大数据环境下平原水库健康系统预警实证研究

发布时间：2018-04-14 04:35

本文选题：平原水库 + 大数据　；参考：《应用基础与工程科学学报》2017年05期

【摘要】：为了解决平原水库安全评价忽略生态和可持续性的不足,针对平原水库面积大、水深小、坝线长和地基处理差的特点,在健康功能分区的基础上,综合考虑安全、生态特征、可持续性和社会政治环境等因素构建平原水库健康警源指标体系,指标体系包括围坝安全、水库社会效益、水库生态效应和状态持续性4类一级指标和15个二级指标.利用积累的海量数据资料和信息反馈形成的数据库,基于决策树C5.0算法建立大数据环境下平原水库健康预警模型,并以丁东水库健康预警为例进行实证研究.研究结果表明,平原水库健康预警模型精度为91.92%;实证规则集揭示影响水库健康警度分类的因素主要有测压管水位指标、水质安全指标、地下水位动态指标和泥沙变化指标;水库健康状况为健康,为无警级别;平原水库健康预警模型充分利用大数据资源,可操作性强,可在其他水库健康管理中广泛应用;将大数据思维引入水利管理,用以解决水利面临的数据庞杂、相关利益群体需求多样化和区域统筹困难等难题,对水库库区生态系统可持续发展与科学管理具有重要意义.
[Abstract]:In order to solve the deficiency of neglecting ecology and sustainability in the safety evaluation of plain reservoir, aiming at the characteristics of large area, small water depth, long dam line and poor foundation treatment of plain reservoir, safety and ecological characteristics are comprehensively considered on the basis of health function zoning.The index system of health warning source of plain reservoir is constructed by such factors as sustainability and social and political environment. The index system includes four kinds of first class indexes and 15 secondary indexes, such as dam safety, reservoir social benefit, reservoir ecological effect and state persistence.Based on the C5.0 algorithm of decision tree, the health early warning model of plain reservoir in big data environment is established by using the accumulated massive data and information feedback database, and the example of health early warning of Dingdong Reservoir is studied.The results show that the accuracy of the model is 91.92, and the main factors influencing the classification of reservoir health alarm are water level index, water quality safety index, groundwater level dynamic index and sediment change index.The health status of reservoirs is healthy and no warning is given; the health warning model of plain reservoirs makes full use of big data's resources and has strong maneuverability, and can be widely used in other reservoirs' health management; and the thinking of big data is introduced into water conservancy management.It is of great significance for the sustainable development and scientific management of the ecosystem of reservoir and reservoir area to solve the problems of complex data, diverse needs of relevant interest groups and difficulty of regional coordination.
【作者单位】：山东农业大学水利土木工程学院;农业大数据中心;
【基金】：国家科技支撑计划项目(2015BAB07B05) 山东省省级水利科研及技术项目(SDSLKY201305) 山东省农业重大应用技术创新项目(SDNYCX1531963)
【分类号】：TV697.1

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