Consequences of spatial structure in soil-geomorphic data on the results of machine learning models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027073%3A_____%2F23%3AN0000016" target="_blank" >RIV/00027073:_____/23:N0000016 - isvavai.cz</a>
Alternative codes found
RIV/62156489:43410/23:43923822
Result on the web
<a href="https://www.tandfonline.com/doi/full/10.1080/10106049.2023.2245381" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/10106049.2023.2245381</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/10106049.2023.2245381" target="_blank" >10.1080/10106049.2023.2245381</a>

Result language
angličtina
Original language name
Consequences of spatial structure in soil-geomorphic data on the results of machine learning models
Original language description
In this paper, we examined the degree to which inherent spatial structure in soil properties influences the outcomes of machine learning (ML) approaches to predicting soil spatial variability. We compared the performances of four ML algorithms (support vector machine, artificial neural network, random forest, and random forest for spatial data) against two non-ML algorithms (ordinary least squares regression and spatial filtering regression). None of the ML algorithms produced residuals that had lower mean values or were less autocorrelated over space compared with the non-ML approaches. We recommend the use of random forest when a soil variable of interest is weakly autocorrelated (Moran's I < 0.1) and spatial filtering regression when it is relatively strongly autocorrelated (Moran's I > 0.4). Overall, this work opens the door to a more consistent selection of model algorithms through the establishment of threshold criteria for spatial autocorrelation of input variables.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)

Project
<a href="/en/project/SS02030018" target="_blank" >SS02030018: Center for Landscape and Biodiversity</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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