Comparative analysis of seven machine learning algorithms and five empirical models to estimate soil thermal conductivity

•The accuracy of 7 ML algorithms and 5 thermal conductivity models was evaluated.•The average RMSE values of ML algorithms were 66%∼82% of the empirical model values.•Soil moisture and bulk density together had > 80% of the feature importance value. Soil thermal conductivity (λ) is an important t...

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Vydáno v:	Agricultural and forest meteorology Ročník 323; s. 109080
Hlavní autoři:	Zhao, Tianyue, Liu, Shuchao, Xu, Jia, He, Hailong, Wang, Dong, Horton, Robert, Liu, Gang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 15.08.2022
Témata:	decision support systems Empirical model empirical models energy balance Feature importance ranking forests Machine learning meteorology normal distribution Random forest regression analysis soil density Soil thermal conductivity soil water content thermal conductivity Thermal conductivity model NN LR GP Random forest GBDT KNN Empirical model Feature importance ranking DT RF Soil thermal conductivity Thermal conductivity model Machine learning ML
ISSN:	0168-1923, 1873-2240
On-line přístup:	Získat plný text
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