Air Quality Assessment by Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F11%3A39892232" target="_blank" >RIV/00216275:25410/11:39892232 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.4018/978-1-60960-156-0.ch005" target="_blank" >http://dx.doi.org/10.4018/978-1-60960-156-0.ch005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-60960-156-0.ch005" target="_blank" >10.4018/978-1-60960-156-0.ch005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Air Quality Assessment by Neural Networks

Popis výsledku v původním jazyce

The chapter presents an overview of current methods for air quality assessment, i.e. air stress indices and air quality indices. Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected airpollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g.using systems based on neural networks and fuzzy logic. Therefore, the chapter presents a design of a model for air quality assessment based on a combination of Kohonen's self-organizing feature maps and fuzzy logic neural networks. The model makes it possible to analyze the structure of data, to find localities with similar air quality, and to interpret the classification results by means of fuzzy logic. Due to its generalization ability, it is also possible to classify unknown localit

Název v anglickém jazyce

Air Quality Assessment by Neural Networks

Popis výsledku anglicky

The chapter presents an overview of current methods for air quality assessment, i.e. air stress indices and air quality indices. Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected airpollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g.using systems based on neural networks and fuzzy logic. Therefore, the chapter presents a design of a model for air quality assessment based on a combination of Kohonen's self-organizing feature maps and fuzzy logic neural networks. The model makes it possible to analyze the structure of data, to find localities with similar air quality, and to interpret the classification results by means of fuzzy logic. Due to its generalization ability, it is also possible to classify unknown localit

Druh

C - Kapitola v odborné knize

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/SP%2F4I2%2F60%2F07" target="_blank" >SP/4I2/60/07: Indikátory pro hodnocení a modelování internakcí mezi životním prostředím, ekonomikou a sociálními souvislostmi</a><br>

Návaznosti

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

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Environmental Modeling for Sustainable Regional Development : System Approaches and Advanced Methods

ISBN

978-1-60960-156-0

Počet stran výsledku

27

Strana od-do

91-117

Počet stran knihy

492

Název nakladatele

IGI Global

Místo vydání

Hershey

Kód UT WoS kapitoly

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