THE PREDICTION OF TROPOSPHERIC OZONE USING A RADIAL BASIS FUNCTION NETWORK

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00226139" target="_blank" >RIV/68407700:21230/15:00226139 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-10759-2_13" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-10759-2_13</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-10759-2_13" target="_blank" >10.1007/978-3-319-10759-2_13</a>

Result language
angličtina
Original language name
THE PREDICTION OF TROPOSPHERIC OZONE USING A RADIAL BASIS FUNCTION NETWORK
Original language description
The goal of this paper is to analyze the tropospheric ozone (O3) concentration time series and its prediction using artificial neural networks (ANNs). Tropospheric ozone has harmful effects on human health and on the environment. This study was based ondaily averaged tropospheric ozone (O3) data from Pardubice in the Czech Republic. In this study, daily averaged ozone concentrations in Pardubice were predicted using a radial basis function network (RBFN) with three pollutant parameters and three meteorological factors in selected areas. We used a three-layer ANN, which consists of input, hidden, and output layers.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
ISCS 2014: Interdisciplinary Symposium on Complex Systems
ISBN
978-3-319-10758-5
ISSN
2194-7287
e-ISSN
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Number of pages
9
Pages from-to
115-123
Publisher name
Springer
Place of publication
Dordrecht
Event location
Florence
Event date
Sep 15, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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