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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU142274" target="_blank" >RIV/00216305:26210/21:PU142274 - isvavai.cz</a>

Výsledek na webu

<a href="https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S2666790821002536" target="_blank" >https://www-sciencedirect-com.ezproxy.lib.vutbr.cz/science/article/pii/S2666790821002536</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.clet.2021.100293" target="_blank" >10.1016/j.clet.2021.100293</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cleaner Engineering and Technology

ISSN

2666-7908

e-ISSN

—

Svazek periodika

neuveden

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

100293-100293

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85117160459