Quantification of parameter uncertainty in re-flooding model based on random forest algorithm

In order to assess the uncertainty of physical models (inputs) of complex accidental phenomena, an inverse uncertainty quantification method based on the Random Forest algorithm combined with the PSO-Kriging proxy model and the KDE-SJ nonparametric statistics is proposed and applied to the model assessment of the re-inundation phenomenon of large breach accidents. The probability density distributions of the model parameters were obtained through the degree of consistency between the calculation results (output) of the system program and the FEBA experimental data as a classification criterion for the Random Forest algorithm. The validation results show that the 95% uncertainty bands obtained by randomly sampling 93 groups of calculations on the probability density distributions can completely envelope the experimental data, but the calibration of the model using the plurality or the mean may not be as effective as the maximum a posteriori mean obtained by the Bayesian approach, which is the maximum a posteriori mean.