摘要
为提高明渠系统中闸门综合流量系数的识别精度与水动力模型的模拟精度,构建了一种集合卡尔曼滤波(En KF)与一维非恒定流模型耦合的参数同化方法,选取4类水情监测数据(上游水位、闸前水位、闸后水位、过闸流量)作为同化观测系统,并应用于一闸—两渠池典型明渠闸控系统综合流量系数的反演与更新。在此基础上,采用最小二乘法识别了同化综合流量系数与闸门开度间的二次函数关系,构建了适用于明渠闸门的综合流量系数预测模型。相较于传统经验公式系数用于水力学模型模拟的结果,同化反演拟合系数用于水动力模拟结果中上游水位的平均绝对误差M_(MAE)下降了71.4%,决定系数R~2提升了25.2%;闸前水位的M_(MAE)下降了62.5%,R~2提升了18.4%。可见该方法能够实现对综合流量系数的实时校正与动态估计,显著提升了明渠系统水动力模型模拟精度与参数可靠性。
In order to improve the identification accuracy of the integrated flow coefficients of gates and the simulation accuracy of the hydrodynamic model in open channel system,this paper constructs a parameter assimilation method coupled with an Ensemble Kalman Filter(EnKF)and one-dimensional unsteady flow model.Four types of water information monitoring data(upstream water level,pre-gate level,post-gate level,and flow rate through the gates)were chosen as the assimilation observation system,and it was applied to the inversion and updating of a typical open channel gate control system with one gate and two channels.On this basis,the least-squares method was used to identify the quadratic relationship between the assimilated integrated flow coefficients and gate openings,and the integrated flow coefficient prediction model for open channel gates was constructed.Compared with the traditional empirical formula coefficients used in hydrodynamic model simulation,the M_(MAE) of the upstream water level in the assimilated inverse fitting coefficients used in the hydrodynamic simulation results decreased by 71.4%,and the R2 was improved by 25.2%;The M_(MAE) of the water level in front of the gate decreased by 62.5%,and the R2 was improved by 18.4%.Thus,the method can achieve realtime correction and dynamic estimation of the integrated flow coefficients,which significantly improves the simulation accuracy and parameter reliability of the hydrodynamic model of the open channel system.
作者
韦云姣
张召
李月强
王艺霖
张家驹
许江涛
WEI Yun-jiao;ZHANG Zhao;LI Yue-qiang;WANG Yi-lin;ZHANG Jia-ju;XU Jiang-tao(School of Water and Environment,Chang'an University,Xi'an 710054,China;Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of the Ministry of Education,Chang'an University,Xi'an 710054,China;Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of Ministry of Water Resources,Chang'an University,Xi'an 710054,China;Department of Water Resources,China Institute of Water Resources and Hydropower Research,Beijing 100038,China;College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China;China South-to-North Water Diversion Middle Route Corporation Limited,Beijing 100038,China;China South-to-North Water Diversion Group Water Network Intelligent Technology Co.,Ltd.,Beijing 100038,China)
出处
《水电能源科学》
北大核心
2025年第10期110-114,共5页
Water Resources and Power
基金
国家重点研发计划(2022YFC3204604)。
关键词
集合卡尔曼滤波
数据同化
水动力模拟
综合流量系数
参数反演
Ensemble Kalman filtering
data assimilation
hydrodynamic simulation
flow coefficient
parameter inversion
