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

   Evrendilek, Fatih; Denizli, Haluk; Yetis, Hakan; Karakaya, Nusret

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

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