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Sensors 2018, 18(8), 2628; https://doi.org/10.3390/s18082628

A Data Quality Control Method for Seafloor Observatories: The Application of Observed Time Series Data in the East China Sea

1
State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China
2
School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, QLD 4072, Australia
3
Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Sanya 572000, China
*
Author to whom correspondence should be addressed.
Received: 25 June 2018 / Revised: 6 August 2018 / Accepted: 8 August 2018 / Published: 10 August 2018
(This article belongs to the Special Issue Underwater Sensing, Communication, Networking and Systems)
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Abstract

With the construction and deployment of seafloor observatories around the world, massive amounts of oceanographic measurement data were gathered and transmitted to data centers. The increase in the amount of observed data not only provides support for marine scientific research but also raises the requirements for data quality control, as scientists must ensure that their research outcomes come from high-quality data. In this paper, we first analyzed and defined data quality problems occurring in the East China Sea Seafloor Observatory System (ECSSOS). We then proposed a method to detect and repair the data quality problems of seafloor observatories. Incorporating data statistics and expert knowledge from domain specialists, the proposed method consists of three parts: a general pretest to preprocess data and provide a router for further processing, data outlier detection methods to label suspect data points, and a data interpolation method to fill up missing and suspect data. The autoregressive integrated moving average (ARIMA) model was improved and applied to seafloor observatory data quality control by using a sliding window and cleaning the input modeling data. Furthermore, a quality control flag system was also proposed and applied to describe data quality control results and processing procedure information. The real observed data in ECSSOS were used to implement and test the proposed method. The results demonstrated that the proposed method performed effectively at detecting and repairing data quality problems for seafloor observatory data. View Full-Text
Keywords: seafloor observatory; data quality control; ARIMA; outlier detection; data interpolation seafloor observatory; data quality control; ARIMA; outlier detection; data interpolation
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Zhou, Y.; Qin, R.; Xu, H.; Sadiq, S.; Yu, Y. A Data Quality Control Method for Seafloor Observatories: The Application of Observed Time Series Data in the East China Sea. Sensors 2018, 18, 2628.

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