首页|Studies from Czech University of Life Sciences Prague Provide New Data on Machine Learning (Digital Soil Mapping Using Machine Learning-based Methods To Predict Soil Organic Carbon In Two Different Districts In the Czech Republic)
Studies from Czech University of Life Sciences Prague Provide New Data on Machine Learning (Digital Soil Mapping Using Machine Learning-based Methods To Predict Soil Organic Carbon In Two Different Districts In the Czech Republic)
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NETL
NSTL
Czech Academy Agricultural Sciences
Fresh data on Machine Learning are presented in a new report. According to news reporting out of Prague, Czech Republic, by NewsRx editors, research stated, “Soil organic carbon (SOC) is an important soil characteristic as well as a way how to mitigate climate change. Information on its content and spatial distribution is thus crucial.” Funders for this research include Czech University of Life Sciences Prague, Prague, Technology Agency of the Czech Republic, Ministry of Agriculture, Czech Republic. Our news journalists obtained a quote from the research from the Czech University of Life Sciences Prague, “Digital soil mapping (DSM) is a suitable way to evaluate spatial distribution of soil properties thanks to its ability to obtain accurate information about soil. This research aims to apply machine learning algorithms using various environmental covariates to generate digital SOC maps for mineral topsoils in the Liberec and Domazlice districts, located in the Czech Republic. The soil class, land cover, and geology maps as well as terrain covariates extracted from the digital elevation model and remote sensing data were used as covariates in modelling. The spatial distribution of SOC was predicted based on its relationships with covariates using random forest (RF), cubist, and quantile random forest (QRF) models. Results of the RF model showed that land cover (vegetation) and elevation were the most important environmental variables in the SOC prediction in both districts. The RF had better efficiency and accuracy than the cubist and QRF to predict SOC in both districts. The greatest R2 value (0.63) was observed in the Domazlice district using the RF model.”
PragueCzech RepublicEuropeCyborgsEmerging TechnologiesMachine LearningCzech University of Life Sciences Prague
NETL
NSTL
Czech Academy Agricultural Sciences
