Hazard assessment modeling and software development of earthquake-triggeredlandslides in the Sichuan-Yunnan area, China

被引:3
|
作者
Shao, Xiaoyi [1 ,2 ]
Ma, Siyuan [3 ,4 ]
Xu, Chong [1 ,2 ]
机构
[1] Minist Emergency Management China, Natl Inst Nat Hazards, Beijing 100085, Peoples R China
[2] Minist Emergency Management China, Key Lab Cpd & Chained Nat Hazards Dynam, Beijing 100085, Peoples R China
[3] China Earthquake Adm, Inst Geol, Beijing 100029, Peoples R China
[4] China Earthquake Adm, Inst Geol, Key Lab Seism & Volcan Hazards, Beijing 100029, Peoples R China
关键词
LANDSLIDE SUSCEPTIBILITY ASSESSMENT; SUPPORT VECTOR MACHINE; REAL-TIME PREDICTION; SPATIAL-DISTRIBUTION; LOGISTIC-REGRESSION; JIUZHAIGOU EARTHQUAKE; WENCHUAN EARTHQUAKE; MINXIAN-ZHANGXIAN; FREQUENCY RATIO; INVENTORY;
D O I
10.5194/gmd-16-5113-2023
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
To enhance the timeliness and accuracy of spatial prediction of coseismic landslides, we propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software named Mat.LShazard V1.0. Based on this software, we evaluate the applicability of this improved spatial prediction strategy in six earthquake events that have occurred near the Sichuan-Yunnan region, including the Wenchuan, Ludian, Lushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that in the first stage (immediately after the quake event), except for the 2013 Minxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan earthquake is the highest, reaching 0.947. The prediction results in the first stage can meet the requirements of emergency rescue by immediately obtaining the overall predicted information of the possible coseismic landslide locations in the quake-affected area. In the second and third stages, with the improvement of landslide data quality, the prediction ability of the model based on the entire landslide database is gradually improved. Based on the entire landslide database, the AUC value of the six events exceeds 0.9, indicating a very high prediction accuracy. For the second and third stages, the predicted landslide area (Ap) is relatively consistent with the observed landslide area (Ao). However, based on the incomplete landslide data in the meizoseismal area, Ap is much smaller than Ao. When the prediction model based on complete landslide data is built, Ap is nearly identical to Ao. This study provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages of emergency rescue, temporary resettlement, and late reconstruction after a major earthquake.
引用
收藏
页码:5113 / 5129
页数:17
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