"VINČA" Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovića Alasa 12-14, 11351 Belgrade, Serbia.
Sci Total Environ. 2022 Nov 15;847:157526. doi: 10.1016/j.scitotenv.2022.157526. Epub 2022 Jul 21.
The development of nuclear technologies has directed environmental radioactivity research toward continuously improving existing and developing new models for different interpolation, optimization, and classification tasks. Due to their adaptability to new data without knowing the actual modeling function, artificial neural networks (ANNs) are extensively used to resolve the tasks for which the application of traditional statistical methods has not provided an adequate response. This study presents an overview of ANN-based modeling in environmental radioactivity studies, including identifying and quantifying radionuclides, predicting their migration in the environment, mapping their distribution, optimizing measurement methodologies, monitoring processes in nuclear plants, and real-time data analysis. Special attention is paid to highlighting the scope of the different case studies and discussing the techniques used in model development over time. The performances of ANNs are evaluated from the perspective of prediction accuracy, emphasizing the advantages and limitations encountered in their use. The most critical elements in model optimization were identified as network structure, selection of input parameters, the properties of input data set, and applied learning algorithm. The analysis of strategies and methods for improving the performance of ANNs has shown that developing integrated and hybrid artificial intelligent tools could provide a new path in environmental radioactivity modeling toward more reliable outcomes and higher accuracy predictions. The review highlights the potential of neural networks and challenges in their application in environmental radioactivity studies and proposes directions for future research.
核技术的发展使得环境放射性研究不断改进现有的和开发新的模型，以用于不同的插值、优化和分类任务。由于人工神经网络 (ANNs) 能够适应新数据而无需了解实际的建模函数，因此被广泛应用于解决传统统计方法无法提供充分响应的任务。本研究概述了基于人工神经网络的环境放射性研究中的建模，包括识别和量化放射性核素、预测其在环境中的迁移、绘制其分布、优化测量方法、监测核电厂中的过程以及实时数据分析。特别强调了不同案例研究的范围，并讨论了随着时间的推移模型开发中使用的技术。从预测准确性的角度评估了 ANNs 的性能，强调了在使用中遇到的优势和限制。确定了模型优化的最关键因素，包括网络结构、输入参数的选择、输入数据集的属性以及应用的学习算法。对提高 ANNs 性能的策略和方法的分析表明，开发集成和混合人工智能工具可以为环境放射性建模提供新的途径，以获得更可靠的结果和更高的准确性预测。本综述强调了神经网络的潜力及其在环境放射性研究中的应用所面临的挑战，并提出了未来研究的方向。
