Artificial Neural Network Optimization to Estimate Radon in Soil

Modeling and estimating radon concentration are of crucial interest to support health protection campaigns. 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 the 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-Forward Back propagation with the Levenberg–Marquardt 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.