ArticleEnvironmental monitoring and assessment2021
Comparison of Cubist models for soil organic carbon prediction via portable XRF measured data.
Article in Environmental monitoring and assessment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses.Scientific reports · 2025Article
- Prediction on the spatial distribution of the seropositive rate of schistosomiasis in Hunan Province, China: a machine learning model integrated with the Kriging method.Parasitology research · 2024Article
- Improved soil carbon stock spatial prediction in a Mediterranean soil erosion site through robust machine learning techniques.Environmental monitoring and assessment · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Soil organic carbon (SOC) tends to form complexes with most metallic ions within the soil system. Relatively few studies compare SOC predictions via portable X-ray fluorescence (pXRF) measured data coupled with the Cubist algorithm. The current study applied three different Cubist models to estimate SOC while using several pXRF measured data. Soil samples (n = 158) were collected from the Litavka floodplain area during two separate sampling campaigns in 2018. Thirteen pXRF data or predictors (K, Ca, Rb, Mn, Fe, As, Ba, Th, Pb, Sr, Ti, Zr, and Zn) were selected to develop the proposed models. Validation and comparison of the models applied the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R
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