Monitoring spatiotemporal variations of diel radon concentrations in peatland and forest ecosystems based on neural network and regression models

Concentrations of outdoor radon-222 (²²²Rn) in temperate grazed peatland and deciduous forest in northwestern Turkey were measured, compared, and modeled using artificial neural networks (ANNs) and multiple nonlinear regression (MNLR) models. The best-performing multilayer perceptron model selected out of 28 ANNs considerably enhanced accuracy metrics in emulating ²²²Rn concentrations relative to the MNLR model. The two ecosystems had similar diel patterns with the lowest ²²²Rn concentrations in the afternoon and the highest ones near dawn. Mean level (5.1?+?2.5 Bq?m^?3 h^?1) of ²²²Rn in the forest was three times smaller than that (15.8?+?9.7 Bq?m^?3) of ²²²Rn in the peatland. Mean ²²²Rn level had negative and positive relationships with air temperature and relative humidity, respectively.