https://doi.org/10.1140/epjs/s11734-024-01341-w
Regular Article
Using land use methodology to construct ring spatial variables for modeling and mapping spatial distribution of dust in snow cover
1
Institute of Industrial Ecology UB RAS, S. Kovalevskoy Str., 20, 620990, Ekaterinburg, Russia
2
Ural Federal University, Mira Str., 19, 620002, Ekaterinburg, Russia
Received:
27
August
2024
Accepted:
16
September
2024
Published online:
2
October
2024
One of the effective approaches to modeling the spatial distribution of a feature is the land use regression (LUR) method, which consists of constructing a mathematical model based on experimental data and geographic information systems (GIS) data. This approach makes it possible to obtain estimates over a large area with high spatial resolution (about 10 m) at relatively low financial and time costs. In our work, we propose to use the improved Land Use (LU) methodology to construct ring spatial variables for modeling and mapping spatial distribution of dust in snow cover of an urbanized area. Using LU, we created two models based on classical regression and four based on artificial neural networks (two based on multilayer perceptron and two based on convolutional neural networks). Data for the study were obtained during screening of snow cover in the city of Novy Urengoy (Russia). We used eight indices to assess the quality and accuracy of the models. The work provides a Taylor diagram for a visual representation of the results of the models. Also, based on the obtained results, detailed maps of the spatial distribution of dust in the snow cover in the study area were constructed, which make it possible to visualize in high detail the places most susceptible to dust pollution. All models turned out to be quite effective. The models based on artificial neural networks (ANN) were most accurate. The accuracy of ANN-based models increased from 3 to 26% depending on the index.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
