Machine Learning-Based Classification and Regression Approach for Sustainable Disaster Management: The Case Study of APR1400 in Korea

Abstract

During nuclear accidents, decision-makers need to handle considerable data to take appropriate protective actions to protect people and the environment from radioactive material release. In such scenarios, machine learning can be an essential tool in facilitating the protection action decisions that will be made by decision-makers. By feeding machines software with big data to analyze and identify nuclear accident behavior, types, and the concentrations of released radioactive materials can be predicted, thus helping in early warning and protecting people and the environment. In this study, based on the ground deposition concentration of radioactive materials at different distances offsite in an emergency planning zone (EPZ), we proposed classification and regression models for three severe accidents. The objective of the classification model is to recognize the transient situation type for taking appropriate actions, while the objective of the regression model is to estimate the concentrations of the released radioactive materials. We used the Personal Computer Transient Analyser (PCTRAN) Advanced Power Reactor (APR) 1400 to simulate three severe accident scenarios and to generate a source term released to the environment. Additionally, the Radiological Consequence Analysis Program (RCAP) was used to assess the off-site consequences of nuclear power plant accidents and to estimate the ground deposition concentrations of radionuclides. Moreover, ground deposition concentrations at different distances were used as input data for the classification and regression tree (CART) models to obtain an accident pattern and to establish a prediction model. Results showed that the ground deposition concentration at a near distance from a nuclear power plant is a more informative parameter in predicting the concentration of radioactive material release, while the ground deposition concentration at a far distance is a very informative parameter in identifying accident types. In the regression model, the R-square of the training and test data was 0.995 and 0.994, respectively, showing a mean strong linear relationship between the predicted and actual concentration of radioactive material release. The mean absolute percentage error was found to be 26.9% and 28.1% for the training and test data, respectively. In the classification model, the model predicted a scenario (1) of 99.8% and 98.9%, scenario (2) of 98.4% and 91.6%, and scenario (3) of 98.6% and 94.7% for the training and test data, respectively.

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

“…The research demonstrated that offsite parameters could support prompt decisionmaking during a nuclear disaster when onsite parameters are unavailable [1]. In this case, machine learning is a viable option for the underlying assessments.…”

“…The receiver operator characteristic (ROC) curve with its area under the curve (AUC) is another metric for evaluating the four models' performance in correctly classifying each accident. ROC AUC is the probability that the classification model ranks a random positive for a specific accident [1]. Generally, the classification of the four models for each accident type was excellent and never went below 0.97.…”

“…Furthermore, nuclear reactors have a greater energy production capacity than many other alternative energy sources, such as hydro, wind, and solar power plants. However, the reactors have several problems, the most serious of which is the potential toxicity to the environment and people [1]. Public awareness of nuclear disaster prevention has grown following the Fukushima Daiichi Nuclear Power Plant accident.…”

Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review

“…Many studies highlight the usefulness of technologies for EDMPP. To focus on a few technologies; various studies describe the utility of artificial intelligence and machine learning and reasoning [69,71,80,168,169,[193][194][195][196][197][198][199][200][201][202][203][204][205][206][207][208][209][210] for EDMPP including for the evaluation of societal implications [211]. If it is seen as useful to evaluate societal implications, the data must contain high quality information on the 'social', especially in relation to marginalized groups.…”

Time-Series Forecasting of a Typical PWR Undergoing Large Break LOCA

“…ML models have also been used to predict the difusion and transport of radioactive materials in the atmosphere [17,18] and the prediction of the radioprotector compounds' efectiveness and toxicity [19]. Furthermore, IAEA is supporting the increasing trend of using robots in environments contaminated with radiation [20].…”