Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results

Yang, Yue-Tzu; Haught, Roy C.; Goodrich, James A.

doi:10.1016/j.jenvman.2009.01.021

Cited by 97 publications

(19 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The extension of this method with dynamic hydraulic behavior was discussed by Munavalli and Kumar [97]. The application of the method to a real-life scenario demonstrated that the quantity, quality and location of measurement nodes played an important role in the estimation of parameters.The study by Yang et al [98] defined contaminant chlorine reactions taking place during the transport of water in pipes. The essence was to characterise the hydraulic dispersion of non-reactive chemicals in order to improve the detection of contaminants using water quality sensors and to establish a model for predicting the fate and transport of a slug of a reactive contaminant.…”

Section: Water Quality Modelling Approachmentioning

confidence: 99%

“…The output of the approach revealed up to 75 percent correlation of the spread of all the water parameters considered. The approach by Yang et al [98] explored a real-time event adaptive detection, identification and warning methodology based on the information collected by the conventional water quality sensors. They performed several pilot scale pipe flow experiments with different chemical and biological contaminants at different concentration levels.…”

Section: Other Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Adedoja

Hamam

Khalaf

et al. 2018

Water

View full text Add to dashboard Cite

Protection of the water system is paramount due to the negative consequences of contaminated water on the public health. Water resources are one of the critical infrastructures that must be preserved from deliberate and accidental attacks. Water qualities are examined at the treatment plant. However, its quality can substantially be contaminated during transportation from the plant to the consumers' taps. Contamination in water distribution networks (WDNs) is a danger that can have severe consequences on public health as well as an economic and social instability. Water distribution networks are immensely susceptible to deliberate or accidental attacks due to the complex nature of the system. Hence, contamination source identification (CSI) is a topical issue in water distribution systems that require immediate attention of researchers in order to protect mankind from the adverse effect of consuming contaminated water. Usually, a contaminant event can be detected by the water quality monitoring sensors or the contaminant warning system (CWS) installed on the network. Nevertheless, how to derive the source of the contamination from the collected information is a difficult task that must be tackled in order to evaluate the spread of the contamination and for immediate remedial strategies. In the past two decades, considerable efforts and advancement have been made by researchers applying various techniques in order to locate the source of the contamination in WDNs. Each of the techniques has certain limitations and applicability as reported in the literature. This paper presents a comprehensive review of the existing techniques with emphasis on their importance and technical challenges. Despite a series of investigations in this domain, the field is yet to be unified. Hence, open research areas are still available to explore. Consequently, improvement on the existing techniques is necessary and hereby suggested. More importantly, practical application of these techniques offer a major research gap that must be addressed.

show abstract

Section: Water Quality Modelling Approachmentioning

confidence: 99%

Section: Other Approachesmentioning

confidence: 99%

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Adedoja

Hamam

Khalaf

et al. 2018

Water

View full text Add to dashboard Cite

show abstract

“…These are the U-shaped loss, inverse U-shaped and V-shaped patterns [9]. Inverse U-shaped patterns, such as Gaussian patterns [33], square patterns [6,33], sine wave patterns [6] and other user-defined geometric patterns [2] are commonly used.…”

Section: Geometric Patterns Of Water-contamination Eventsmentioning

confidence: 99%

Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster–Shafer method

Hou

Huang

et al. 2013

Meas. Sci. Technol.

View full text Add to dashboard Cite

This study presents a method for detecting contamination events of sources of drinking water based on the Dempster-Shafer (D-S) evidence theory. The detection method has the purpose of protecting water supply systems against accidental and intentional contamination events. This purpose is achieved by first predicting future water-quality parameters using an autoregressive (AR) model. The AR model predicts future water-quality parameters using recent measurements of these parameters made with automated (on-line) water-quality sensors. Next, a probabilistic method assigns probabilities to the time series of residuals formed by comparing predicted water-quality parameters with threshold values. Finally, the D-S fusion method searches for anomalous probabilities of the residuals and uses the result of that search to determine whether the current water quality is normal (that is, free of pollution) or contaminated. The D-S fusion method is extended and improved in this paper by weighted averaging of water-contamination evidence and by the analysis of the persistence of anomalous probabilities of water-quality parameters. The extended D-S fusion method makes determinations that have a high probability of being correct concerning whether or not a source of drinking water has been contaminated. This paper's method for detecting water-contamination events was tested with water-quality time series from automated (on-line) water quality sensors. In addition, a small-scale, experimental, water-pipe network was tested to detect water-contamination events. The two tests demonstrated that the extended D-S fusion method achieves a low false alarm rate and high probabilities of detecting water contamination events.

show abstract

“…Simultaneously, the direction of the deviation signal vector represents that the characteristics of the agents can be used to classify pollutants further. Yang et al [6] conducted a series of experiments based on a pilot experiment system and reported a new real-time adaptive method for contaminant classification. In this method, four discriminative systems were established according to the response of different conventional indicators caused by 11 different pollutants, which were divided into three categories: fast reaction, slow reaction, and no reaction.…”

Section: Introductionmentioning

confidence: 99%

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors

Huang

Jin

Hou

et al. 2017

Sensors

View full text Add to dashboard Cite

Water quality early warning system is mainly used to detect deliberate or accidental water pollution events in water distribution systems. Identifying the types of pollutants is necessary after detecting the presence of pollutants to provide warning information about pollutant characteristics and emergency solutions. Thus, a real-time contaminant classification methodology, which uses the multi-classification support vector machine (SVM), is proposed in this study to obtain the probability for contaminants belonging to a category. The SVM-based model selected samples with indistinct feature, which were mostly low-concentration samples as the support vectors, thereby reducing the influence of the concentration of contaminants in the building process of a pattern library. The new sample points were classified into corresponding regions after constructing the classification boundaries with the support vector. Experimental results show that the multi-classification SVM-based approach is less affected by the concentration of contaminants when establishing a pattern library compared with the cosine distance classification method. Moreover, the proposed approach avoids making a single decision when classification features are unclear in the initial phase of injecting contaminants.

show abstract

Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: Techniques and experimental results

Cited by 97 publications

References 43 publications

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Towards Development of an Optimization Model to Identify Contamination Source in a Water Distribution Network

Detection of water-quality contamination events based on multi-sensor fusion using an extented Dempster–Shafer method

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors

Contact Info

Product

Resources

About