Pollution source intelligent location algorithm in water quality sensor networks

Yan, Xuesong; Gong, Jingyu; Wu, Q. H.

doi:10.1007/s00521-020-05000-8

Cited by 34 publications

(14 citation statements)

References 52 publications

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“…After analyzing measurement series from a real sewage network, we concluded that an exponential function could be a good approximation of a real signal. Similar approach was used in Yan et al [ 22 ]. We provided an exponential signal generator that use function

defined in Equation ( 18 ).…”

Section: Resultsmentioning

confidence: 99%

“…The algorithm represents the state of the sewage network for a given time point and produces the most probable events. Such assumption eliminates the possibility of analyzing the time series from sensors offline (using historical data) and then finding the events using optimization algorithms, e.g., by simulating many times the network states and comparing the simulation results with measured values ([ 22 ]).…”

Section: Resultsmentioning

confidence: 99%

“…The reasons for the difficulty of this task come from the randomness of the injection process involving the type of pollutants, the injection point, the injection amount, time, duration, etc. [ 22 ] Moreover, some physical properties of the wastewater are less predictable or more difficult to measure precisely than others.…”

Section: Introductionmentioning

confidence: 99%

“…In 2021, Yan et al [ 22 ] proposed an improved genetic algorithm for the pollution source localization in water supply networks. They formulated assumptions about the behavior of pollutants in water, which are also used in this article.…”

Section: Introductionmentioning

confidence: 99%

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Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Chachuła

Słojewski

Nowak

2022

Sensors

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Illegal discharges of pollutants into sewage networks are a growing problem in large European cities. Such events often require restarting wastewater treatment plants, which cost up to a hundred thousand Euros. A system for localization and quantification of pollutants in utility networks could discourage such behavior and indicate a culprit if it happens. We propose an enhanced algorithm for multisensor data fusion for the detection, localization, and quantification of pollutants in wastewater networks. The algorithm processes data from multiple heterogeneous sensors in real-time, producing current estimates of network state and alarms if one or many sensors detect pollutants. Our algorithm models the network as a directed acyclic graph, uses adaptive peak detection, estimates the amount of specific compounds, and tracks the pollutant using a Kalman filter. We performed numerical experiments for several real and artificial sewage networks, and measured the quality of discharge event reconstruction. We report the correctness and performance of our system. We also propose a method to assess the importance of specific sensor locations. The experiments show that the algorithm’s success rate is equal to sensor coverage of the network. Moreover, the median distance between nodes pointed out by the fusion algorithm and nodes where the discharge was introduced equals zero when more than half of the network nodes contain sensors. The system can process around 5000 measurements per second, using 1 MiB of memory per 4600 measurements plus a constant of 97 MiB, and it can process 20 tracks per second, using 1.3 MiB of memory per 100 tracks.

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defined in Equation ( 18 ).…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Chachuła

Słojewski

Nowak

2022

Sensors

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“…Simulation-optimization with added hydraulic demand uncertainty and GA has also been investigated [ 19 ]. Recently, a Poisson model for a changing water demand was coupled with an improved GA [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Grbčić

Kranjčević

Družeta

2021

Sensors

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This paper presents and explores a novel methodology for solving the problem of a water distribution network contamination event, which includes determining the exact source of contamination, the contamination start and end times and the injected contaminant concentration. The methodology is based on coupling a machine learning algorithm for predicting the most probable contamination sources in a water distribution network with an optimization algorithm for determining the values of contamination start time, end time and injected contaminant concentration for each predicted node separately. Two slightly different algorithmic frameworks were constructed which are based on the mentioned methodology. Both algorithmic frameworks utilize the Random Forest algorithm for classification of top source contamination node candidates, with one of the frameworks directly using the stochastic fireworks optimization algorithm to determine the contamination start time, end time and injected contaminant concentration for each predicted node separately. The second framework uses the Random Forest algorithm for an additional regression prediction of each top node’s start time, end time and contaminant concentration and is then coupled with the deterministic global search optimization algorithm MADS. Both a small sized (92 potential sources) network with perfect sensor measurements and a medium sized (865 potential sources) benchmark network with fuzzy sensor measurements were used to explore the proposed frameworks. Both algorithmic frameworks perform well and show robustness in determining the true source node, start and end times and contaminant concentration, with the second framework being extremely efficient on the fuzzy sensor measurement benchmark network.

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Dynamic Grouping Design System of Leisure Sports Based on Computer Intelligent Algorithm

Zhang

2022

Lecture Notes on Data Engineering and Communications Technologies

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Pollution source intelligent location algorithm in water quality sensor networks

Cited by 34 publications

References 52 publications

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Dynamic Grouping Design System of Leisure Sports Based on Computer Intelligent Algorithm

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