The Neural Network Assisted Land Use Regression

• Main Authors: Jan Bitta, Vladislav Svozilík, Aneta Svozilíková Krakovská
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Atmosphere
• Subjects: land; use; regression; model; air; pollution
• Online Access: https://www.mdpi.com/2073-4433/12/4/452

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.