Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F20%3A83590" target="_blank" >RIV/60460709:41210/20:83590 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2073-445X/9/12/487" target="_blank" >https://www.mdpi.com/2073-445X/9/12/487</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/land9120487" target="_blank" >10.3390/land9120487</a>

Result language
angličtina
Original language name
Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil
Original language description
Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley-Black method. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), element ratios, elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetat
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
40104 - Soil science

Project
<a href="/en/project/EF16_019%2F0000845" target="_blank" >EF16_019/0000845: Centre for investigation of synthesis and transformation of nutritional substances in the food chain in interaction with potentially harmful substances of athropogenic origin: assessment of contamination risks for the quality of production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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