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斜坡灾害预警模型及其综合应用研究

Research on slope disaster early warning model and its comprehensive application

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【作者】 龚淑云欧鸥刘懿俊刘川炜唐嘉锋魏慧剪鑫磊卢涵宇

【Author】 GONG Shu-yun;OU Ou;LIU Yi-jun;LIU Chuan-wei;TANG Jia-feng;WEI Hui;JIAN Xin-lei;LU Han-yu;Geological Bureau of Shenzhen;School of Cyber Security, Chengdu University of Technology;College of Environment and Civil Engineering, Chengdu University of Technology;College of Big Data and Information Engineering,Guizhou University;

【通讯作者】 欧鸥;

【机构】 深圳市地质局成都理工大学网络安全学院成都理工大学环境与土木工程学院贵州大学大数据与信息工程学院

【摘要】 针对正确构建合适的预警预报模型或方法是影响斜坡灾害成功预警预报的核心问题。本文总结了斜坡预警中常用的多种模型与方法,及其在典型工程中的应用效果,重点对其适用性进行了综合对比分析。通过对常用的预警预报方法(极限平衡法、Verhulst模型、灰色预测模型、指数平滑法、神经网络等)在适用条件、优劣性、可操作性及应用效果的详细分析,得到不同模型的选取原则与标准,并结合深圳边坡的特点及影响因素建立对应的预警模型,结果表明在构建斜坡灾害预警预报模型上具有一定的指导意义和应用价值。

【Abstract】 Aiming at the key problem of influencing the successful early warning and forecasting of slope disasters, the proper early warning and forecasting model or method is constructed correctly. This paper summarizes various models and methods commonly used in slope early warning, and their application effects in typical projects, focusing on the comprehensive comparative analysis of their applicability. Through the detailed analysis of the application conditions, advantages and disadvantages, maneuverability and application effects of the commonly used early warning and forecasting methods(limit equilibrium method, Verhulst model, grey forecasting model, exponential smoothing method, neural network, etc.), the selection principles and standards of different models are obtained, and the construction of different models is combined with the characteristics and influencing factors of Shenzhen slope. The corresponding early warning model is established, and the results show that it has certain guiding significance and application value in building the early warning and forecasting model of slope disasters.

【基金】 广东省深圳市地质局地质工程院士工作站专项经费资助项目(2013B090400025);国家自然科学基金资助项目(41501114);四川气象灾害预测预警与应急管理研究中心项目(ZHYJ17-YB08);贵州省科技计划项目(黔科合[2016]2844,[2017]2816)
  • 【文献出处】 广西大学学报(自然科学版) ,Journal of Guangxi University(Natural Science Edition) , 编辑部邮箱 ,2019年04期
  • 【分类号】P642.2
  • 【被引频次】3
  • 【下载频次】364
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