Multivariate prediction of major safety variables with confidence estimation during severe accidents in nuclear power plants

Although the probability of a severe accident at a nuclear power plant is low, it can result in catastrophic outcomes. This study employed deep-learning-based time-series models to simultaneously predict the core exit temperature, containment pressure, and hydrogen concentration, which are critical monitoring variables during severe accidents. Four models (recurrent neural network, long short-term memory, convolutional neural network (CNN), and temporal convolutional network) were implemented in a multi-input multi-output structure and trained on simulation data from cold-leg loss-of-coolant accident (LOCA), hot-leg LOCA, and steam generator tube rupture scenarios. To address predictive uncertainty, Monte Carlo dropout was applied to estimate the confidence intervals. Among the models, the CNN demonstrated a superior balance between predictive accuracy and computational efficiency. It achieved highly competitive performance, despite having significantly fewer trainable parameters and a dramatically faster training time. This approach combines multivariate prediction and uncertainty quantification, demonstrating the practical potential for integration into AI-based operator support systems. This methodology is expected to enhance the situational assessments of operators and support proactive mitigation strategies. Future work will expand the scope of validation by incorporating a wider range of accident scenarios and operational conditions, while also accounting for external and environmental variables that may influence the prediction accuracy.