Identification of Groundwater Contamination Sources Using Meshfree RPCM Simulation and Particle Swarm Optimization

Guneshwor, L.; Eldho, T. I.; Kumar, Avinash

doi:10.1007/s11269-017-1885-1

Cited by 53 publications

(14 citation statements)

References 32 publications

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“…A recent overview of the developments of PSO is given by [ 62 ]. PSO was used in the work by [ 63 ] for identifying the unknown groundwater contaminant sources as a part of the simulation-optimization procedure. In Ma et al [ 64 ] PSO was used for gas emission source identification and compared with the firefly algorithm and Ant Colony Optimization algorithm.…”

Section: Materials and Methodsmentioning

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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Section: Materials and Methodsmentioning

confidence: 99%

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

Grbčić

Kranjčević

Družeta

2021

Sensors

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“…The optimal position that the i-th particle is currently experiencing, that is, the individual extremum is represented by p i = p i1 , p i2 , ⋯ , p id ⋯ , p iD . The best position that a particle swarm currently experiences is represented by p g = p g1 , p g2 , ⋯ , p gd ⋯ , p gD , also known as the global extremum [13].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Particle swarm optimization in image processing of power flow learning distribution

Huang

2021

Discov Internet Things

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In order to realize the coordination and integration optimization of the power system itself, this paper constructed the mathematical model of the hybrid power system and solved the multi-objective optimization problem of the heating system through the optimized particle swarm optimization algorithm. Based on the back-to-back VSC-HVDC grid-connected composite system, this paper studied the integrated control strategy of the device to achieve the simultaneous parallel and tie line currents. At the same time, this paper simplified and improved the proposed disassembly criteria for grid-connected composite devices and integrated them into the grid-connected composite device. In addition, on this basis, the integrated control of the three functions of de-listing, juxtaposition and tie line power adjustment of the same device was further studied. Simulation studies show that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.

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“…The PSO, which is a swarm intelligence method, originated from the study of the ocking behavior of birds simulating their behavior of ying and foraging in groups. Guneshwor et al (2018) proposed a simulation-optimization model by using a radial point collocation method and the PSO to identify the unknown groundwater contaminant sources. In this method, due to the lack of dynamic adjustment of particle velocity, it is easy to fall into local optima.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Multipopulation Evolutionary Algorithm for Contamination Source Identification in Water Distribution Systems

Yang

Zhou

et al. 2021

J. Water Resour. Plann. Manage.

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Real-time monitoring of drinking water in a water distribution system (WDS) can eectively warn and reduce safety risks. One of the challenges is to identify the contamination source through these observed data due to the real-time, non-uniqueness, and large scale characteristics. To address the real-time and non-uniqueness challenges, we propose an adaptive multi-population evolutionary optimization algorithm to determine the real-time characteristics of contamination sources, where each population aims to locate and track a dierent global optimum. The algorithm adaptively adjusts the number of populations using a feedback learning mechanism. To eectively locate an optimal solution for a population, a co-evolutionary strategy is used to identify the location and the injection prole separately. Experimental results on three WDS networks show that the proposed algorithm is competitive in comparison with three other state-of-the-art evolutionary algorithms.

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Identification of Groundwater Contamination Sources Using Meshfree RPCM Simulation and Particle Swarm Optimization

Cited by 53 publications

References 32 publications

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

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

Particle swarm optimization in image processing of power flow learning distribution

Adaptive Multipopulation Evolutionary Algorithm for Contamination Source Identification in Water Distribution Systems

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