Computer Science – Performance
Scientific paper
Mar 2008
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008p%26ss...56..438u&link_type=abstract
Planetary and Space Science, Volume 56, Issue 3-4, p. 438-447.
Computer Science
Performance
Scientific paper
Regression equations for predicting thermal conductivity based on easily measured or available porosity, penetration resistance and content of sand with respect to variously textured terrestrial soils, and porosity and hardness with respect to snow are presented. It is shown that with respect to single soils the performance of regression equations based on penetration resistance and porosity was quite satisfactory (R2=0.923 0.968, RMSE=0.067 0.138 W m 1 K 1) and was improved after adding a content of sand (R2=0.958 0.968, RMSE=0.055 0.102 W m 1 K 1). When the data of all the soils were analysed together, the performance of the regression equations based on penetration resistance and porosity was unsatisfactory (R2=0.399) and was substantially improved after adding the content of sand (R2=0.946). The performance of the regression equations for snow thermal conductivity based on the index of hardness and porosity was better (R2=0.657) than that based only on density (R2=0.501) [Sturm, M., Perovich, D.K., Holmgren, J., 2002. Thermal conductivity and heat transfer through the snow on the ice of the Beaufort Sea. J. Geophys. Res. 107 (C21), 8043]. The suggested regression equations can help in minimising uncertainties associated with measurements in extraterrestrial media.
Lipiec J.
Usowicz B.
Usowicz Jerzy B.
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