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Identification of abnormal buoy data based on time series correlation analysis method
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Yu Zhang1, Yan Zhou2, Bangyi Tao1, 3, *, Jixing Gu4, Chuan’gao Zhao4, Zengzhou Hao1, Yiwei Zhang1, 5, Haiqing Huang1, Zhihua Mao1
Haiyang Xuebao | 2020, 42(11) : 131 - 141
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Haiyang Xuebao | 2020, 42(11): 131-141
• Article •
Identification of abnormal buoy data based on time series correlation analysis method
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Yu Zhang1, Yan Zhou2, Bangyi Tao1, 3, *, Jixing Gu4, Chuan’gao Zhao4, Zengzhou Hao1, Yiwei Zhang1, 5, Haiqing Huang1, Zhihua Mao1
Affiliations
  • 1 State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
  • 2 Zhejiang Academy of Marine Sciences, Hangzhou 310007, China
  • 3 Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
  • 4 Yantai Marine Environmental Monitoring Center Station, State Oceanic Administration, Yantai 264006, China
  • 5 Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
Published: 2020-11-25 doi: 10.3969/j.issn.0253-4193.2020.11.013
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The identification of abnormal marine ecological buoy data is the key to ensure the quality of buoy data. In this study, we found that the gradual abnormal data type is different from the traditional jump abnormal data through analysis of the coastal buoy data in Zhejiang for many years. With a single parameter analysis method, it is difficult to work out accurately the new gradual abnormal data type of stable and gradual deviation from the normal data. Therefore, multiple parameters correlation coefficient method is proposed based on the relationships between pH, dissolved oxygen and chlorophyll a on the condition of that the correlation between two parameters is stable or even consistent at a certain time series. There are two simple statistical parameters of the cross-correlation coefficient of 8-day time window (R8 d) and the difference of R8 d (ΔR) in this method. Those could be used to automatically detect the gradual abnormal buoy data and do very well. The multiple parameters correlation coefficient method provides a new idea for the gradual abnormal data identification, and also improves the automatic monitoring capability of marine ecological buoy abnormal data.

ecological buoy  /  environmental monitoring  /  validation  /  correlation analysis
Yu Zhang, Yan Zhou, Bangyi Tao, Jixing Gu, Chuan’gao Zhao, Zengzhou Hao, Yiwei Zhang, Haiqing Huang, Zhihua Mao. Identification of abnormal buoy data based on time series correlation analysis method[J]. Haiyang Xuebao, 2020 , 42 (11) : 131 -141 . DOI: 10.3969/j.issn.0253-4193.2020.11.013
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Article Info
doi: 10.3969/j.issn.0253-4193.2020.11.013
  • Receive Date：2019-09-28
  • Online Date：2026-03-27
  • Published：2020-11-25
Article Data
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History
  • Received：2019-09-28
  • Revised：2020-06-22
Funding
Affiliations
    1 State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
    2 Zhejiang Academy of Marine Sciences, Hangzhou 310007, China
    3 Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
    4 Yantai Marine Environmental Monitoring Center Station, State Oceanic Administration, Yantai 264006, China
    5 Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
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表12种不同金属材料的力学参数
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Number of
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Number of
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Percentage of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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