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Statements

Subject Item
n2:RIV%2F61989100%3A27740%2F13%3A86088506%21RIV14-MSM-27740___
rdf:type
n7:Vysledek skos:Concept
dcterms: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.
dcterms: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
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
skos:notation
RIV/61989100:27740/13:86088506!RIV14-MSM-27740___
n7:predkladatel
n19:orjk%3A27740
n3:aktivita
n16:P
n3:aktivity
P(ED1.1.00/02.0070), P(EE.2.3.20.0073)
n3:cisloPeriodika
12
n3:dodaniDat
n5:2014
n3:domaciTvurceVysledku
n10:6042570 n10:8051283
n3:druhVysledku
n15:J
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
104324
n3:idVysledku
RIV/61989100:27740/13:86088506
n3:jazykVysledku
n17:eng
n3:klicovaSlova
Symbolic Regression; Quality Index; Knowledge extraction; Air pollution
n3:klicoveSlovo
n8:Air%20pollution n8:Symbolic%20Regression n8:Knowledge%20extraction n8:Quality%20Index
n3:kodStatuVydavatele
DE - Spolková republika Německo
n3:kontrolniKodProRIV
[B3D2F69307F1]
n3:nazevZdroje
Advances in Intelligent Systems and Computing. Volume 210
n3:obor
n20:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:projekt
n14:ED1.1.00%2F02.0070 n14:EE.2.3.20.0073
n3:rokUplatneniVysledku
n5:2013
n3:svazekPeriodika
210
n3:tvurceVysledku
Radecký, Michal Vozňák, Miroslav Gajdoš, Petr
s:issn
2194-5357
s:numberOfPages
12
n9:doi
10.1007/978-3-319-00542-3_51
n18:organizacniJednotka
27740