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Identification of source information for sudden hazardous chemical leakage accidents in surface water on the basis of particle swarm optimisation, differential evolution and Metropolis-Hastings sampling
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering. School of Economics and Management, Fuzhou University, Fuzhou, 350116, China.ORCID iD: 0000-0002-6734-249X
School of Economics and Management, Fuzhou University, Fuzhou, 350116, China.
Luleå University of Technology, Department of Social Sciences, Technology and Arts, Business Administration and Industrial Engineering. School of Economics and Management, Fuzhou University, Fuzhou, 350116, China.ORCID iD: 0000-0003-4637-0376
School of Economics and Management, Fuzhou University, Fuzhou, 350116, China.
2021 (English)In: Environmental Science and Pollution Research, ISSN 0944-1344, E-ISSN 1614-7499, Vol. 28, no 47, p. 67292-67309Article in journal (Refereed) Published
Abstract [en]

A quick and accurate identification of source information on sudden hazardous chemical leakage accident is crucial for early accident warning and emergency response. This study firstly regards source identification problem of sudden hazardous chemical leakage accidents as an inverse problem and presents a source identification model based on the Bayesian framework. Secondly, a new identification method is designed on the basis of particle swarm optimisation (PSO), differential evolution (DE) and the Metropolis–Hastings (M–H) sampling method. Lastly, the designed method, i.e. PSO-DE-MH, is verified by an outdoor experiment analyses in a section of the South–North Water Transfer Project. Results show that the number of iterations, the average absolute error, the average relative error and the average standard deviations of the identification results obtained by PSO-DE-MH are less than those of PSO-DE and DE-MH. Moreover, the relative error and the sampling relative error of the identification results under five different measurement errors (MEs) (σ = 0.01, 0.05, 0.1, 0.15, 0.2) are less than 9.5% and 0.2%, respectively. The designed method is effective even when the standard deviation of the ME increases to 0.2. Therefore, the designed method can effectively and accurately obtain the source information of sudden hazardous chemical leakage accidents. This study provides a new idea and method to solve the difficult problems of emergency management.

Place, publisher, year, edition, pages
Springer Nature, 2021. Vol. 28, no 47, p. 67292-67309
Keywords [en]
Emergency identification, Bayesian inference, Particle swarm optimisation, Differential evolutionary, Sudden hazardous chemical leakage accidents
National Category
Control Engineering
Research subject
Quality technology and logistics
Identifiers
URN: urn:nbn:se:ltu:diva-86496DOI: 10.1007/s11356-021-15132-6ISI: 000671651300010PubMedID: 34247354Scopus ID: 2-s2.0-85110406081OAI: oai:DiVA.org:ltu-86496DiVA, id: diva2:1582289
Note

Validerad;2021;Nivå 2;2021-12-03 (johcin);

Funder: National Social Science Fund of China (17BGL179)

Available from: 2021-07-30 Created: 2021-07-30 Last updated: 2023-09-04Bibliographically approved

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Yang, HaidongLiu, Biyu

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