Applications of Machine Learning to Consequence Analysis of Hypothetical Accidents at Barakah Nuclear Power Plant Unit 1

Abstract

The United Arab Emirates (UAE) built four nuclear power plants at the Barakah site to supply 25% of the region’s electricity. Among the Barakah Nuclear Power Plants, (BNPPs), their main objectives are to achieve the highest possible safety for the environment, operators, and community members; quality nuclear reactors and energy; and power production efficiency. To meet these objectives, decision-makers must access large amounts of data in the case of a nuclear accident to prevent the release of radioactive materials. Machine learning offers a feasible solution to propose early warnings and help contain accidents. Thus, our study aimed at developing and testing a machine learning model to classify nuclear accidents using the associated release of radioactive materials. We used Radiological Assessment System for Consequence Analysis (RASCAL) software to estimate the concentration of released radioactive materials in the four seasons of the year 2020. We applied these concentrations as predictors in a classification tree model to classify three types of severe accidents at Unit 1 of BNPPs each season. The average accuracy of the classification models in the four seasons was 97.3% for the training data and 96.5% for the test data, indicating a high efficacy. Thus, the generated classification models can distinguish between the three simulated accidents in any season.