The Neural Network Assisted Land Use Regression
The result's identifiers
Result code in 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>
Alternative codes found
RIV/61989100:27360/21:10247922 RIV/61989100:27710/21:10247922
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
The Neural Network Assisted Land Use Regression
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20700 - Environmental engineering
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Atmosphere
ISSN
2073-4433
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
4
Country of publishing house
CH - SWITZERLAND
Number of pages
23
Pages from-to
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UT code for WoS article
000642740300001
EID of the result in the Scopus database
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