Predicting Common Air Quality Index - The Case of Czech Microregions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F15%3A39899727" target="_blank" >RIV/00216275:25410/15:39899727 - isvavai.cz</a>

Výsledek na webu

<a href="http://aaqr.org/VOL15_No2_April2015/15_AAQR-14-08-OA-0154_544-555.pdf" target="_blank" >http://aaqr.org/VOL15_No2_April2015/15_AAQR-14-08-OA-0154_544-555.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4209/aaqr.2014.08.0154" target="_blank" >10.4209/aaqr.2014.08.0154</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting Common Air Quality Index - The Case of Czech Microregions

Popis výsledku v původním jazyce

This paper presents a design of models for common air quality index prediction using computational intelligence methods. In addition, the sets of input variables were optimized for each air pollutant prediction by genetic algorithms. Based on data measured by the three monitoring stations of Dukla, Rosice and Brnenska in the Czech Republic, the models were designed to predict air quality indices for each air pollutant separately and, consequently, to predict the common air quality index. Considering the root mean squared error, the results showed that the compositions of individual prediction models significantly outperform single prediction models of the common air quality index. The feature selection procedure indicates that the determinants of air quality indices were strongly locality specific. Therefore, the models can be applied to obtain more accurate one day ahead predictions of air quality indices. Here we show that the composition models achieve high prediction accuracy for maximum air quality indices (between 50.69 and 63.36%). The goal of the prediction by various methods was to compare the results of the prediction with the aim of various recommendations to micro-regional public administration management.

Název v anglickém jazyce

Predicting Common Air Quality Index - The Case of Czech Microregions

Popis výsledku anglicky

This paper presents a design of models for common air quality index prediction using computational intelligence methods. In addition, the sets of input variables were optimized for each air pollutant prediction by genetic algorithms. Based on data measured by the three monitoring stations of Dukla, Rosice and Brnenska in the Czech Republic, the models were designed to predict air quality indices for each air pollutant separately and, consequently, to predict the common air quality index. Considering the root mean squared error, the results showed that the compositions of individual prediction models significantly outperform single prediction models of the common air quality index. The feature selection procedure indicates that the determinants of air quality indices were strongly locality specific. Therefore, the models can be applied to obtain more accurate one day ahead predictions of air quality indices. Here we show that the composition models achieve high prediction accuracy for maximum air quality indices (between 50.69 and 63.36%). The goal of the prediction by various methods was to compare the results of the prediction with the aim of various recommendations to micro-regional public administration management.

Druh

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

CEP obor

DI - Znečištění a kontrola vzduchu

OECD FORD obor

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Projekt

<a href="/cs/project/TD010130" target="_blank" >TD010130: Regionalizace indikátorů ekonomické výkonnosti ve vazbě na kvalitu životního prostředí</a><br>

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Aerosol and Air Quality Research

ISSN

1680-8584

e-ISSN

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Svazek periodika

15

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

TW - Čínská republika (Tchaj-wan)

Počet stran výsledku

12

Strana od-do

544-555

Kód UT WoS článku

000353175300015

EID výsledku v databázi Scopus

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