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

Abstract

Concentrations of outdoor radon-222 ((222)Rn) 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 (222)Rn concentrations relative to the MNLR model. The two ecosystems had similar diel patterns with the lowest (222)Rn concentrations in the afternoon and the highest ones near dawn. Mean level (5.1 + 2.5Bq m(-3) h(-1)) of (222)Rn in the forest was three times smaller than that (15.8 + 9.7Bq m(-3)) of (222)Rn in the peatland. Mean (222)Rn level had negative and positive relationships with air temperature and relative humidity, respectively.