地震研究

在地震应急阶段,如何定量评估地震滑坡道路中断风险是一项亟待完善的关键技术。为解决这一难题,以2008年汶川MS8.0地震、2014年鲁甸MS6.5地震及2012年彝良MS5.7、5.6地震为案例开展地震滑坡道路中断风险应急评估模型的构建和检验。汶川研究区用来建立地震滑坡道路中断风险多变量决策树的应急评估模型,并对模型作有效性评价,鲁甸和彝良研究区用来对所建模型开展相似区域外延适用性的评价。通过P值检验模型统计学的显著性,使用Kappa值评价模型推断结果与实际情况的一致性。汶川研究区的P值为2.52×10-203,Kappa系数为0.91。说明使用模型计算出的道路中断风险是地震滑坡道路是否中断的良好指标。鲁甸和彝良研究区的P值为9.7×10-107,Kappa系数为0.81。这表明在允许一定误差的情况下,本研究建立的地震滑坡道路中断风险多变量决策树应急评估模型可以推广应用到其它类似地区。

In mountainous areas,roads are often damaged by earthquake-induced landslides.The degree of road damage and the existing functional state will have a very important impact on the whole earthquake emergency response.To solve this problem,in this paper we propose a new modle.To test our new model,we select four historical earthquake events for case study:the 2008 Wenchuan,Sichuan MS8.0 earthquake,the 2014 Ludian,Yunnan MS6.5 earthquake,and the 2012 Yiliang,Yunnan MS5.6&5.7 double earthquakes.Here,the Wenchuan earthquake case is for testing the effectiveness of our new model.The other three cases serve for testing the practicability of our model for the future earthquake disasters emergency assessment in other places.P-value and Kappa coefficient are used to examine the model's statistical portability.The computed P-value for the Sichuan area was less than 0.001(just 2.52×10-203)and the Kappa coefficient is 0.91.This suggests that the computed RIR values are good indicators of blocked-road occurrences.In the Yunnan study areas,the computed P-value is less than 0.001 too(just 9.7×10-107)and the Kappa coefficient is 0.81.The results from the Yunnan study areas suggest that the Multivariate Decision Tree can be introduced to another study areas and our approach can be used in other earthquake events.

引言
1 方法和数据
2 研究结果
3 讨论
4 结论

图1 基于GIS和多变量决策树的地震滑坡道路中断风险水平空间分布应急评估流程<br/>Fig.1 Framework of emergency assessment of road disruption risk influenced by the earthquake-induced landslide based on GIS and Multivariate Decision Tree

图1 基于GIS和多变量决策树的地震滑坡道路中断风险水平空间分布应急评估流程
Fig.1 Framework of emergency assessment of road disruption risk influenced by the earthquake-induced landslide based on GIS and Multivariate Decision Tree

图2 汶川地震滑坡滑动距离空间分布<br/>Fig.2 The sliding distances of landslides induced by the Wenchuan MS8.0 earthquake

图2 汶川地震滑坡滑动距离空间分布
Fig.2 The sliding distances of landslides induced by the Wenchuan MS8.0 earthquake

图3 路段缓冲区及滑坡易发性像元统计示意图<br/>Fig.3 Road buffer and the landslide susceptibility illustrated by image elements

图3 路段缓冲区及滑坡易发性像元统计示意图
Fig.3 Road buffer and the landslide susceptibility illustrated by image elements

表1 缓冲区属性示例表<br/>Tab.1 A demonstration of buffers' attribute

表1 缓冲区属性示例表
Tab.1 A demonstration of buffers' attribute

图4 地震滑坡易发性水平空间分布应急评估图流程<br/>Fig.4 Flow chart showing the assessment of the earthquake-induced landslide susceptibility

图4 地震滑坡易发性水平空间分布应急评估图流程
Fig.4 Flow chart showing the assessment of the earthquake-induced landslide susceptibility

图5 汶川MS8.0地震(a)、鲁甸MS6.5地震及2012年彝良MS5.7、5.6地震(b)研究区概况<br/>Fig.5 The Wenchuan MS8.0 earthuqake area(a),the Lundian MS6.5 earthquake area,and Yiliang MS5.7,5.6 earthquake area(b)

图5 汶川MS8.0地震(a)、鲁甸MS6.5地震及2012年彝良MS5.7、5.6地震(b)研究区概况
Fig.5 The Wenchuan MS8.0 earthuqake area(a),the Lundian MS6.5 earthquake area,and Yiliang MS5.7,5.6 earthquake area(b)

图6 研究区地震滑坡易发性水平空间分布反演图<br/>Fig.6 Distribution of the inverted landslide susceptibility in the study areas

图6 研究区地震滑坡易发性水平空间分布反演图
Fig.6 Distribution of the inverted landslide susceptibility in the study areas

图7 汶川研究区地震滑坡中断路段(a)和其它路段(b)滑坡属性统计图<br/>Fig.7 Number of per-susceptibility lever cells in blocked-sections(a)and other sections(b)

图7 汶川研究区地震滑坡中断路段(a)和其它路段(b)滑坡属性统计图
Fig.7 Number of per-susceptibility lever cells in blocked-sections(a)and other sections(b)

表2 滑坡易发性与道路中断风险列联表<br/>Tab.2 Contingency table of road disruption risk and landslide susceptibility

表2 滑坡易发性与道路中断风险列联表
Tab.2 Contingency table of road disruption risk and landslide susceptibility

图8 汶川研究区地震滑坡道路中断风险多变量决策树<br/>Fig.8 The Multivariate Decision Tree obtained from the analysis of road disruption in the Wenchuan study area

图8 汶川研究区地震滑坡道路中断风险多变量决策树
Fig.8 The Multivariate Decision Tree obtained from the analysis of road disruption in the Wenchuan study area

表3 各叶节点的基尼指数<br/>Tab.3 Gini indexes of leaf nodes

表3 各叶节点的基尼指数
Tab.3 Gini indexes of leaf nodes

表4 基于GIS和多变量决策树模型评估的汶川研究区地震滑坡道路中断风险结果<br/>Tab.4 Evaluation of the road disruption by the Multivariate Decision Tree in Wenchuan area

表4 基于GIS和多变量决策树模型评估的汶川研究区地震滑坡道路中断风险结果
Tab.4 Evaluation of the road disruption by the Multivariate Decision Tree in Wenchuan area

图9 研究区地震滑坡道路中断风险反演<br/>Fig.9 Distributions of the inverted road disruption risk in study areas

图9 研究区地震滑坡道路中断风险反演
Fig.9 Distributions of the inverted road disruption risk in study areas

表5 基于多变量决策树模型的鲁甸和彝良研究区地震滑支道路中断风险评估结果<br/>Tab.5 Evaluation of the road interruption risk influenced by earthquake-induced landslides in Ludian-Yiliang study area by the Multivariate Decision Tree

表5 基于多变量决策树模型的鲁甸和彝良研究区地震滑支道路中断风险评估结果
Tab.5 Evaluation of the road interruption risk influenced by earthquake-induced landslides in Ludian-Yiliang study area by the Multivariate Decision Tree

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备注

引言

1 方法和数据

2 研究结果

3 讨论

4 结论

期刊信息