Method to determine nuclear accident release category via environmental monitoring data based on a neural network

Abstract

After a severe nuclear accident, the source term is typically unknown. Therefore, great importance is attached to obtaining source term information for subsequent emergency planning. The purpose of the study involved performing a rapid determination of the release category using the gamma dose rate monitoring data over a short period during an accident. The release categories PWR1–PWR9 in 'The Reactor Safety Study' of the United States were used as a reference. The International Radiological Assessment System (InterRAS) was used to construct a nuclear accident model and generate the required simulation data. After a series of experiments, appropriate parameters were selected to construct the backpropagation neural network (BPNN) classifier to estimate the release category. The genetic algorithm (GA) and simulated annealing (SA) algorithm were used to search for the weights and thresholds of the BPNN classifier in advance, and this avoided the problem wherein bad initial values can cause the classifier to fall into local minimums, decreased training time, and improved prediction accuracy from 98.36% to 99.10%. With respect to the possible absence of gamma dose rate monitoring data, particle swarm optimisation (PSO) was used to complete the missing data, thereby ensuring that the classifier can normally predict the release category. After testing, in the absence of 4 of the total 16 gamma dose rate data, the classifier can still maintain a prediction accuracy of more than 80%.