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Position: Home > Articles > Evaluation of Different Predictor Models for Detailed Soil Particle-Size Distribution Pedosphere 2018,28 (1)

Evaluation of Different Predictor Models for Detailed Soil Particle-Size Distribution

作  者：
Fatemeh Meskini-Vishkaee;Naser Davatga
单  位：
Soil and Water Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), 61335-3341 Ahvaz (Iran);Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), 3177993545 Karaj (Iran
关键词：
model;psd;gm (1,1);ps;data;textura
摘  要：
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.

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