Performance evaluation for three pollution detection methods using data from a real contamination accident

Abstract

Early warning systems have been widely deployed to safeguard water security. Many contamination detection methods have been developed and evaluated in the past decades. Although encouraging detection performance has been obtained and reported, these evaluations mainly used artificial or laboratory data. The evaluation of detection performance with data from real contamination accidents has rarely been conducted. Implementation of contamination event methods without full assessment using field data might lead to failure of an early warning system. In this paper, the detection performance of three contamination detection methods, a Pearson correlation Euclidean distance (PE) based detection method, a multivariate Euclidean distance (MED) method and a linear prediction filter (LPF) method, was evaluated using data from a real contamination accident. Results improve understanding of the implementation of detection methods to field situations and show that all methods are prone to yielding worse detection performance when applied to data from a real contamination accident. They also revealed that the Pearson correlation Euclidean distance based method is more capable of differentiating between equipment noise and presence of contamination and has greater potential to be used in real field situations than the MED and LPF methods.

Keywords: Contamination detection; Euclidean distance; Linear prediction filter; Pearson correlation.