Print Email Facebook Twitter Data-driven hazardous gas dispersion modeling using the integration of particle filtering and error propagation detection Title Data-driven hazardous gas dispersion modeling using the integration of particle filtering and error propagation detection Author Zhu, Zhengqiu (National University of Defense Technology) Qiu, S. (TU Delft Web Information Systems; National University of Defense Technology) Chen, Bin (National University of Defense Technology) Wang, Rongxiao (National University of Defense Technology) Qiu, Xiaogang (National University of Defense Technology) Date 2018 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. Subject Atmospheric dispersionData-driven modelingError propagationGaussian dispersion modelParticle filterOA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:c151739e-716f-40e4-b1c7-66039ac7d643 DOI https://doi.org/10.3390/ijerph15081640 ISSN 1661-7827 Source International Journal of Environmental Research and Public Health, 15 (8) Part of collection Institutional Repository Document type journal article Rights © 2018 Zhengqiu Zhu, S. Qiu, Bin Chen, Rongxiao Wang, Xiaogang Qiu Files PDF ijerph_15_01640.pdf 1.83 MB Close viewer /islandora/object/uuid:c151739e-716f-40e4-b1c7-66039ac7d643/datastream/OBJ/view