An accurate mathematical representation of soil particle-size distribution(PSD) is required to estimate soil hydraulic properties or to compare texture measurements using different classification systems. However, man...An accurate mathematical representation of soil particle-size distribution(PSD) is required to estimate soil hydraulic properties or to compare texture measurements using different classification systems. However, many databases do not contain full PSD data,but instead contain only the clay, silt, and sand mass fractions. The objective of this study was to evaluate the abilities of four PSD models(the Skaggs model, the Fooladmand model, the modified Gray model GM(1,1), and the Fredlund model) to predict detailed PSD using limited soil textural data and to determine the effects of soil texture on the performance of the individual PSD model.The mean absolute error(MAE) and root mean square error(RMSE) were used to measure the goodness-of-fit of the models, and the Akaike's information criterion(AIC) was used to compare the quality of model fits. The performance of all PSD models except the GM(1,1) improved with increasing clay content in soils. This result showed that the GM(1,1) was less dependent on soil texture.The Fredlund model was the best for describing the PSDs of all soil textures except in the sand textural class. However, the GM(1,1) showed better performance as the sand content increased. These results indicated that the Fredlund model showed the best performance and the least values of all evaluation criteria, and can be used using limited soil textural data for detailed PSD.展开更多
基金supported by the Rice Research Institute, Rasht of Iran
文摘An accurate mathematical representation of soil particle-size distribution(PSD) is required to estimate soil hydraulic properties or to compare texture measurements using different classification systems. However, many databases do not contain full PSD data,but instead contain only the clay, silt, and sand mass fractions. The objective of this study was to evaluate the abilities of four PSD models(the Skaggs model, the Fooladmand model, the modified Gray model GM(1,1), and the Fredlund model) to predict detailed PSD using limited soil textural data and to determine the effects of soil texture on the performance of the individual PSD model.The mean absolute error(MAE) and root mean square error(RMSE) were used to measure the goodness-of-fit of the models, and the Akaike's information criterion(AIC) was used to compare the quality of model fits. The performance of all PSD models except the GM(1,1) improved with increasing clay content in soils. This result showed that the GM(1,1) was less dependent on soil texture.The Fredlund model was the best for describing the PSDs of all soil textures except in the sand textural class. However, the GM(1,1) showed better performance as the sand content increased. These results indicated that the Fredlund model showed the best performance and the least values of all evaluation criteria, and can be used using limited soil textural data for detailed PSD.
