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

Liu, Shuming; Han, Ce; Smith, Kate; Lei, Musuizi; Li, Ruonan

doi:10.1016/j.jenvman.2015.07.026

Cited by 14 publications

(8 citation statements)

References 15 publications

(33 reference statements)

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“…Their algorithm significantly improves detection performance under multi-index evidence conflicts. Liu et al [6] compared the detection performance of three contamination detection methods, namely, a Pearson correlation Euclidean distance (PE)-based detection method, a multivariate Euclidean distance method, and a linear prediction filter method, using data from an actual contamination accident. The PE method can differentiate between equipment noise and contamination presence better than the two other methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Detection Method for Organic Contamination Events in Water Distribution Systems Using the UV-Vis Spectrum Based on Semi-Supervised Learning

Yin

Wang

et al. 2018

Water

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A method that uses the ultraviolet-visible (UV-Vis) spectrum to detect organic contamination events in water distribution systems exhibits the advantages of rapid detection, low cost, and no need for reagents. The speed, accuracy, and comprehensive analysis of such a method meet the requirements for online water quality monitoring. However, the UV-Vis spectrum is easily disturbed by environmental factors that cause fluctuations of the spectrum and result in false alarms. This study proposes an adaptive method for detecting organic contamination events in water distribution systems that uses the UV-Vis spectrum based on a semi-supervised learning model. This method modifies the baseline using dynamic orthogonal projection correction and adjusts the support vector regression model in real time. Thus, an adaptive online anomaly detection model that maximizes the use of unlabeled data is obtained. Experimental results demonstrate that the proposed method is adaptive to baseline drift and exhibits good performance in detecting organic contamination events in water distribution systems.

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Section: Introductionmentioning

confidence: 99%

Adaptive Detection Method for Organic Contamination Events in Water Distribution Systems Using the UV-Vis Spectrum Based on Semi-Supervised Learning

Yin

Wang

et al. 2018

Water

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“…Most of the reviewed studies in the present work concluded that the methods proposed by authors are capable of detecting certain contamination events [2], [5]- [7], [12], [14]- [18], [20]. However, further research is needed to test these methods for conditions, which could influence the accuracy of the methods.…”

Section: Contamination Detectionmentioning

confidence: 75%

“…Numerous studies have been done to develop and select the most precise event detection algorithm. The accuracy of the event detection method is defined by its ability to place the current measurement of water quality parameters into one of two classes: background -clear and safe water, event -contaminated water [12]. The detection methods during last decades have evolved and expanded from single factor correlation analysis to generic algorithms and artificial neural network analysis.…”

Section: Contamination Detectionmentioning

confidence: 99%

“…FAR is equal to the number of FPs divided by the total number of values that are actually below the detection threshold as in equation (1). The PD is defined as the number of true positives divided by all events that exceed the detection threshold equation (2) [12]. TP represents a true positive detection, TN -true negative.…”

Section: Contamination Detectionmentioning

confidence: 99%

“…However, it should be noted that the observed studies were performed within various conditions, used different data sets, types of contaminants and detection sensors, which should be taken into account for selection of contamination detection approach. However, each of studies analyzed in this paper has been aimed to find the best mathematical approach and compare it to previously used algorithms that mean a reliable data and methods comparison in data analysis done by previous authors [7], [12] III. DISCUSSION 11 algorithms for detection of contamination event of drinking water were compared in this paper.…”

Section: Contamination Detectionmentioning

confidence: 99%

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On-Line Drinking Water Contamination Event Detection Methods

Dejus

Neščerecka

Juhna

2017

ETR

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A task of water supply systems is to provide safe drinking water to every customer, which is a basic human need. Aging of water supply networks and increased precaution of terrorism risks led to re-evaluation of drinking water supply system reliability and vulnerability to accidental and intentional contamination. Contamination of drinking water can cause health, social, psychological and economic issues. During the last decade, early warning systems (EWS) were often used to ensure the safety of drinking water. EWS are driven by conventional sets of drinking water quality sensors, and the collected data are analyzed in real time. For detection of contamination events, numbers of algorithms have been developed. Most of the algorithms are based on statistical analysis or machine learning. The aim of this study was to compare existing methods and to identify the method, which is suitable for contamination detection in drinking water from non-compound specific sensors and requires relatively low computational resource. A detailed review of 11 different algorithms was presented in the current study with the primary focus on detection probability. Cluster analysis in combination with Mahalanobis distances of feature vectors and Canonical correlation analysis (CCA) approach were selected as the most promising methods for application in a new generation of EWS to detect and classify possible contamination events and agents. While canonical correlation analysis method was the most accurate for detection of contamination events, an advantage of Mahalanobis distances was that it not only detects the contamination events but also could identify the type of contaminant. In this study, we conclude that CCA and Mahalanobis distance methods might be applied for detection of contamination events with relatively high and reliable precision.

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Integrating fuzzy logic with Pearson correlation to optimize contaminant detection in water distribution system with uncertainty analyses

Osmani

Banik

Ali

2019

Environ Monit Assess

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

Cited by 14 publications

References 15 publications

Adaptive Detection Method for Organic Contamination Events in Water Distribution Systems Using the UV-Vis Spectrum Based on Semi-Supervised Learning

Adaptive Detection Method for Organic Contamination Events in Water Distribution Systems Using the UV-Vis Spectrum Based on Semi-Supervised Learning

On-Line Drinking Water Contamination Event Detection Methods

Integrating fuzzy logic with Pearson correlation to optimize contaminant detection in water distribution system with uncertainty analyses

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