Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (1): 138-144.DOI: 10.11772/j.issn.1001-9081.2017.01.0138

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

MAO Yingchi, QI Hai, JIE Qing, WANG Longbao   

  1. College of Computer and Information, Hohai University, Nanjing Jiangsu 211100, China
  • Received:2016-08-05 Revised:2016-08-24 Online:2017-01-10 Published:2017-01-09
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (U1301252), the National Science and Technology Support Program (2013BAB06B04), the Technology Project of China Huaneng Group Company Headquarters (HNKJ13-H17-04), the Science and Technology Project of Yunnan Province (2014GA007), the Special Fund for Basic Scientific Research of Central Universities (2015B22214).


毛莺池, 齐海, 接青, 王龙宝   

  1. 河海大学 计算机与信息学院, 南京 211100
  • 通讯作者: 毛莺池
  • 作者简介:毛莺池(1976-),女,上海人,副教授,博士,CCF会员,主要研究方向:分布式计算与并行处理、分布式数据管理;齐海(1994-),男,安徽安庆人,硕士研究生,主要研究方向:分布式计算、并行处理;接青(1989-),女,山东烟台人,硕士研究生,主要研究方向:分布式计算、并行处理、数据管理;王龙宝(1977-),男,江苏盐城人,讲师,主要研究方向:智能数据处理。
  • 基金资助:

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).

Key words: Wireless Sensor Network (WSN), abnormal event detection, Back Propagation (BP) model, multivariate water quality parameter, time-series data

摘要: 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%。

关键词: 无线传感器网络, 异常事件检测, BP模型, 多变量水质参数, 时间序列数据

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