M-TAEDA:多变量水质参数时序数据异常事件检测算法

M-TAEDA:temporal abnormal event detection algorithm for multivariate time-series data of water quality

在供水管网中部署传感器网络实时获取多个水质参数时间序列数据,当供水管网发生污染时,高效准确地检测水质异常是一个重要问题。提出多变量水质参数时间异常事件检测算法(M-TAEDA),利用BP模型分析多变量水质参数的时序数据,确定可能离群点;结合贝叶斯序贯分析独立更新每个参数的事件概率,预测单个传感器节点检测的异常概率;将单变量的事件概率融合为统一多变量事件概率,融合判断异常事件。实验结果表明:BP模型模拟多变量水质参数进行预测可以达到90%精确度;与单变量参数时间异常事件检测算法(S-TAEDA)相比,M-TAEDA可以提高异常检出率约40%,降低误报率约45%。

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 probabihty 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）.