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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Bibliographic Details
Published in:	Agricultural and forest meteorology Vol. 323; p. 109080
Main Authors:	Zhao, Tianyue, Liu, Shuchao, Xu, Jia, He, Hailong, Wang, Dong, Horton, Robert, Liu, Gang
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 15.08.2022
Subjects:	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
Online Access:	Get full text
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