Data-driven hazardous gas dispersion modeling using the integration of particle filtering and error propagation detection

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

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
17 Downloads (Pure)

Abstract

The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method based on data assimilation is proposed by applying particle filter to Gaussian-based dispersion model. The core of the method is continually updating dispersion coefficients by assimilating observed data into the model during the calculation process. Another contribution of this paper is that error propagation detection rules are proposed to evaluate their effects since the measured and computational errors are inevitable. So environmental protection authorities can be informed to what extent the model output is of high confidence. To test the feasibility of our method, a numerical experiment utilizing the SF6 concentration data sampled from an Indianapolis field study is conducted. Results of accuracy analysis and error inspection imply that Gaussian dispersion models based on particle filtering and error propagation detection have better performance than traditional dispersion models in practice though sacrificing some computational efficiency.

Original languageEnglish
Article number1640
Number of pages17
JournalInternational Journal of Environmental Research and Public Health
Volume15
Issue number8
DOIs
Publication statusPublished - 2018

Keywords

  • Atmospheric dispersion
  • Data-driven modeling
  • Error propagation
  • Gaussian dispersion model
  • Particle filter
  • OA-Fund TU Delft

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