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In the literature, many studies concentrated on indoor radon, while few of them investigated the outdoor radon spatial distribution and the factors that influence its formation. In this context, the vast possibilities of the artificial intelligence systems, based on machine learning techniques, can show remarkable capabilities. This paper focuses on\u00a0the optimization of the architecture and the parameters of an artificial neural network (ANN) for inferring outdoor radon concentrations. More specifically, in the development of alternative ANN models, the Feed\u2010Forward Back propagation with the Levenberg\u2013Marquardt is performed with different hidden layers to train the models and a bootstrap resampling method is applied to improve the model generalization. Some evaluation metrics and a sensitivity analysis are also included in order to assess the prediction accuracy among the ANN models.<\/jats:p>","DOI":"10.1002\/sam.70036","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T13:16:24Z","timestamp":1757337384000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Neural Network Optimization to Estimate Radon in Soil"],"prefix":"10.1002","volume":"18","author":[{"given":"Veronica","family":"Distefano","sequence":"first","affiliation":[{"name":"European Centre for Living Technology (ECLT), ca' Foscari University of Venice  Italy"},{"name":"Department of Management and Economics University Pegaso  Naples Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1820-2068","authenticated-orcid":false,"given":"Sandra","family":"De Iaco","sequence":"additional","affiliation":[{"name":"DSE\u2014Section of Mathematics and Statistics 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