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Data Assimilation in Air Contaminant Dispersion Using a Particle Filter and Expectation-Maximization Algorithm

机译：空气污染物扩散中数据吸收的粒子滤波和期望最大化算法

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摘要

The accurate prediction of air contaminant dispersion is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in chemical industry parks. Conventional atmospheric dispersion models can seldom give accurate predictions due to inaccurate input parameters. In order to improve the prediction accuracy of dispersion models, two data assimilation methods (i.e., the typical particle filter & the combination of a particle filter and expectation-maximization algorithm) are proposed to assimilate the virtual Unmanned Aerial Vehicle (UAV) observations with measurement error into the atmospheric dispersion model. Two emission cases with different dimensions of state parameters are considered. To test the performances of the proposed methods, two numerical 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 predictions when the dimension of state parameters is relatively low. In contrast, when the dimension of state parameters becomes higher, the method of particle filter combining the expectation-maximization algorithm performs better in terms of the parameter estimation accuracy. Therefore, the proposed data assimilation methods are able to effectively support air quality monitoring and emergency management in chemical industry parks.
机译：空气污染物扩散的准确预测对于空气质量监测和化学工业园区污染物气体泄漏事故的应急管理至关重要。由于输入参数不正确，常规大气扩散模型很少能给出准确的预测。为了提高色散模型的预测精度，提出了两种数据同化方法（即典型的粒子滤波和粒子滤波与期望最大化算法的组合），将虚拟无人机观测与测量同化。误差进入大气扩散模型。考虑了具有不同状态参数维数的两种发射情况。为了测试所提方法的性能，设计并实现了与两种排放情况相对应的两个数值实验。结果表明，当状态参数的维数相对较小时，粒子滤波器可以有效地估计模型参数，提高模型预测的准确性。相反，当状态参数的维数变大时，结合期望最大化算法的粒子滤波方法在参数估计精度方面表现更好。因此，提出的数据同化方法能够有效地支持化学工业园区的空气质量监测和应急管理。

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