{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T05:50:51Z","timestamp":1771998651229,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T00:00:00Z","timestamp":1690934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62171327"],"award-info":[{"award-number":["62171327"]}]},{"name":"National Natural Science Foundation of China","award":["62171328"],"award-info":[{"award-number":["62171328"]}]},{"name":"National Natural Science Foundation of China","award":["62072350"],"award-info":[{"award-number":["62072350"]}]},{"name":"National Natural Science Foundation of China","award":["B210610"],"award-info":[{"award-number":["B210610"]}]},{"name":"basic technology and science research foundation from the Hubei Nuclear Power Operation Engineering Technology Research Center","award":["62171327"],"award-info":[{"award-number":["62171327"]}]},{"name":"basic technology and science research foundation from the Hubei Nuclear Power Operation Engineering Technology Research Center","award":["62171328"],"award-info":[{"award-number":["62171328"]}]},{"name":"basic technology and science research foundation from the Hubei Nuclear Power Operation Engineering Technology Research Center","award":["62072350"],"award-info":[{"award-number":["62072350"]}]},{"name":"basic technology and science research foundation from the Hubei Nuclear Power Operation Engineering Technology Research Center","award":["B210610"],"award-info":[{"award-number":["B210610"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e25081160","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T10:57:33Z","timestamp":1690973853000},"page":"1160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants"],"prefix":"10.3390","volume":"25","author":[{"given":"Yongjie","family":"Fu","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China"},{"name":"Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, China"}]},{"given":"Dazhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"CNNC Key Laboratory on Nuclear Industry Simulation, China Nuclear Power Operation Technology Corporation, Ltd., Wuhan 430040, China"}]},{"given":"Yunlong","family":"Xiao","sequence":"additional","affiliation":[{"name":"China Nuclear Power Operation Technology Corporation, Ltd., Wuhan 430040, China"}]},{"given":"Zhihui","family":"Wang","sequence":"additional","affiliation":[{"name":"CNNC Key Laboratory on Nuclear Industry Simulation, China Nuclear Power Operation Technology Corporation, Ltd., Wuhan 430040, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5007-7303","authenticated-orcid":false,"given":"Huabing","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, China"},{"name":"Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.enpol.2016.01.011","article-title":"Historical construction costs of global nuclear power reactors","volume":"91","author":"Lovering","year":"2016","journal-title":"Energy Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1016\/j.eng.2021.10.010","article-title":"Contemplation on China\u2019s energy-development strategies and initiatives in the context of its carbon neutrality goal","volume":"7","author":"Dai","year":"2021","journal-title":"Engineering"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e13883","DOI":"10.1016\/j.heliyon.2023.e13883","article-title":"A review of the application of artificial intelligence to nuclear reactors: Where we are and what\u2019s next","volume":"9","author":"Huang","year":"2023","journal-title":"Heliyon"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1016\/j.ymssp.2010.11.018","article-title":"Prognostic modelling options for remaining useful life estimation by industry","volume":"25","author":"Sikorska","year":"2011","journal-title":"Mech. 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