Efficiency of the t-distribution stochastic neighbor embedding technique for detailed visualization and modeling interactions between agricultural soil quality indicators

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F21%3A85810" target="_blank" >RIV/60460709:41210/21:85810 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1537511021002178?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1537511021002178?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.biosystemseng.2021.08.033" target="_blank" >10.1016/j.biosystemseng.2021.08.033</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficiency of the t-distribution stochastic neighbor embedding technique for detailed visualization and modeling interactions between agricultural soil quality indicators

Popis výsledku v původním jazyce

Dimensionality reduction is important for revealing important details that may be useful in decision making. Although different dimensionality reduction methods have been applied in several soil based studies, Kohonen self-organizing map neural network (KSOM-NN) has attracted significant attention from researchers because of the quality of data visualization and interpretation. However, there is a dearth of studies that compare KSOM-NN and other robust data reduction techniques such as the t distribution stochastic neighbor embedding (t-SNE) method to improve visualization and interpretation of the relationships between soil quality indicators in agricultural soil. This study compares the above mentioned methods for characterizing soil quality indicators including particle size distribution, soil organic matter SOM, cation exchange capacity CEC, soil reaction pH, electrical conductivity EC, zinc Zn, iron Fe, manganese Mn, potassium K and phosphorus P in agricultural dryland. There were strongly posit

Název v anglickém jazyce

Efficiency of the t-distribution stochastic neighbor embedding technique for detailed visualization and modeling interactions between agricultural soil quality indicators

Popis výsledku anglicky

Dimensionality reduction is important for revealing important details that may be useful in decision making. Although different dimensionality reduction methods have been applied in several soil based studies, Kohonen self-organizing map neural network (KSOM-NN) has attracted significant attention from researchers because of the quality of data visualization and interpretation. However, there is a dearth of studies that compare KSOM-NN and other robust data reduction techniques such as the t distribution stochastic neighbor embedding (t-SNE) method to improve visualization and interpretation of the relationships between soil quality indicators in agricultural soil. This study compares the above mentioned methods for characterizing soil quality indicators including particle size distribution, soil organic matter SOM, cation exchange capacity CEC, soil reaction pH, electrical conductivity EC, zinc Zn, iron Fe, manganese Mn, potassium K and phosphorus P in agricultural dryland. There were strongly posit

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biosystems Engineering

ISSN

1537-5110

e-ISSN

1537-5129

Svazek periodika

210

Číslo periodika v rámci svazku

oct

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

282-298

Kód UT WoS článku

000697667200004

EID výsledku v databázi Scopus

2-s2.0-85114776197