Publication detail

Modelling tropospheric ozone variations using artificial neural networks: A case study on the Black Sea coast (Russian Federation)

Makarova, A., Evstaf'eva, E., Lapchenco, V., Varbanov, P.S.

Original Title

Modelling tropospheric ozone variations using artificial neural networks: A case study on the Black Sea coast (Russian Federation)

Type

journal article in Scopus

Language

English

Original Abstract

This study focuses on modelling tropospheric ozone using neural networks to predict its concentration depending on environmental parameters. Predicting tropospheric ozone concentration is an important task, especially in recreational areas, since it harms human health. Considering the great complexity in describing the mechanisms of formation and destruction of tropospheric ozone and their dependence on a number of factors (temperature, humidity, pressure, wind speed and direction) the authors have suggested using Neural Networks to predict it. The selection of the network configuration and the algorithm for its training largely depends on the type of initial data available to researchers. The following fundamental factors were taken into account: temperature, humidity, and wind direction. The selected Neural Networks have been applied to a dataset obtained from the Russian Federation. From the obtained results, the best forecasting accuracy was achieved by using a Feed-Forward Back-Propagation Artificial Neural Network with 3 layers. The accuracy of prediction of the artificial neural networks against the measured data was evaluated using the Index of Agreement (IOA), which was estimated at 0.87, which equals other work in this field. This level of accuracy is equivalent to previous advances, but the standard software and built-in neural network configurations were used. The presented results have also confirmed that the wind speed and direction have a significant impact on the forecast accuracy – excluding wind speed reduces the IOA to 0.839; excluding wind speed and direction reduces the IOA to 0.0.807. © 2021

Keywords

Artificial neural networks; Meteorological factors; Modelling; Tropospheric ozone; Wind speed and direction

Authors

Makarova, A., Evstaf'eva, E., Lapchenco, V., Varbanov, P.S.

Released

1. 12. 2021

Publisher

Elsevier, Ltd.

ISBN

2666-7908

Periodical

Cleaner Engineering and Technology

Number

5

State

Kingdom of the Netherlands

Pages from

100293

Pages to

100293

Pages count

13

URL

https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S2666790821002536

BibTex

@article{BUT174909,
  author="Petar Sabev {Varbanov}",
  title="Modelling tropospheric ozone variations using artificial neural networks: A case study on the Black Sea coast (Russian Federation)",
  journal="Cleaner Engineering and Technology",
  year="2021",
  number="5",
  pages="100293--100293",
  doi="10.1016/j.clet.2021.100293",
  issn="2666-7908",
  url="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S2666790821002536"
}