Event Detection with Multivariate Water Parameters in the Water Monitoring Applications

Abstract

The real-time time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the 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 the pollution occurs. In order to comprehensively reduce the event detection deviation, a Temporal Abnormal Event Detection Algorithm for Multivariate time series data (M-TAEDA) was proposed. In M-TAEDA, first, Back Propagation neural network models are adopted to analyze the time series data of multiple water quality parameters and calculate the possible outliers. Then, M-TAEDA algorithm determines the potential contamination events through Bayesian sequential analysis to estimate the probability of a contamination event. Finally, it can make decision based on the multiple event probabilities fusion in the water supply system. The experimental results indicate that the proposed M-TAEDA algorithm can obtain the 90% accuracy with BP neural network model and improve the rate of detection about 40% and reduce the false alarm rate about 45%, compared with the temporal event detection of Single Variate Temporal Abnormal Event Detection Algorithm (S-TAEDA).