简介:林齐根(1991-),男,广东汕头人,博士生,研究方向为灾害风险评估模型。E-mail: linqigen@mail.bnu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41271544);地表过程模型与模拟创新研究群体科学基金(41621061);国家重点研发计划专项项目(2016YFA0602403)

Earthquake-triggered Landslide Susceptibility Assessment Based on Support Vector Machine Combined with Newmark Displacement Model

LIN Qigen, LIU Yanyi, LIU Lianyou, WANG Ying*()   

  1. 1. Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China
    2. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
  • Received:2017-07-21 Revised:2017-08-31 Online:2017-12-25 Published:2017-12-25
  • Contact: WANG Ying E-mail:linqigen@mail.bnu.edu.cn;wy@bnu.edu.cn

摘要:

Newmark位移模型是研究地震滑坡易发性的经典模型,机器学习方法支持向量机模型也越来越多的应用到滑坡易发性评估研究。本文将Newmark位移模型与支持向量机模型相结合,建立基于物理机理的地震滑坡易发性评估模型并应用于2008年汶川地震重灾区汶川县。从震后遥感影像目视解译出汶川县1900处地震诱发滑坡,并将其随机划分为70%的训练数据集和30%的验证数据集。选择地形起伏度、坡度、地形曲率、与构造断裂带距离、与水系距离、与道路距离6个因子与Newmark位移值共同作为地震滑坡易发性影响因素。利用ROC曲线和模型不确定性等指标对模型结果进行评估,并与二元统计模型频率比和多元统计模型Logistic回归的结果进行对比。结果表明:与频率比和Logistic回归模型相比,支持向量机模型的正确率最高,训练集和验证集ROC曲线下的面积分别为0.876和0.851。将模型应用于绘制汶川县地震滑坡易发性图,结果显示滑坡易发性图与实际的滑坡点位分布一致性较高,有80.4%的滑坡位于极高和高易发区。这说明支持向量机与Newmark位移方法结合建立的地震滑坡易发性评估模型有较高的预测价值,可以为滑坡风险评估和管理提供依据。

关键词: 滑坡易发性评估, 地震滑坡, 机器学习, 支持向量机, Newmark位移模型

Abstract:

The Newmark displacement model is a common physically based model for earthquake induced landslide susceptibility mapping. The machine learning model is one of the statistical methods and is increasingly applied to landslide susceptibility mapping in recent years. The main purpose of this paper is to combine the Newmark displacement method with machine learning model for developing a mechanism-based earthquake-induced landslide susceptibility model and improving the predictive accuracy. The support vector machine (SVM) method was selected and eight thematic data layers, including landslide inventory, topographic relief, slope, curvature, Newmark displacement value, distance from faults, distance from drainages and distance from roads, were prepared in GIS. A total of 1,900 landslides were subsequently randomly divided into two subsets: a training subset comprising 70% of the landslides and a validation subset containing the remaining 30%. The model is then applied to Wenchuan County, which was one of the most severely affected areas during the May 12, 2008 (Mw 7.9) Wenchuan earthquake in China. The model performance was evaluated using the receiver operation characteristic (ROC) curve and the model estimation uncertainty in comparison with the other two statistical methods: frequency ratio (FR) bivariate statistical model and the logistic regression (LR) multivariate statistical model. The results show that five variables including topographic relief, Newmark displacement value, distance from faults, distance from drainages and distance from roads are finally selected based on a significance level of 0.05 and the multi-collinearity detection. The values of the area under the ROC curve (AUC) demonstrate that the SVM model exhibited the highest accuracy for the training and validation data sets with AUC values of 0.876 and 0.851, respectively, followed by the LR model (AUC values of 0.836 and 0.842 for training and validation, respectively) and FR model (AUC values of 0.844 and 0.808 for training and validation, respectively). For the evaluation of the model prediction uncertainties, the pixels classified as high susceptibility (probability ≥ 0.75) and low susceptibility (probability < 0.25) are more valuable and practical, both of which have high reliability to determine whether the location is stable. The prediction variation is low for pixels classified as high susceptibility and low susceptibility (with an average of the standard deviation less than 0.05), indicating that the SVM and LR models consistently identified these pixels as stable or unstable. Furthermore, these high and low susceptibility pixels account for about 70% of all pixels for the SVM model, which is about 25% higher than that of LR model. It means that the SVM model performs better than the LR model in these high reliability pixels. For the pixels classified as intermediate susceptibility (probability 0.25 ~ 0.75), the standard deviation of the predictive probability of the SVM model is about 0.09, which is larger than that of the LR model. It indicates that the SVM model exhibited larger uncertainty than the LR model in these intermediate susceptibility pixels. However, it is difficult to determine whether these pixels are stable or not. Also, the pixels with intermediate susceptibility only account for about 30% of the total samples for the SVM model. In general, the SVM model combined with the Newmark displacement method outperform the LR model in accuracy and uncertainty evaluation. The proposed method was applied to produce the earthquake-triggered landslide susceptibility map of Wenchuan County. The comparison of landslide susceptibility map and actual landslide distribution showed that the high susceptibility areas can account for about 80.4% of the actual landslides. This indicates that the combination of support vector machine and the Newmark displacement method has a higher predictive value. The proposed method can potentially help risk assessment and effective management of landslides caused by earthquakes.

Key words: landslide susceptibility assessment, earthquake-triggered landslides, machine learning, support vector machine, Newmark displacement model