RT Journal Article
T1 Testing machine learning algorithms for the prediction of depositional fluxes of the radionuclides 7Be, 210Pb and 40K
A1 De La Torre Luque, P.
A1 Dueñas-Buey, Mª Concepción
A1 Gordo Puertas, Elisa
A1 Cañete-Hidalgo, Sergio Andrés
K1 Aprendizaje automático (Inteligencia artificial)
K1 Isótopos radiactivos
K1 Deposición atmosférica
AB The monthly depositional fluxes of 7Be, 210Pb and 40K were measured at Malaga, (Southern Spain) from 2005 to 2018. In this work, the depositional fluxes of these radionuclides are investigated and their relations with several atmospheric variables have been studied by applying two popular machine learning methods: Random Forest and Neural Network algorithms. We extensively test different configurations of these algorithms and demonstrate their predictive ability for reproducing depositional fluxes. The models derived with Neural Networks achieve slightly better results, in average, although similar, having into account the uncertainties. The mean Pearson-R coefficients, evaluated with a k-fold cross-validation method, are around 0.85 for the three radionuclides using Neural Network models, while they go down to 0.83, 0.79 and 0.8 for 7Be, 210Pb and 40K, respectively, for the Random Forest models. Additionally, applying the Recursive Feature Elimination technique we determine the variables more correlated with the depositional fluxes of these radionuclides, which elucidates the main dependences of their temporal variability.
PB Elsevier
YR 2023
FD 2023
LK https://hdl.handle.net/10630/27107
UL https://hdl.handle.net/10630/27107
LA eng
NO De La Torre Luque, P. et al. “Testing Machine Learning Algorithms for the Prediction of Depositional Fluxes of the Radionuclides 7Be, 210Pb and 40K.” Journal of environmental radioactivity 265 (2023): 107213–107213. Web.
NO This research was funded by Consejo de Seguridad Nuclear (Spain).Funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026