测绘学报 ›› 2015, Vol. 44 ›› Issue (12): 1351-1358.doi: 10.11947/j.AGCS.2015.20140416

• 摄影测量学与遥感 • 上一篇    下一篇

水文变化语义约束的实时水位观测数据流在线滤波方法

丁雨淋1,2,3, 朱庆1,4, 何小波1,4, 林珲3, 杜志强5, 张叶廷5, 苗双喜1,4, 杨晓霞6   

  1. 1. 西南交通大学高速铁路运营安全空间信息技术国家地方联合工程实验室, 四川 成都 611756;
    2. 江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 江西 南昌 330000;
    3. 香港中文大学太空与地球信息科学研究所, 香港 999077;
    4. 西南交通大学地球科学与环境工程学院, 四川 成都 611756;
    5. 武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079;
    6. 成都理工大学地球科学学院, 四川 成都 610059
  • 收稿日期:2014-08-13 修回日期:2015-06-08 出版日期:2015-12-20 发布日期:2016-01-04
  • 通讯作者: 朱庆,E-mail:zhuq66@263.net E-mail:zhuq66@263.net
  • 作者简介:丁雨淋(1987-),女,博士,副研究员,研究方向为虚拟地理环境与多维动态地理信息系统。E-mail:rainforests@126.com
  • 基金资助:
    江西省重大生态安全问题监控协同创新中心资助项目(JXS-EW-00);国家自然科学基金(41101354,41201440);国家863计划(2013AA122301)第12期丁雨淋,等:水文变化语义约束的实时水位观测数据流在线滤波方法December 2015 Vol.44 No.12 AGCShttp://xb.sinomaps.com

Real-time Observational Water Level Data Stream Online Filtering Method with Hydrological Changes Semantic Constraints

DING Yulin1,2,3, ZHU Qing1,4, HE Xiaobo1,4, LIN Hui3, DU Zhiqiang5, ZHANG Yeting5, MIAO Shuangxi1,4, YANG Xiaoxia6   

  1. 1. State-Province Joint Engineering Laboratory of Spatial Information Technology of High-speed Rail Safety, Southwest Jiaotong University, Chengdu 611756, China;
    2. Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330000, China;
    3. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong 999077, China;
    4. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;
    5. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
    6. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059
  • Received:2014-08-13 Revised:2015-06-08 Online:2015-12-20 Published:2016-01-04
  • Supported by:
    The Collaborative Innovation Center for Major Ecological Security Issues of Jiangxi Province and Monitoring Implementation (No.JXS-EW-00);The National Natural Science Foundation of China (Nos.41101354;41201440);The National High-tech Research Program of China (863 Program)(No. 2013AA122301)

摘要: 受外界环境和仪器设备等的影响,实时水位观测数据流噪声和数据异常问题突出,严重制约了实时应用效能。针对已有数据清洗方法适应性差,难以根据动态观测数据的变化特征进行动态调整问题,本文提出了一种水文变化语义约束的实时水位观测数据流在线滤波方法:在实时水位观测数据变化特征与水文时空过程动态演变规律之间建立高层语义映射,实现水文变化语义知识约束下的卡尔曼模型参数自适应调整,从而突破传统滤波方法的瓶颈。采用多种降雨情景下的实时水位观测数据进行了试验,证明了该方法结果质量的可靠性。

关键词: 实时水位观测数据流, 水文变化语义约束, 卡尔曼滤波

Abstract: Irregular environmental changes and occasional instrument malfunctions have made noises and exceptions in observational data prominence. Therefore, before processing real-time water level data online, data cleaning is urgently needed to ensure data quality. Since traditional data filtering methods didn't take the data change pattern into consideration, these methods have encountered some severe problems, including the poor adaptability of filter model, the low estimation precision and prohibitively high calculation cost. To overcome these shortcomings, this paper presents a hydrological change semantics constrained online Kalman filtering method: creating dynamic semantic mapping between real-time data changing pattern and the rules of spatial-temporal hydrological process evolution; implementing the change semantic constrained Kalman filtering method to support the adaptive parameter optimization. Observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available.

Key words: real-time observational water level data stream, change semantic constraint, Kalman filtering

中图分类号: