Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F17%3A73491" target="_blank" >RIV/60460709:41210/17:73491 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.cageo.2017.04.008" target="_blank" >http://dx.doi.org/10.1016/j.cageo.2017.04.008</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cageo.2017.04.008" target="_blank" >10.1016/j.cageo.2017.04.008</a>

Result language
angličtina
Original language name
Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation
Original language description
A myriad of signal pre-processing strategies and multivariate calibration techniques has been explored in attempt to improve the spectroscopic prediction of soil organic carbon (SOC) over the last few decades. Therefore, to come up with a novel, more powerful, and accurate predictive approach to beat the rank becomes a challenging task. However, there may be a way, so that to combine several individual predictions into a single final one (according to ensemble learning theory). As this approach performs best when combining in nature different predictive algorithms that are calibrated with structurally different predictor variables, we tested predictors of two different kinds: 1) reflectance values (or transforms) at each wavelength and 2) absorption feature parameters. Consequently we applied four different calibration techniques, two per each type of predictors: a) partial least squares regression and support vector machines for type 1, and b) multiple linear regression and random forest fo
Czech name
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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/QJ1230319" target="_blank" >QJ1230319: Soil water regime within a sloping agricultural area</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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