用于决策支持系统的“土壤-植物”链中的放射性核素行为模型

A. Nikitin, I. A. Cheshyk, S. Kalinichenko, O. Shurankova
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摘要

本文证明了半机械模型增强的主要目标。“土壤-植物”链是放射性同位素从核事故沉积物流向人类的重要组成部分。因此,应将描述该系统的模型集成到放射性同位素释放事故后果清算、辐射防护措施有效性评价和危险辐射设施设计的决策支持系统中。这种模型必须具有预测结果相当准确、应用领域灵活广泛、便于实际使用等特点。提出了放射性核素在“土壤-植物”系统中的行为模式,分为经验模式、机制模式和半机制模式。经验性方法没有考虑到放射性核素的生物可利用性及其被植物吸收的变化的基本机制,需要不断更新和改进过渡参数。机械模型在现实生活中没什么用处。最后一种最符合上述要求。然而,需要大量的努力来提高它们的准确性、可用性和泛化。这需要将现有和计划中的传感器系统集成到数据模型中;审议影响放射性核素向工厂转移的其他因素;因地制宜,提高模式的泛化水平;利用机器学习方法将相关领域积累的信息整合到模型中;更多放射性同位素的覆盖范围;在仿真结果中加入不确定性估计;放射性核素行为模型与地理信息系统的集成保持建模结果足够的可解释性和可见性。
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Models of radionuclides behavior in "soil-plant" chain for decision support systems
The main objectives for semi-mechanistic models enhancement are justified in the article. The "soil-plant" chain is an essential part of radioisotopes flows from nuclear accident depositions to human beings. Therefore a model which describes this system should be integrated into decision support systems for liquidation consequences of accidents with releasing radioisotopes into the environment, evaluation effectiveness of measures for radiation protection, and designing hazardous radiation facilities. Such a model must show rather exact forecast results, flexibility and wide application area convenience for practical use, and other properties. Presented now models of radionuclides behavior in "soil-plant" system divided on empiric, mechanistic, and semi-mechanistic. The empirical ones do not take into account the basic mechanisms of changes in the biological availability of radionuclides and their absorption by plants, and require constant updating and refinement of the transition parameters. Mechanistic models are of little use in real life. The last ones best meet the requirements noted above. However, substantial efforts are needed for improving their accuracy, usability, and generalization. This requires integration into data models from existing and planned sensor systems; consideration of additional factors influ-encing the transfer of radionuclides to plants; increasing the level of generalization of models with adjustment to local conditions; the use of machine learning methods to integrate information accumulated in related fields into the model; coverage of more radioactive isotopes; adding an uncertainty estimate to the simulation result; integration of models of radionuclide behavior into geoinformation systems; maintaining a sufficient level of interpretability and visibility of modeling results.
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