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

Kadowaki, Masanao   ; Nagai, Haruyasu  ; Yoshida, Toshiya*; Terada, Hiroaki   ; Tsuzuki, Katsunori  ; Sawa, Hiroki*

This study develops an estimation method using machine learning for uncertainty in forecasted plume directions. Bayesian machine learning was used in the machine learning approach. A three-day forecast simulation was conducted every day from 2015-2020, considering a hypothetical release of Cs 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 estimation. Additionally, 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 used in this study effectively estimates the uncertainty of plume directions predicted through atmospheric dispersion simulations.