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引用本文:孙逸群,包为民,江鹏,徐玉英,贺成民,陈伟东,黄琳煜.基于无迹卡尔曼滤波的新安江模型实时校正方法.湖泊科学,2018,30(2):488-496. DOI:10.18307/2018.0220
SUN Yiqun,BAO Weimin,JIANG Peng,XU Yuying,HE Chengmin,CHENG Weidong,HUANG Linyu.Real-time updating of XAJ model by using Unscented Kalman Filter. J. Lake Sci.2018,30(2):488-496. DOI:10.18307/2018.0220
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基于无迹卡尔曼滤波的新安江模型实时校正方法
孙逸群1, 包为民1, 江鹏2, 徐玉英3, 贺成民1,4, 陈伟东1, 黄琳煜5
1.河海大学水文水资源学院, 南京 210098;2.Desert Research Institute, Las Vegas, NV, 89119;3.辽宁省柴河水库管理局, 铁岭 112000;4.陕西省水文水资源勘测局, 西安 710068;5.上海市浦东新区水文水资源管理署, 上海 200129
摘要:
通过利用实时水文观测数据对洪水预报模型进行校正,可增加流域洪水预报的实时性和精确度.本文讨论了水文模型状态变量选取对滤波效果的影响,并给出了状态变量选取原则.在集总式新安江模型的基础上,结合状态变量选取原则,应用无迹卡尔曼滤波技术构建了新安江模型的实时校正方法.方法应用于闽江邵武流域洪水预报的计算结果表明,采用无迹卡尔曼滤波方法后,不仅能够直接校正模型状态,同时也能有效地提高模型预报精度,适合应用于实际流域洪水预报作业中.
关键词:  流域水文模型  实时校正  无迹卡尔曼滤波  新安江模型  状态变量  闽江  邵武流域
DOI:10.18307/2018.0220
分类号:
基金项目:国家重点基础研究发展计划专项(2016YFC0402703)、国家自然科学基金项目(51709077,41371048,51479062,51709076)和中央高校基本科研业务费专项资金(2017B10914,2015B14314,2017B15414)联合资助.
Real-time updating of XAJ model by using Unscented Kalman Filter
SUN Yiqun1, BAO Weimin1, JIANG Peng2, XU Yuying3, HE Chengmin1,4, CHENG Weidong1, HUANG Linyu5
1.College of Hydrology and Water Resources, Hohai University, Nanjing 210098, P. R. China;2.Desert Research Institute, Las Vegas, NV, 89119;3.Liaoning Provincial Chaihe Reservoir Administration, Tieling 112000, P. R. China;4.Shanxi Provincial Hydrology and Water Resources Survey, Xi'an 710068, P. R. China;5.Shanghai Pudong New Area Hydrology and Water Resource Administration, Shanghai 200129, P. R. China
Abstract:
The performance of real-time flood forecasting can be improved by updating with the real-time observations. The performance of filtering is determined by the state variables and therefore the criteria for choosing state variables is proposed. With this criteria, a real-time updating method of XAJ model is proposed by using the Unscented Kalman Filter and the conceptual XAJ model. The effectiveness of the new method is supported by a real case study where the filter is applied to flood forecasting in Shaowu Basin, Min River. The results shows that the method using UKF can remarkably update the state variables and improve the accuracy of flood forecasting. It is practical and can be applied to real flood forecasting tasks.
Key words:  Hydrology model  real-time updating  Unscented Kalman Filter  XAJ model  state variables  Min River  Shaowu Basin
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