The Neural Network Assisted Land Use Regression

• Published in: Atmosphere 2021-04, Vol.12 (4), p.452
• Main Authors: Bitta, Jan, Svozilík, Vladislav, Svozilíková Krakovská, Aneta
• Format: Article
• Language: English
• Subjects: air; Air pollution; Air quality; Air quality assessments; Air quality models; Data; Dispersion; Dispersion models; Emission analysis; Emissions; land; Land use; Mathematical models; Model testing; Neural networks; Outdoor air quality; Pollutants; pollution; Pollution data; Pollution dispersion; Quality assessment; Quality control; Random variables; regression; Regression analysis; Regression models; Remote sensing; use
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos12040452

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 R2 of the models was 0.639 and 0.652. Neural network regression provided a significantly higher quality of the models, their R2 was 0.937 and 0.938 for the factors based on emission data and pollution dispersion model results respectively.