Testing machine learning algorithms for the prediction of depositional fluxes of the radionuclides Be, Pb and K.

The monthly depositional fluxes of Be, Pb and K 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 Be, Pb and K, 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.