Version 1
: Received: 28 July 2017 / Approved: 31 July 2017 / Online: 31 July 2017 (11:02:27 CEST)
How to cite:
Wang, R.; Chen, B.; Qiu, S.; Zhu, Z.; Qiu, X. Data Assimilation in the Air Contaminant Dispersion Using Particle Filter and Expectation-Maximization Algorithm with UAV Observations. Preprints2017, 2017070089. https://doi.org/10.20944/preprints201707.0089.v1
Wang, R.; Chen, B.; Qiu, S.; Zhu, Z.; Qiu, X. Data Assimilation in the Air Contaminant Dispersion Using Particle Filter and Expectation-Maximization Algorithm with UAV Observations. Preprints 2017, 2017070089. https://doi.org/10.20944/preprints201707.0089.v1
Wang, R.; Chen, B.; Qiu, S.; Zhu, Z.; Qiu, X. Data Assimilation in the Air Contaminant Dispersion Using Particle Filter and Expectation-Maximization Algorithm with UAV Observations. Preprints2017, 2017070089. https://doi.org/10.20944/preprints201707.0089.v1
APA Style
Wang, R., Chen, B., Qiu, S., Zhu, Z., & Qiu, X. (2017). Data Assimilation in the Air Contaminant Dispersion Using Particle Filter and Expectation-Maximization Algorithm with UAV Observations. Preprints. https://doi.org/10.20944/preprints201707.0089.v1
Chicago/Turabian Style
Wang, R., Zhengqiu Zhu and Xiaogang Qiu. 2017 "Data Assimilation in the Air Contaminant Dispersion Using Particle Filter and Expectation-Maximization Algorithm with UAV Observations" Preprints. https://doi.org/10.20944/preprints201707.0089.v1
Abstract
The precise prediction of air contaminant dispersion is essential to the air quality monitoring and the emergency management of the contaminant gases leakage incidents in the chemical industry park. The conventional atmospheric dispersion models can seldom give precise prediction due to inaccurate input parameters. In order to improve the prediction accuracy of dispersion model, two data assimilation methods (i.e. one is merely based on the typical particle filter while the other is a combination of particle filter and expectation-maximization algorithm) are proposed to assimilate the UAV observations into the atmospheric dispersion model. Two emission cases are taken into consideration, the difference between which is the different dimensions of state variables. To test the performances of the proposed methods, experiments corresponding to the two emission cases are designed and implemented. The results show that the particle filter can effectively estimate the model parameters and improve the accuracy of model prediction when the dimension of state variables is low. In contrast, when the dimension of state variables becomes higher, the method of particle filter combining expectation-maximization algorithm performs better in the parameter estimation accuracy and warm-up time. Therefore, the data assimilation methods are able to effectively support the air quality monitoring and emergency management in chemical industry parks.
Keywords
air contaminant dispersion; data assimilation; particle filter; expectation-maximization algorithm; UAV
Subject
Engineering, Chemical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
