TY - JOUR
T1 - Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization
AU - Qiu, Sihang
AU - Chen, Bin
AU - Wang, Rongxiao
AU - Zhu, Zhengqiu
AU - Wang, Yuan
AU - Qiu, Xiaogang
PY - 2018/4/1
Y1 - 2018/4/1
N2 - Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.
AB - Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.
KW - Atmospheric dispersion
KW - Expectation maximization (EM)
KW - Neural network
KW - Particle swarm optimization (PSO)
KW - Source estimation
UR - http://www.scopus.com/inward/record.url?scp=85041462328&partnerID=8YFLogxK
U2 - 10.1016/j.atmosenv.2018.01.056
DO - 10.1016/j.atmosenv.2018.01.056
M3 - Article
AN - SCOPUS:85041462328
SN - 1352-2310
VL - 178
SP - 158
EP - 163
JO - Atmospheric Environment
JF - Atmospheric Environment
ER -