The Neural Network Assisted Land Use Regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F21%3A10247922" target="_blank" >RIV/61989100:27350/21:10247922 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27360/21:10247922 RIV/61989100:27710/21:10247922

Výsledek na webu

<a href="https://doi.org/10.3390/atmos12040452" target="_blank" >https://doi.org/10.3390/atmos12040452</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/atmos12040452" target="_blank" >10.3390/atmos12040452</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Neural Network Assisted Land Use Regression

Popis výsledku v původním jazyce

Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a possibility to incorporate more factors affecting pollutant concentration than standard dispersion models. The goal of the study was to perform the LUR model in the Polish-Czech-Slovakian Tritia region, to test two sets of pollution data input factors, i.e., factors based on emission data and pollution dispersion model results, to test regression via neural networks and compare it with standard linear regression. Both input datasets, emission data and pollution dispersion model results, provided a similar quality of results in the case when standard linear regression was used, the R-2 of the models was 0.639 and 0.652. Neural network regression provided a significantly higher quality of the models, their R-2 was 0.937 and 0.938 for the factors based on emission data and pollution dispersion model results respectively.

Název v anglickém jazyce

The Neural Network Assisted Land Use Regression

Popis výsledku anglicky

Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a possibility to incorporate more factors affecting pollutant concentration than standard dispersion models. The goal of the study was to perform the LUR model in the Polish-Czech-Slovakian Tritia region, to test two sets of pollution data input factors, i.e., factors based on emission data and pollution dispersion model results, to test regression via neural networks and compare it with standard linear regression. Both input datasets, emission data and pollution dispersion model results, provided a similar quality of results in the case when standard linear regression was used, the R-2 of the models was 0.639 and 0.652. Neural network regression provided a significantly higher quality of the models, their R-2 was 0.937 and 0.938 for the factors based on emission data and pollution dispersion model results respectively.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20700 - Environmental engineering

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Atmosphere

ISSN

2073-4433

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

23

Strana od-do

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Kód UT WoS článku

000642740300001

EID výsledku v databázi Scopus

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