一个可解释的时间序列数据预测框架,用于核电站严重事故
Yongjie Fu1,2, Dazhi Zhang3, Yunlong Xiao4
1College of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了GRUS,这是一个可解释的框架,用于预测核电站严重事故数据. GRUS结合了GRU和SHAP分析,用于准确的预测和加强核安全决策.
科学领域:
- 核工程 核工程是指核工程.
- 计算科学 计算科学
背景情况:
- 准确预测核电站严重事故数据对于安全性和可靠性至关重要.
- 现有的预测方法往往缺乏可解释性,这阻碍了在紧急情况下有效的决策.
研究的目的:
- 开发一个可解释的框架 (GRUS) 来预测核电站严重事故数据.
- 通过提供对潜在机制的洞察,增强核安全预测模型的实用性.
主要方法:
- 数据预处理和时间特征提取.
- 在初始预测中使用门式循环单位 (GRU) 模型.
- 应用夏普利添加式解释 (SHAP) 分析来评估特征贡献并提高可解释性.
- 使用选定的功能重新训练GRU模型.
主要成果:
- 与GRU,LSTM和ARIMAX模型相比,GRUS框架在预测MSLB和LOCA事故中的违规数据方面表现优越.
- 在严重事故中,GRUS有效地预测了核心参数的趋势.
- 该框架为影响事故进展的因素提供了有价值的见解.
结论:
- GRUS框架提供了一种可解释和准确的方法,用于预测核电站严重事故数据.
- 加强决策能力和更有效的应急响应策略是GRUS.所实现的.
- 这种可解释的方法可以增强对核安全应用预测模型的信心和实用性.
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