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.
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