Ozone prediction on the basis of neural networks, support vector regression and methods with uncertainty

The article presents modeling of daily average ozone level prediction by means of neural networks, support vector regression and methods based on uncertainty. Based on data measured by a monitoring station of the Pardubice micro-region, the Czech Republic, and optimization of the number of parameter...

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Veröffentlicht in:	Ecological informatics Jg. 12; S. 31 - 42
Hauptverfasser:	Hájek, Petr, Olej, Vladimír
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2012
Schlagworte:	algorithms Czech Republic Evolutionary fuzzy logic-based systems government and administration monitoring Neural networks ozone Ozone prediction prediction regression analysis Takagi–Sugeno fuzzy inference systems Takagi–Sugeno intuitionistic fuzzy inference systems uncertainty ε-Support vector regression Czech Republic Ozone prediction Takagi–Sugeno intuitionistic fuzzy inference systems Neural networks ε-Support vector regression Takagi–Sugeno fuzzy inference systems Evolutionary fuzzy logic-based systems
ISSN:	1574-9541
Online-Zugang:	Volltext
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