引用本文: 李朝奎, 陈建辉, 魏振伟, 周倩, 周访滨. 显式统计预警模型下地质灾害预警方法及应用[J]. 武汉大学学报 ( 信息科学版), 2019, 44(7): 1020-1026.
LI Chaokui, CHEN Jianhui, WEI Zhenwei, ZHOU Qian, ZHOU Fangbin. Method and Application of Geological Hazard Early Warning Based on Explicit Statistical Principle[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 1020-1026.
 Citation: LI Chaokui, CHEN Jianhui, WEI Zhenwei, ZHOU Qian, ZHOU Fangbin. Method and Application of Geological Hazard Early Warning Based on Explicit Statistical Principle[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 1020-1026.

## Method and Application of Geological Hazard Early Warning Based on Explicit Statistical Principle

• 摘要: 以第2代显式统计预警模型为基础，针对中山市地质条件及降雨情况，构建了满足该地区需求的统计预警模型。在此基础上叠加了地质灾害易发分区图，同时在模型计算过程中，对高程低于10 m的平原地区直接判定为无风险，减少了数据运算，实现了研究区更为精准、单元尺度更小的地质灾害分级预警。与中山市以往采用的单点监测预警效果相比，该模型针对不同的地形地貌进行分区，逐一构建小区域预警模型，得出了地质灾害预警分级图。实践表明，所提出的区域性预警方法较先前的单点预警方法定位更准确，预警等级表现更直观。

Abstract: On the basis of the second generation explicit statistical early warning model, based on the geological conditions and rainfall conditions of Zhongshan, a statistical early warning model for the area is constructed. On this basis, the zoning map of geological hazard is superimposed. At the same time, the plain area with elevation less than 10 meters is directly judged as no risk in the calculation process, so that it can reduce the amount of calculation and realize the more precise geological hazard warning in the study area. Compared with the early warning method of single point installation monitoring equipment in Zhongshan, the research area is divided into some small regions according to different terrain and geomorphology, and then the early warning formula of each region is derived. Finally, the early warning map of geological hazards is obtained by combining different regional formulas and geological hazard prone zoning maps. The experiment shows that the regional early warning method can be more accurate in positioning than the previous method, and the levels of early warning become more intuitive.

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