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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. |
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