Testing Contamination Source Identification Methods for Water Distribution Networks

Seth, Arpan; Klise, Katherine A.; Siirola, John Daniel; Haxton, Terranna; Laird, Carl D.

doi:10.1061/(asce)wr.1943-5452.0000619

Cited by 37 publications

(25 citation statements)

References 32 publications

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“…However, a single injection is assumed, and it is noted that multiple contaminant sources should be considered in future work where the likelihood evaluation needs to be adjusted. Seth et al [12] investigated the efficiency of three different methods for source detection; Bayesian probability-based method, backtracking method (using contaminant status algorithm), and optimizationbased method where accuracy in case of multiple injection locations was investigated for two and three contamination injection locations. It was noted that the Bayesian method is designed only for a single contamination location while the contaminant status algorithm used in De Sanctis et al [13] provides a list of possible solutions that narrow down search space for the optimization method; however, it also does not identify the possible number of injection locations.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network

Lučin

Grbčić

Čarija

et al. 2021

Sensors

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In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations.

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Section: Introductionmentioning

confidence: 99%

Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network

Lučin

Grbčić

Čarija

et al. 2021

Sensors

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“…In agriculture studies, WSN has been used for monitoring environmental conditions; scheduling irrigation based on real-time network data; controlling environmental conditions and parameters to improve cropping processes, and improving production quantity and quality [ 23 , 24 ]. A water quality simulation program using WSN data, however, can also be a useful tool to effectively predict point source pollution in irrigation channels since the location, time, and source (i.e., source tracing) of water quality contamination can be determined using sensor data [ 6 , 25 , 26 ]. Furthermore, a water quality sensor network with WSN can collect application-oriented data, and provide real-time information for agricultural and environmental monitoring [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework

Lin

Mukhtar

Huang

et al. 2020

Sensors

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Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a “backward pollution source tracing” (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.

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“…Most widely used simulation software is EPANET developed by the Environmental Protection Agency [1] which provides quick results of hydraulic and water-quality analysis of complex networks. EPANET can be used in investigation of algorithms and methods for optimal sensor placement [2,3], contaminant source detection [4][5][6] or contaminant characterization [7].…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Investigation of Mixing Phenomena in Double-Tee Junctions

Grbčić

Kranjčević

Lučin

et al. 2019

Water

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This work investigates mixing phenomena in a pressurized pipe system with two sequential Tee junctions and experiments are conducted for a range of different inlet flow ratios, varying distances between Tee junctions and two pipe branching configurations. Additionally, obtained experimental results are compared with results from previous studies by different authors and are used to validate the numerical model using the open source computational fluid dynamics toolbox OpenFOAM. Two different numerical approaches are used—Passive scalar model and Multiphase model. It is found that both numerical models produce similar results and that they are both greatly dependent on the turbulent Schmidt number. After the calibration procedure, both models provided good results for all investigated flow ratios, double-Tee junction distances, and pipe branching configurations, therefore both numerical models can be applied for a wide range of pipe networks configurations, but passive scalar model is the viable choice due to its much higher computational efficiency. Obtained results also describe the relationship between the double-Tee distances and complete mixing occurrence.

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Testing Contamination Source Identification Methods for Water Distribution Networks

Cited by 37 publications

References 32 publications

Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network

Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework

Experimental and Numerical Investigation of Mixing Phenomena in Double-Tee Junctions

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