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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F12%3A39895104" target="_blank" >RIV/00216275:25410/12:39895104 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ecoinf.2012.09.001" target="_blank" >http://dx.doi.org/10.1016/j.ecoinf.2012.09.001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecoinf.2012.09.001" target="_blank" >10.1016/j.ecoinf.2012.09.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 parameters by a defined objective function and genetic algorithm a model of daily average ozone level prediction in a certain time has been designed. The designed model has been optimized in light of its input parameters. The goal of prediction by various methods was to compare the results of prediction with the aim of various recommendations to micro-regional public administration management. It is modeling by means of feed-forward perceptron type neural networks, time delay neural networks, radial basis function neural networks, epsilon-support vector regression, fuzzy inference systems and Takagi-Sugeno intuitionistic fuzzy inference systems. Special attention is paid to the adaptation of the Taka

Název v anglickém jazyce

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

Popis výsledku anglicky

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 parameters by a defined objective function and genetic algorithm a model of daily average ozone level prediction in a certain time has been designed. The designed model has been optimized in light of its input parameters. The goal of prediction by various methods was to compare the results of prediction with the aim of various recommendations to micro-regional public administration management. It is modeling by means of feed-forward perceptron type neural networks, time delay neural networks, radial basis function neural networks, epsilon-support vector regression, fuzzy inference systems and Takagi-Sugeno intuitionistic fuzzy inference systems. Special attention is paid to the adaptation of the Taka

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Ecological Informatics

ISSN

1574-9541

e-ISSN

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Svazek periodika

12

Číslo periodika v rámci svazku

Listopad

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

31-42

Kód UT WoS článku

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EID výsledku v databázi Scopus

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