Rapid deep learning prediction model using satellite imagery for radiation accident Announcement system in Serbia

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-11-01 DOI:10.1016/j.nucengdes.2024.113657

Slavko Dimović, Milica Ćurčić, Dušan Nikezić, Ivan Lazović, Dušan Radivojević

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引用次数: 0

Abstract

Radioactivity environmental monitoring with the help of the Radiation Accident Announcement System (RAAS) established in the Republic of Serbia is of vital importance for rapid response in the event of intervention dose values being reached such as Operational Intervention levels (OILs). There are cases of impossibility of using a ground-based model in order to predict the transport and spread of radiation during a nuclear accident such as the one in the Fukushima Daiichi nuclear power plant 2011. Modern technology has made it possible to use machine learning models with remote sensing data in addition (or an alternative) to atmospheric models with ground data collection methods. Deep learning (DL) model was developed and trained on a satellite-based cloud fraction dataset to forecast diurnal cloud drift. By forecasting the movement of clouds that are potential carriers of radioactive materials, decision-makers can provide an adequate response with an Action Plan in the case of nuclear and radiation accidents and incidents. Designed Application Programming Interface (API) has been developed and integrated between the DL model and the RAAS. In this way, the monitoring, data sharing and exchange processes were automated in order to trigger the DL model when an OILs value of 1 μSv/h is reached. The hyperparameter optimization process of the DL model was done with grid search and Particle Swarm Optimization (PSO) to achieve maximum performance on the data in a reasonable amount of time. The evolution metrics to monitor and measure the performance of the DL model were cosine similarity (CS) and structural similarity (SS) with the best score of 0.78282 and 0.27063 for grid search, respectively, while 0.27065 and 0.78282 for PSO, respectively. It can be concluded that remote sensing imagery with DL model is an alternative approach against a ground-based forecasting system, and is able to predict in near real-time independently of ground network system and data.

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利用卫星图像的快速深度学习预测模型用于塞尔维亚辐射事故公告系统

在塞尔维亚共和国建立的辐射事故公告系统（RAAS）的帮助下，放射性环境监测对于在达到干预剂量值（如操作干预水平）时做出快速反应至关重要。在一些情况下，无法使用地面模型来预测核事故（如 2011 年福岛第一核电站事故）期间的辐射传播和扩散。现代技术已使利用遥感数据的机器学习模型成为可能，以补充（或替代）利用地面数据收集方法的大气模型。开发了深度学习（DL）模型，并在基于卫星的云分数数据集上进行了训练，以预测昼夜云漂移。通过预测作为放射性物质潜在载体的云的移动，决策者可以在发生核与辐射事故和事件时通过行动计划做出适当的反应。在 DL 模型和 RAAS 之间开发和集成了应用程序接口（API）。通过这种方式，监测、数据共享和交换过程实现了自动化，以便在 OILs 值达到 1 μSv/h 时触发 DL 模型。DL 模型的超参数优化过程是通过网格搜索和粒子群优化（PSO）完成的，以便在合理的时间内实现数据的最大性能。监测和衡量 DL 模型性能的演化指标是余弦相似度（CS）和结构相似度（SS），网格搜索的最佳得分分别为 0.78282 和 0.27063，而 PSO 的最佳得分分别为 0.27065 和 0.78282。由此可以得出结论，遥感图像与 DL 模型是地面预报系统的替代方法，能够独立于地面网络系统和数据进行近实时预报。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.