Abstract:The real-time time-series data of multiple water parameters are acquired via the water sensor networks deployed in the water supply network. The accurate and efficient detection and warning of pollution events to prevent pollution from spreading is one of the most important issues when the pollution occurs. In order to comprehensively evaluate the abnormal event detection to reduce the detection deviation, a Temproal Abnormal Event Detection Algorithm for Multivariate time series data (M-TAEDA) was proposed. In M-TAEDA, it could analyze the time-series data of multiple parameters with BP (Back Propagation) model to determine the possible outliers, respectively. M-TAEDA algorithm could detect the potential pollution events through Bayesian sequential analysis to estimate the probability of an abnormal event. Finally, it can make decision through the multiple event probability fusion in the water supply systems. The experimental results indicate that the proposed M-TAEDA algorithm can get the 90% accuracy with BP model and improve the rate of detection about 40% and reduce the false alarm rate about 45% compared with the temporal abnormal event detection of Single-Variate Temproal Abnormal Event Detection Algorithm (S-TAEDA).
毛莺池, 齐海, 接青, 王龙宝. M-TAEDA:多变量水质参数时序数据异常事件检测算法[J]. 计算机应用, 2017, 37(1): 138-144.
MAO Yingchi, QI Hai, JIE Qing, WANG Longbao. M-TAEDA: temporal abnormal event detection algorithm for multivariate time-series data of water quality. Journal of Computer Applications, 2017, 37(1): 138-144.
[1] HALL J, HERRMANN J G. On-line water quality parameters as indicators of distribution system contamination[J]. Journal American Water Works Association, 2007, 99(1):66-77. [2] HUANG T, MA X, JI X, et al. Online detecting spreading events with the spatio-temporal relationship in water distribution networks[M]//Advanced Data Mining and Applications. Berlin:Springer, 2013:145-156. [3] STOTEY M V, GAAG B V D, BURNS B P. Advances in on-line drinking water quality monitoring and early warning systems[J]. Water Research, 2011, 45(2):741-747. [4] YIM S J, CHOI Y H. Fault-tolerant event detection using two thresholds in wireless sensor networks[C]//Proceedings of the 15th IEEE Pacific Rim International Symposium on Dependable Computing. Piscataway, NJ:IEEE, 2009:331-335. [5] XUE W, LUO Q, WU H. Pattern-based event detection in sensor networks[J]. Distributed & Parallel Databases, 2012, 30(1):27-62. [6] BYRT D, CARLSON K H. Expanded summary:real-time detection of intentional chemical contamination in the distribution system[J]. Journal American Water Works Association, 2005, 97(7):130-133. [7] WANG X R, LIZIER J T, OBST O, et al. Spatiotemporal anomaly detection in gas monitoring sensor networks[C]//EWSN 2008:Proceedings of the 5th European Conference on Wireless Sensor Networks. Berlin:Springer, 2008:90-105. [8] UUSITAL L. Advantages and challenges of Bayesian networks in environmental modelling[J]. Ecological Modelling, 2014, 203(3/4):312-318. [9] ELIADED G, LAMBROU T P, PANAYIOTOU C G, et al. Contamination event detection in water distribution systems using a model-based approach[J]. Procedia Engineering, 2014, 89:1089-1096. [10] 侯迪波,陈玥,赵海峰,等.基于RBF神经网络和小波分析的水质异常检测方法[J].传感器与微系统,2013,32(2):138-141.(HOU D B, CHEN Y, ZHAO H F, et al. Based on the RBF neural network and wavelet analysis the water quality of anomaly detection method[J]. Transducer and Microsystem Technologies, 2013, 32(2):138-141.) [11] PERELMAN L, OSTFELD A. Bayesian networks for source intrusion detection[J]. Journal of Water Resources Planning and Management, 2012, 139(4):426-432. [12] 孔英会,景美丽.基于混淆矩阵和集成学习的分类方法研究[J].计算机工程与科学,2012,34(6):111-117.(KONG Y H, JING M L. Classification method based on confusion matrix and the integrated learning research[J]. Computer Engineering and Science, 2012, 34(6):111-117.) [13] MURRAY R, HAXTON T, et al Water quality event detection systems for drinking water contamination warning systems:Development testing and application of CANARY[EB/OL].[2016-06-20] . https://cfpub.epa.gov/si/si_public_file_download.cfm?p_download_id=496189. [14] KLISE K A, MCKENNA S A. Multivariate applications for detecting anomalous water quality[C]//Proceedings of the 2006 Symposium on Water Distribution Systems Analysis. Cincinnati, OH:American Society of Civil Engineers, 2011:1-11. [15] MCKENNA S A, WILSON M, KLISE K A. Detecting changes in water quality data[J]. Journal American Water Works Association, 2008, 77(1):74-85.