@Article{Li2019Computational,
author="Li, Hui
and Zhang, Jianwen
and Yi, Junkai",
title="Computational source term estimation of the Gaussian puff dispersion",
journal="Soft Computing",
year="2019",
month="Jan",
day="01",
volume="23",
number="1",
pages="59--75",
abstract="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.",
issn="1433-7479",
doi="10.1007/s00500-018-3440-2",
url="https://doi.org/10.1007/s00500-018-3440-2"
}