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. 2017 Dec 4;17(12):2806.
doi: 10.3390/s17122806.

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

Yingchi Mao  1 , Hai Qi  2 , Ping Ping  3 , Xiaofang Li  4
Affiliations

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

Yingchi Mao et al. Sensors (Basel). .

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.

Keywords: back propagation model; connected dominating set; event detection; multivariate water quality parameters; spatial-temporal model; time-series data; water supply network.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The topology of a water supply network.
Figure 2
Figure 2
Procedure in the off-line phase.
Figure 3
Figure 3
Procedure in the on-line phase.
Figure 4
Figure 4
BP (Back Propagation) neural network structure of free chlorine.
Figure 5
Figure 5
Event probability of single water quality parameter. (a) free Chlorine; (b) EC; (c) pH; (d) Temperature; (e) TOC; (f) turbidity.
Figure 6
Figure 6
Event probability of multiple water quality parameters.
Figure 7
Figure 7
State transitions of the fused values of six water quality parameters at the moment τ1
Figure 8
Figure 8
Time series of multivariate water quality parameters. (a) Free Chlorine; (b) EC; (c) pH; (d) Temperature; (e) TOC; (f) turbidity.
Figure 8
Figure 8
Time series of multivariate water quality parameters. (a) Free Chlorine; (b) EC; (c) pH; (d) Temperature; (e) TOC; (f) turbidity.
Figure 9
Figure 9
Comparison of detection rates between M-STED and S-STED.
Figure 10
Figure 10
Comparison of false alarm rates between M-STED and S-STED.
Figure 11
Figure 11
Comparison of ROC (receiver operating curve) between M-STED and S-STED.
Figure 12
Figure 12
The average delay time in the different node densities.
Figure 13
Figure 13
Scalability with an increased number of nodes.
See this image and copyright information in PMC

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