دورية أكاديمية
An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants
| العنوان: | An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants |
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| المؤلفون: | Yongjie Fu, Dazhi Zhang, Yunlong Xiao, Zhihui Wang, Huabing Zhou |
| المصدر: | Entropy, Vol 25, Iss 8, p 1160 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Science LCC:Astrophysics LCC:Physics |
| مصطلحات موضوعية: | time series prediction, GRU, SHAP, MSLB, LOCA, Science, Astrophysics, QB460-466, Physics, QC1-999 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 25081160 1099-4300 |
| Relation: | https://www.mdpi.com/1099-4300/25/8/1160; https://doaj.org/toc/1099-4300 |
| DOI: | 10.3390/e25081160 |
| URL الوصول: | https://doaj.org/article/857fd4f5280349b89cef819dd94aa64c |
| رقم الأكسشن: | edsdoj.857fd4f5280349b89cef819dd94aa64c |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 25081160 10994300 |
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| DOI: | 10.3390/e25081160 |