An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants

Accurately predicting severe accident data in nuclear power plants is of utmost importance for ensuring their safety and reliability. However, existing methods often lack interpretability, thereby limiting their utility in decision making. In this paper, we present an interpretable framework, called...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2023-08, Vol.25 (8), p.1160
Main Authors: Fu, Yongjie, Zhang, Dazhi, Xiao, Yunlong, Wang, Zhihui, Zhou, Huabing
Format: Article
Language:English
Subjects:
Accident data
Accident prediction
Accidents
Analysis
Computational linguistics
Decision making
Emergency response
Forecasts and trends
GRU
Language processing
LOCA
Mathematical models
MSLB
Natural language interfaces
Neural networks
Nuclear accidents
Nuclear energy
Nuclear power plants
Nuclear reactors
Nuclear safety
Safety and security measures
SHAP
time series prediction
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Summary:Accurately predicting severe accident data in nuclear power plants is of utmost importance for ensuring their safety and reliability. However, existing methods often lack interpretability, thereby limiting their utility in decision making. In this paper, we present an interpretable framework, called GRUS, for forecasting severe accident data in nuclear power plants. Our approach combines the GRU model with SHAP analysis, enabling accurate predictions and offering valuable insights into the underlying mechanisms. To begin, we preprocess the data and extract temporal features. Subsequently, we employ the GRU model to generate preliminary predictions. To enhance the interpretability of our framework, we leverage SHAP analysis to assess the contributions of different features and develop a deeper understanding of their impact on the predictions. Finally, we retrain the GRU model using the selected dataset. Through extensive experimentation utilizing breach data from MSLB accidents and LOCAs, we demonstrate the superior performance of our GRUS framework compared to the mainstream GRU, LSTM, and ARIMAX models. Our framework effectively forecasts trends in core parameters during severe accidents, thereby bolstering decision-making capabilities and enabling more effective emergency response strategies in nuclear power plants.
ISSN:1099-4300
1099-4300
DOI:10.3390/e25081160