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针对传统知识驱动型滑坡灾害研究多依赖专业人员经验,具有主观性和不确定性的问题,该文提出了基于数据驱动滑坡致灾因子评价及危险性区划的方法。采用证据权模型,较好地平衡了滑坡危险性区划中准确性与高效性之间的矛盾,实现了较为精确的滑坡易发性及危险性区划;利用感知层、网络层、应用层的物联网技术,实现了高危险区滑坡点在线预警监测。3S技术支持下的滑坡危险性区划及监测实验结果表明:所用模型及监测技术不仅可以准确评价滑坡致灾因子权重及危险性区划,还能够精准、高效实现滑坡点实时监控预警。
In view of the subjectivity and uncertainty of traditional knowledge-driven landslide disaster research, which rely more on professional experience, this paper presents a method based on data-driven landslide hazard assessment and hazard zoning. The model of evidence right balances the contradiction between accuracy and efficiency of landslide risk zoning, and achieves a more accurate landslide susceptibility and risk zoning. Using the perception layer, network layer, application layer objects Networking technology to achieve high-risk landslide point on-line early warning and monitoring. Landslide risk zoning and monitoring experiments under 3S technical support show that the models and monitoring techniques can not only accurately evaluate the weight and risk zoning of landslide hazard factors, but also realize the real-time monitoring and early warning of landslide points accurately and efficiently.