Application of Bayesian machine learning for estimation of uncertainty in forecasted plume directions by atmospheric dispersion simulations

Abstract

When assisting emergency responses to a nuclear accident through atmospheric dispersion simulations, it is necessary to provide the prediction results and their uncertainties. This study develops an estimation method using machine learning for uncertainty in forecasted plume directions. The difference in plume directions derived from the meteorological forecast and analysis inputs was considered as the uncertainty in forecasted plume direction. Bayesian machine learning was used to predict the uncertainty based on the accumulated uncertainty estimation result in past cases. A three-day forecast simulation was conducted every day from 2015 to 2020, considering a hypothetical release of ¹³⁷Cs from a nuclear facility to create training and test datasets for the machine learning. The findings reveal that the rate of good predictability was greater than 50% even in the forecast 36 h later when investigating the effectiveness of the Bayesian model on uncertainty prediction. The frequency of miss prediction of higher uncertainty was low (0.9%−7.9%) throughout the forecast period. However, the rate of over-prediction of uncertainty increased with forecast time up to 31.2%, which is acceptable as a conservative estimation. These results show that the Bayesian model in this study effectively estimates the uncertainty of plume directions predicted through atmospheric dispersion simulations. GRAPHICAL ABSTRACT