Acta Geodaetica et Cartographica Sinica >

2015 , Vol. 44 >Issue 12: 1351 - 1358

DOI: https://doi.org/10.11947/j.AGCS.2015.20140416

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

  • DING Yulin ,
  • ZHU Qing ,
  • HE Xiaobo ,
  • LIN Hui ,
  • DU Zhiqiang ,
  • ZHANG Yeting ,
  • MIAO Shuangxi ,
  • YANG Xiaoxia
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  • 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 date: 2014-08-13

  Revised date: 2015-06-08

  Online 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)

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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

Cite this article

DING Yulin , ZHU Qing , HE Xiaobo , LIN Hui , DU Zhiqiang , ZHANG Yeting , MIAO Shuangxi , YANG Xiaoxia . Real-time Observational Water Level Data Stream Online Filtering Method with Hydrological Changes Semantic Constraints[J]. Acta Geodaetica et Cartographica Sinica, 2015 , 44(12) : 1351 -1358 . DOI: 10.11947/j.AGCS.2015.20140416

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