Why conventional detection methods fail in identifying the existence of contamination events. (15th April 2016)

• Record Type: Journal Article
• Authors: Liu, Shuming; Li, Ruonan; Smith, Kate; Che, Han
• Is Part Of: Water research. Volume 93(2016)
• Page Start: 222
• Page End: 229
• Publication Date: 2016-04-15
• Subjects: Early warning system -- Contamination detection -- Water security -- Pearson correlation; COD chemical oxygen demand -- DO dissolved oxygen -- EWS early warning system -- FAR false alarm rate -- FN false negative -- FP false positive -- LPF linear prediction filters -- MED multivariate Euclidean distance -- ORP oxidation reduction potential -- PE Pearson correlation Euclidean distance based method -- PD probability of detection -- ROC received operating characteristic -- TN true negative -- TOC total organic carbon -- TP true positive; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2016.02.027
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading. Graphical abstract: Highlights: The widely used contamination data construction approach is misleading. Conventional methods are more suitable for detecting sudden spike-like variation. MED and LPF methods failed to detect block-like variation in real events. PE method performed better than MED and LPF methods when applied to real event data.