Collaborative based pollution sources identification algorithm in water supply sensor networks

Gong, Jinyu; Yan, Xuesong; Hu, Chengyu

doi:10.5004/dwt.2019.24204

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…The experimental results prove the algorithm's effectiveness for water pollution's traceability. Yan et al [16][17][18] proposed an intelligent optimization method based on expensive optimization methods to solve water pollution's intelligent traceability in the large-scale pipe network nodes. The results prove the effectiveness of the large-scale pipe networks.…”

Section: Introductionmentioning

confidence: 99%

An intelligent traceability method of water pollution based on dynamic multi-mode optimization

Yan

2022

Neural Comput & Applic

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Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in real time to determine the pollution source’s characteristic information and provide technical support to emergency management departments for decision making. The problems of water pollution’s real-time traceability are as follows: non-uniqueness and dynamic real time of pollution sources. Aiming at these two difficulties, an intelligent traceability algorithm based on dynamic multi-mode optimization was designed and proposed in the work. As a multi-mode optimization problem, pollution traceability could have multiple similar optimal solutions. Firstly, the new algorithm divided the population reasonably through the optimal subpopulation division strategy, which made the nodes’ distribution in a single subpopulation more similar and conducive to local optimization. Then, a similar peak penalty strategy was used to eliminate similar solutions and reduce the non-unique solutions’ number, since real-time traceability required higher algorithm convergence than traditional offline traceability and dynamic problems with parameter changes, historical information preservation, and adaptive initialization strategies could make reasonable use of the algorithm’s historical knowledge to improve the population space and increase the population convergence rate when the problem changed. The experimental results showed the proposed new algorithm’s effectiveness in solving problems—accurately tracing the source of pollution, and obtain corresponding characteristic information in a short time.

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Section: Introductionmentioning

confidence: 99%

An intelligent traceability method of water pollution based on dynamic multi-mode optimization

Yan

2022

Neural Comput & Applic

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“…A Bayesian approach was investigated for the contamination source localization in a water distribution network with stochastic demands [ 28 ], and recently, reference [ 29 ] constructed a Bayesian framework for the same application of contamination source localization but with mobile sensor data. Additionally, a Gaussian surrogate model was implemented with a collaborative based algorithm [ 30 ] specifically for the contamination source identification problem.…”

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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Pollution source intelligent location algorithm in water quality sensor networks

Yan

Gong

2020

Neural Comput & Applic

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Collaborative based pollution sources identification algorithm in water supply sensor networks

Cited by 8 publications

References 38 publications

An intelligent traceability method of water pollution based on dynamic multi-mode optimization

An intelligent traceability method of water pollution based on dynamic multi-mode optimization

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

Pollution source intelligent location algorithm in water quality sensor networks

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