| 基于信息量法的地质灾害气象风险预警模型:以甘肃省陇南地区为例 |
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| 引用本文： | 罗鸿东,李瑞冬,张勃,曹博.基于信息量法的地质灾害气象风险预警模型:以甘肃省陇南地区为例[J].地学前缘,2019,26(6):289-297. |
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| 作者姓名： | 罗鸿东 李瑞冬 张勃 曹博 |
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| 作者单位： | 西北师范大学地理与环境科学学院,甘肃兰州730070;甘肃省地质环境监测院,甘肃兰州730050;甘肃省地质环境监测院,甘肃兰州730050;甘肃省地下水工程及地热资源重点实验室,甘肃兰州730050;西北师范大学地理与环境科学学院,甘肃兰州730070 |
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| 基金项目： | 国土资源部公益性行业科研专项(201511053)；甘肃省地质灾害应急能力建设科研专项(2017121003) |
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| 摘 要： | 地质灾害气象风险预警是目前地质灾害防治研究领域的难点和热点。陇南地区是中国地质灾害造成人员财产损失和受灾害威胁最严重的区域之一，为精细化和准确化预报陇南地区地质灾害风险，在ArcGIS平台将研究区划分为250 m×250 m的栅格单元，使用信息量法选取9个影响因素进行地质环境敏感性评价，结合有效降雨量构建地质灾害气象风险预警模型；该模型通过6次历史降雨事件引发的156起地质灾害验证，预报准确率为83.42%，提高了研究区内地质灾害风险预警精度。该研究基于信息量法的地质环境敏感性分区客观合理，综合考虑下垫面和气象要素的第二代预警模型在类似地区的应用，有较高的准确性和适用性。
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| 关 键 词： | 地质灾害 气象风险预警 信息量法 地质环境敏感性 |
| 收稿时间： | 2018-12-10 |
An early warning model system for predicting meteorological risk associated with geological disasters in the Longnan area,Gansu Province based on the information value method |
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| LUO Hongdong,LI Ruidong,ZHANG Bo,CAO Bo.An early warning model system for predicting meteorological risk associated with geological disasters in the Longnan area,Gansu Province based on the information value method[J].Earth Science Frontiers,2019,26(6):289-297. |
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| Authors: | LUO Hongdong LI Ruidong ZHANG Bo CAO Bo |
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| Abstract: | Early warning of meteorological risk associated with geological disasters is difficult and it is presently the focus of geological disaster prevention and management. The Longnan area in Gansu Province is one of the most hazardous areas in China with major casualties and property losses caused by natural disasters. This study is aimed to accurately predict the meteorological risk associated with geological disasters in Longnan. By using ArcGIS software, we divided the research area into 250 m×250 m raster and selected 9 factors to assess the areas susceptibility of geological environment by the information value method. Combining effective precipitation and susceptibility we constructed an early warning model system which was verified and tested by 6 historical rainfall events. The system has a statistical prediction accuracy of 83.42%, which is an improvement for the study area. We show that the geoenvironmental susceptibility division, based on the information value method, is objective and sensible. And a second generation early-warning system that integrates underlying surface and meteorological factors should have higher prediction accuracy and applicability in the study area. |
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| Keywords: | geological disasters meteorological risk early warning information value susceptibility of geological environment |
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