About: Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	This article deals with searching for dependences within the System of Measuring Stations.Weather measuring stations represent one of the most important data sources. The same could be said about stations that measure the composition of air and the level of pollutants. Knowledge of the current state of air quality resulting from the measured values is essential for citizens, especially in areas affected by heavy industry or dense traffic. Computation of such air quality indicators depends on values obtained from measuring stations which are more or less reliable. They can have failures or they can measure just a part of the required values. In general, searching for dependences represents a complex and non-linear problem that can be effectively solved by some class of evolutionary algorithms. This article describes a method that helps us to predict the levels of air quality in the case of station failure or data loss. The model is constructed by the symbolic regression with usage of the principles of genetic algorithms. The level of air quality of a given station is predicted with respect to a set of surrounding stations. All experiments were focused on real data obtained from the system of stations located in the Czech republic. This article deals with searching for dependences within the System of Measuring Stations.Weather measuring stations represent one of the most important data sources. The same could be said about stations that measure the composition of air and the level of pollutants. Knowledge of the current state of air quality resulting from the measured values is essential for citizens, especially in areas affected by heavy industry or dense traffic. Computation of such air quality indicators depends on values obtained from measuring stations which are more or less reliable. They can have failures or they can measure just a part of the required values. In general, searching for dependences represents a complex and non-linear problem that can be effectively solved by some class of evolutionary algorithms. This article describes a method that helps us to predict the levels of air quality in the case of station failure or data loss. The model is constructed by the symbolic regression with usage of the principles of genetic algorithms. The level of air quality of a given station is predicted with respect to a set of surrounding stations. All experiments were focused on real data obtained from the system of stations located in the Czech republic. (en)
Title	Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression (en)
skos:prefLabel	Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression Searching for Dependences within the System of Measuring Stations by Using Symbolic Regression (en)
skos:notation	RIV/61989100:27740/13:86088506!RIV14-MSM-27740___
http://linked.open...avai/predkladatel	IT4Innovations
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(ED1.1.00/02.0070), P(EE.2.3.20.0073)
http://linked.open...iv/cisloPeriodika	12
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Gajdoš, Petr Vozňák, Miroslav
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoká škola báňská - Technická univerzita Ostrava / IT4Innovations
http://linked.open...dnocenehoVysledku	104324
http://linked.open...ai/riv/idVysledku	RIV/61989100:27740/13:86088506
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Symbolic Regression; Quality Index; Knowledge extraction; Air pollution (en)
http://linked.open.../riv/klicoveSlovo	Knowledge extraction Quality Index Air pollution Symbolic Regression
http://linked.open...odStatuVydavatele	DE - Spolková republika Německo
http://linked.open...ontrolniKodProRIV	[B3D2F69307F1]
http://linked.open...i/riv/nazevZdroje	Advances in Intelligent Systems and Computing. Volume 210
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Bio-Inspired Methods: research, development and knowledge transfer IT4Innovations Centre of Excellence
http://linked.open...UplatneniVysledku	2013
http://linked.open...v/svazekPeriodika	210
http://linked.open...iv/tvurceVysledku	Radecký, Michal Vozňák, Miroslav Gajdoš, Petr
issn	2194-5357
number of pages	12 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-319-00542-3_51
http://localhost/t...ganizacniJednotka	27740

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