Nuclear accident source term estimation using Kernel Principal Component Analysis, Particle Swarm Optimization, and Backpropagation Neural Networks

Abstract

Rapid estimation of the release rate of source items after a nuclear accident is very important for nuclear emergency and decision making. A source term estimation method, based on the Backpropagation Neural Network (BPNN), was developed. Kernel Principal Component Analysis (KPCA) is used to reduce the dimension of the input parameters, which can accelerate the training of the neural network. Particle Swarm Optimization (PSO) is used to optimize weights and thresholds of BPNN, so that the neural network can better find the global optimal value, avoid falling into the local minimum. The large amount of data required for neural network training is generated using InterRAS software, the model constructed demonstrates the feasibility of this method. The proposed method can estimate the release rate of I-131 after half an hour of release, which is helpful to the emergency response, or provide an initial value or priori information for other methods.