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Sensors 2017, 17(12), 2806; doi:10.3390/s17122806

Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring

1
College of Computer and Information, Hohai University, Nanjing 211100, China
2
School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou 213032, China
This paper is an extended version of an earlier conference paper: 21st International Conference on Database Systems for Advanced Applications (DASFAA 2016), Dallas, TX, USA, 16–19 April 2016.
*
Author to whom correspondence should be addressed.
Received: 17 September 2017 / Revised: 24 November 2017 / Accepted: 30 November 2017 / Published: 4 December 2017
(This article belongs to the Special Issue Sensors in Agriculture)
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Abstract

Time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the agricultural water supply network. The accurate and efficient detection and warning of contamination events to prevent pollution from spreading is one of the most important issues when pollution occurs. In order to comprehensively reduce the event detection deviation, a spatial–temporal-based event detection approach with multivariate time-series data for water quality monitoring (M-STED) was proposed. The M-STED approach includes three parts. The first part is that M-STED adopts a Rule K algorithm to select backbone nodes as the nodes in the CDS, and forward the sensed data of multiple water parameters. The second part is to determine the state of each backbone node with back propagation neural network models and the sequential Bayesian analysis in the current timestamp. The third part is to establish a spatial model with Bayesian networks to estimate the state of the backbones in the next timestamp and trace the “outlier” node to its neighborhoods to detect a contamination event. The experimental results indicate that the average detection rate is more than 80% with M-STED and the false detection rate is lower than 9%, respectively. The M-STED approach can improve the rate of detection by about 40% and reduce the false alarm rate by about 45%, compared with the event detection with a single water parameter algorithm, S-STED. Moreover, the proposed M-STED can exhibit better performance in terms of detection delay and scalability. View Full-Text
Keywords: event detection; back propagation model; multivariate water quality parameters; time-series data; spatial-temporal model; connected dominating set; water supply network event detection; back propagation model; multivariate water quality parameters; time-series data; spatial-temporal model; connected dominating set; water supply network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mao, Y.; Qi, H.; Ping, P.; Li, X. Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring. Sensors 2017, 17, 2806.

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