Sequential Monte Carlo sampler applied to source term estimation in complex atmospheric environments
Abstract
The accurate and rapid reconstruction of a pollution source represents an important but challenging problem. Several strategies have been proposed to tackle this issue among which we find the Bayesian solutions that have the interesting ability to provide a complete characterization of the source parameters through their posterior probability density function. However, these existing techniques have certain limitations such as their computational complexity, the required model assumptions, their difficulty to converge, the sensitive choice of model/algorithm parameters which clearly limit their easy use in practical scenarios. In this paper, to overcome these limitations, we propose a novel Bayesian solution based on a general and flexible population-based Monte Carlo algorithm, namely the sequential Monte Carlo sampler. Owing to its full adaptivity through the learning process, the main advantage of such an algorithm lies in its capability to be used without requiring any specific assumptions on the underlying statistical model and also without requiring from the user any difficult choices of certain parameter values. The performance of the proposed inference strategy is assessed using twin experiments in complex built-up environments.
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