{"title":"川滇地区地震诱发滑坡危险性评价模型及软件开发","authors":"Xiaoyi Shao, Si-yuan Ma, Chong Xu","doi":"10.5194/gmd-16-5113-2023","DOIUrl":null,"url":null,"abstract":"Abstract. To enhance the timeliness and accuracy of spatial prediction of\ncoseismic landslides, we propose an improved three-stage spatial prediction\nstrategy and develop corresponding hazard assessment software named\nMat.LShazard V1.0. Based on this software, we evaluate the applicability of\nthis improved spatial prediction strategy in six earthquake events that have\noccurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian,\nLushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that\nin the first stage (immediately after the quake event), except for the 2013\nMinxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan\nearthquake is the highest, reaching 0.947. The prediction results in the\nfirst stage can meet the requirements of emergency rescue by immediately\nobtaining the overall predicted information of the possible coseismic\nlandslide locations in the quake-affected area. In the second and third\nstages, with the improvement of landslide data quality, the prediction\nability of the model based on the entire landslide database is gradually\nimproved. Based on the entire landslide database, the AUC value of the six\nevents exceeds 0.9, indicating a very high prediction accuracy. For the\nsecond and third stages, the predicted landslide area (Ap) is relatively\nconsistent with the observed landslide area (Ao). However, based on the\nincomplete landslide data in the meizoseismal area, Ap is much smaller than\nAo. When the prediction model based on complete landslide data is built, Ap\nis nearly identical to Ao. This study provides a new application tool for\ncoseismic landslide disaster prevention and mitigation in different stages\nof emergency rescue, temporary resettlement, and late reconstruction after a\nmajor earthquake.\n","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China\",\"authors\":\"Xiaoyi Shao, Si-yuan Ma, Chong Xu\",\"doi\":\"10.5194/gmd-16-5113-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. To enhance the timeliness and accuracy of spatial prediction of\\ncoseismic landslides, we propose an improved three-stage spatial prediction\\nstrategy and develop corresponding hazard assessment software named\\nMat.LShazard V1.0. Based on this software, we evaluate the applicability of\\nthis improved spatial prediction strategy in six earthquake events that have\\noccurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian,\\nLushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that\\nin the first stage (immediately after the quake event), except for the 2013\\nMinxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan\\nearthquake is the highest, reaching 0.947. The prediction results in the\\nfirst stage can meet the requirements of emergency rescue by immediately\\nobtaining the overall predicted information of the possible coseismic\\nlandslide locations in the quake-affected area. In the second and third\\nstages, with the improvement of landslide data quality, the prediction\\nability of the model based on the entire landslide database is gradually\\nimproved. Based on the entire landslide database, the AUC value of the six\\nevents exceeds 0.9, indicating a very high prediction accuracy. For the\\nsecond and third stages, the predicted landslide area (Ap) is relatively\\nconsistent with the observed landslide area (Ao). However, based on the\\nincomplete landslide data in the meizoseismal area, Ap is much smaller than\\nAo. When the prediction model based on complete landslide data is built, Ap\\nis nearly identical to Ao. This study provides a new application tool for\\ncoseismic landslide disaster prevention and mitigation in different stages\\nof emergency rescue, temporary resettlement, and late reconstruction after a\\nmajor earthquake.\\n\",\"PeriodicalId\":12799,\"journal\":{\"name\":\"Geoscientific Model Development\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2023-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscientific Model Development\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.5194/gmd-16-5113-2023\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscientific Model Development","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/gmd-16-5113-2023","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
Abstract. 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.
期刊介绍:
Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication:
* geoscientific model descriptions, from statistical models to box models to GCMs;
* development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results;
* new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data;
* papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data;
* model experiment descriptions, including experimental details and project protocols;
* full evaluations of previously published models.