The hazardous or toxic chemical releases have a detrimental impact on public safety. Estimating source parameters is of particular importance in aiding emergency response and post-assessment. Source term estimation from sensor measurements with a given Gaussian puff dispersion model is a typical inverse problem, which can be transformed into an optimization problem. In this paper, we employed the particle swarm optimization, the Nelder–Mead method, and their hybrid method to solve the optimization problem. Furthermore, we proposed a three-dimensional neighborhood topology which considerably improves performance of the particle swarm optimization. We implemented all these algorithms in JAVA on an embedded system to make a preliminary estimation of the accidental puff release. Numerical experiments with synthetic datasets show that the particle swarm optimization maintains a balance between computation time, accuracy, robustness, and implementation complexity. In contrast, the hybrid algorithm has an advantage in computation time at the expense of more sophisticated implementation.
This is a preview of subscription content, log in to check access.
This work was supported by the National Natural Science Foundation of China under grants of the general technical foundation research joint fund (Project No. U1636208). And this project was also supported by the Ministry of Science and Technology of China under grants of the national key technology R&D program (Project No. 2015BAK39B02).
Compliance with ethical standards
Conflict of interest
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.
Human and animals rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
Abo-Hammour Z (2014) Optimization solution of Troesch’s and Bratu’s problems of ordinary type using novel continuous genetic algorithm. Discrete Dyn Nat Soc 5:703–719MathSciNetGoogle Scholar
Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5:19–28Google Scholar
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 1:1–23Google Scholar
Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435CrossRefGoogle Scholar
Abualigah LM, Khader AT, Hanandeh ES (2018a) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466CrossRefGoogle Scholar
Abualigah LM, Khader AT, Hanandeh ES (2018b) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125CrossRefGoogle Scholar
Abualigah LM, Khader AT, Hanandeh ES (2018c) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 5:1–25Google Scholar
Arqub OA, Abo-Hammour Z (2014) Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm. Inf Sci 279:396–415MathSciNetCrossRefzbMATHGoogle Scholar
Baghmisheh MTV, Peimani M, Sadeghi MH, Ettefagh MM, Tabrizi AF (2012) A hybrid particle swarm Nelder-Mead optimization method for crack detection in cantilever beams. Appl Soft Comput 12(8):2217–2226CrossRefGoogle Scholar
Benarie M (1980) Critical considerations and improvements to the short-time Gaussian plume models. Urban Air Pollution Modelling. Palgrave Macmillan, UK, pp 65–87Google Scholar
Berbekar E, Harms F, Leitl B (2015) Dosage-based parameters for characterization of puff dispersion results. J Hazard Mater 283(3):178–185CrossRefGoogle Scholar
Bhattacharjee Y (2008) Combating terrorism, new efforts to detect explosives require advances on many fronts. Science 5882:1416–1417CrossRefGoogle Scholar
Cao X et al (2011) Dispersion coefficients for Gaussian Puff models. Bound Layer Meteorol 139:487–500CrossRefGoogle Scholar
Cui H et al (2011) A tracer experiment study to evaluate the CALPUFF real time application in a near-field complex terrain setting. Atmos Environ 45(39):7525–7532CrossRefGoogle Scholar
Elperin T, Fominykh A, Krasovitov B (2016) Effect of raindrop size distribution on scavenging of aerosol particles from gaussian air pollution plumes and puffs in turbulent atmosphere. Process Saf Environ Prot 102:303–315CrossRefGoogle Scholar
Gifford FAJ (1961) Use of routine meteorological observations for estimating atmospheric dispersion. Nucl Saf 2:47–51Google Scholar
Greenwood BW, Hudson JM, Bodner AI (1991) Analysis of community risk resulting from rupture of a sour gas pipeline. The analysis, communication, and perception of risk. Adv Risk Anal 9:63–76Google Scholar
Griffiths RF (1994) Errors in the use of the briggs parameterization for atmospheric dispersion coefficients. Atmos Environ 28:2861–2865CrossRefGoogle Scholar
Guha A (1997) A unified eulerian theory of turbulent deposition to smooth and rough surfaces. J Aerosol Sci 28:1517–1537CrossRefGoogle Scholar
Holmes NS, Morawska L (2006) A review of dispersion modelling and its application to the dispersion of particles: an overview of different dispersion models available. Atmos Environ 40:5902–5928CrossRefGoogle Scholar
Hosseini B, Stockie JM (2016) Bayesian estimation of airborne fugitive emissions using a gaussian plume model. Atmos Environ 141:122–138CrossRefGoogle Scholar
Johannesson G, Hanley B, Nitao J (2004) Dynamic Bayesian models via Monte Carlo–an introduction with examples. Int J Therm Sci 43:939–950CrossRefGoogle Scholar
Kameyama K (2009) Particle swarm optimization–a survey. IEICE Trans Inf Syst E 92D(7):1724–1731Google Scholar
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 congress on evolutionary computation, pp 1671–1676Google Scholar
Li H, Zhang J (2017) Fast source term estimation using the PGA-NM hybrid method. Eng Appl Artif Intell 62:68–79CrossRefGoogle Scholar
Li C et al (2017) Soft measurement of wood defects based on LDA feature fusion and compressed sensor images. J For Res 28(6):1274–1281CrossRefGoogle Scholar
Lutman ER, Jones SR, Hill RA, Mcdonald P, Lambers B (2004) Comparison between the predictions of a gaussian plume model and a lagrangian particle dispersion model for annual average calculations of long-range dispersion of radionuclides. J Environ Radioact 75:339–355CrossRefGoogle Scholar
Oettl D, Kukkonen J, Almbauer RA (2001) Evaluation of a gaussian and a lagrangian model against a roadside data set, with emphasis on low wind speed conditions. Atmos Environ 35:2123–2132CrossRefGoogle Scholar
Pullen J et al (2005) A comparison of contaminant plume statistics from a Gaussian puff and urban CFD model for two large cities. Atmos Environ 39:1049–1068CrossRefGoogle Scholar
Stern R, Yamartino RJ (2001) Development and first evaluation of micro-calgrid: a 3-d, urban-canopy-scale photochemical model. Atmos Environ 35:149–165CrossRefGoogle Scholar