Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization

Sihang Qiu, Bin Chen, Rongxiao Wang, Zhengqiu Zhu, Yuan Wang, Xiaogang Qiu

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)158-163
Number of pages6
JournalAtmospheric Environment
Volume178
DOIs
Publication statusPublished - 1 Apr 2018

Keywords

  • Atmospheric dispersion
  • Expectation maximization (EM)
  • Neural network
  • Particle swarm optimization (PSO)
  • Source estimation

Fingerprint Dive into the research topics of 'Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization'. Together they form a unique fingerprint.

  • Cite this