Prediction of burn-up nucleus density based on machine learning

Machine learning models were built by using four different algorithms using Linear Regression, Regression Tree, Multi-Layer Perceptron, and Random Forest by 10-fold Cross-Validation method using the training set. The validity of the four different machine learning algorithms was verified by predicting the nuclide densities of U-235, U-238, Pu-239, Pu-241, Cs-137, Cm-244, and Nd-254 at different burn-up depths by enrichment and burn-up depth. The experimental results show that the Pearson Correlation Coefficients of the training sets of the four algorithms based on the 10-fold Cross-Validation method are all greater than 0.72, among which the evaluation coefficients of the models of Regression Tree and Random Forest are better than those of the Multi-Layer Perceptron and Linear Regression; however, the prediction based on the test set is found that the model of the Multi-Layer Perceptron predicts better than the other three models, and the average deviation is less than 1% and the average deviation is less than 3% for the Regression Tree and Random Forest algorithm model.