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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20700 - Environmental engineering

Result continuities

  • Project

  • 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

  • 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

  • UT code for WoS article

    000642740300001

  • EID of the result in the Scopus database