@article{
author = "Malinovic-Milicevic, Slavica and Vyklyuk, Yaroslav and Radovanovic, Milan M. and Petrovic, Marko D",
year = "2018",
abstract = "In this article, we proposed a simple neural network (NN) technique for estimating
erythemal ultraviolet (UV) radiation in Novi Sad (Serbia) using available input
parameters. The technique implies the use of one of two models depending on the
availability of the input parameter: (a) NN model 1 (NNM1) which uses global
solar radiation, clearness index, cloudiness and air mass; and (b) NN model
2 (NNM2) which adds total ozone content (TOC) to the NNM1 inputs. The three
feed-forward NNs with different internal structures and back propagation learning
method for each NN model were used in modelling. The parallel calculation was
used for learning each NN. The results showed that the NNM1 provides satisfactory estimate (R = 0.975, MBE = −0.614%, MAPE = 12.580%, RMSE =
17.716%) and that additional use of TOC NNM2 considerably improves the
results (R = 0.982, MBE = −0.726%, MAPE = 10.161%, RMSE = 14.509%).
The performances of developed NNMs become significantly better if warm part of
the year is isolated (MAPE = 10.981 and 8.958; RMSE = 13.889 and 11.709, for
NNM1 and NNM2, respectively). Variations of reconstructed annual averages of
daily doses in the period 1949–2012 indicate ability of the technique to model the
relationship between erythemal UV radiation and the affecting atmospheric factors. The analysis showed that the increasing trend during the warm part of the
year in the period 1981–1996 was mainly caused by TOC, while the increase after
1996 was to a greater extent caused by cloudiness.",
publisher = "United Kingdom : John Wiley & Sons Ltd.",
journal = "International Journal of Climatology",
title = "Long-term erythemal ultraviolet radiation in Novi Sad (Serbia) reconstructed by neural network modelling",
pages = "3264-3272",
volume = "38",
number = "8",
doi = "10.1002/joc.5499",
url = "https://hdl.handle.net/21.15107/rcub_dais_12919"
}