Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation

The monitoring of a radiation situation around a nuclear power plant is a demanding task due to the high uncertainty of all involved variables and limited availability of measurements from a sparse monitoring network. Assessment of the situation requires experienced specialists who may be unavailable during critical times. Our goal is to provide an automated method of instant radiation situation assessment that does not underestimate its uncertainty. We propose a state-space model based on an atmospheric dispersion model, local correction of a numerical weather model, and a temporal model of the released activity. This state-space model is highly nonlinear and evaluation of the likelihood function requires extensive numerical calculations. The sequential Monte Carlo method is one of the few options for estimating the state recursively. Since the simple bootstrap approach yields an extremely computationally demanding algorithm, we investigate the use of existing techniques for the design of a more efficient proposal density. We propose combining the Laplace approximation and various population Monte Carlo methods. Data from an existing monitoring network were used to calibrate relevant parts of the model. Performance of the methods in a real radiation emergency situation is evaluated in a simulated experiment due to the lack of real data. The proposed tailor-made proposal was found to be much more computationally efficient than previously published methods. The adaptive Monte Carlo methods thus represent a compelling computational approach for the evaluation of probabilistic environmental models. The data used and a Python implementation of the methods are available as supplementary material online.