Radial basis collocation method with parameters optimized for estimating pollutant release history

Lei, Fei; Ou, Jiahao; Wang, Xueli; Zhu, Hengyu

doi:10.1007/s11356-021-17144-8

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…Han et al (2020) proposed a method that couples the dispersion equation of groundwater pollutants with a genetic algorithm (GA) for identifying groundwater pollution sources. Lei et al (2022) established a new model combining the radial configuration in time (RBCN) method coupled with the differential evolutionary algorithm (DE) for the solution of pollutant source terms and achieved a better solution accuracy. The use of optimization algorithm to solve the pollutant source term can minimize the error of the model simulation and provide a reliable reference for pollution control and treatment of water bodies.…”

Section: Introductionmentioning

confidence: 99%

The traceability of sudden water pollution in river canals based on the pollutant diffusion quantification formula

Lin

Ren

Tao

et al. 2023

Front. Environ. Sci.

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For the problem that the traceability parameters of sudden water pollution are difficult to determine, a fast traceability model based on a simplified mechanistic model coupled with an optimization algorithm is proposed to improve the accuracy of sudden water pollution traceability. In this paper, according to the diffusion law of pollutants, a quantitative formula of pollutant diffusion is proposed, and the differential calculation process of the pollutant convection equation is optimized. The Dynamic Programming and Beetle Antennae Search algorithm (DP-BAS) with dynamic step size is used in the reverse optimization process, which can avoid the problem of entering the local optimal solution in the calculation process. The DP-BAS is used to inverse solve the quantization equation to realize the decoupling of pollutant traceability parameters, transforming the multi-parameter coupled solution into a single-parameter solution, reducing the solution dimension, and optimizing the difficulty and solution complexity of pollutant traceability. The proposed traceability model is applied to the simulation case, the results show that the mean square errors of pollutant placement mass, location, and time are 2.39, 1.16, and 1.19 percent, respectively. To further verify the model reliability, the Differential Evolution and Markov Chain Monte Carlo simulation method (DE-MCMC) as well as Genetic Algorithms (GA) were introduced to compare with the proposed model to prove that the model has certain reliability and accuracy.

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Section: Introductionmentioning

confidence: 99%