Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization. (April 2018)

• Record Type: Journal Article
• Authors: Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Zhu, Zhengqiu; Wang, Yuan; Qiu, Xiaogang
• Is Part Of: Atmospheric environment. Volume 178(2018)
• Page Start: 158
• Page End: 163
• Publication Date: 2018-04
• Subjects: Atmospheric dispersion -- Source estimation -- Neural network -- Particle swarm optimization (PSO) -- Expectation maximization (EM); Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2018.01.056
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: 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. Highlights: A dispersion prediction method based on artificial neural network is proposed. The method uses particle swarm optimization and expectation maximization to estimate the dispersion source. The method has high accuracy in dispersion prediction and source estimation. The method is verified by a field study.