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

被引:0
|
作者
Fatemeh MESKINI-VISHKAEE [1 ]
Naser DAVATGAR [2 ]
机构
[1] Soil and Water Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO)  2. Soil and Water R
关键词
Akaike’s information criterion; Fredlund model; Gray model; mean absolute error; root mean square error; soil texture;
D O I
暂无
中图分类号
S152 [土壤物理学];
学科分类号
0903 ; 090301 ;
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.
引用
收藏
页码:157 / 164
页数:8
相关论文
共 50 条
  • [1] Evaluation of Different Predictor Models for Detailed Soil Particle-Size Distribution
    Fatemeh MESKINIVISHKAEE
    Naser DAVATGAR
    [J]. Pedosphere, 2018, 28 (01) - 164
  • [2] Evaluation of Different Predictor Models for Detailed Soil Particle-Size Distribution
    Meskini-Vishkaee, Fatemeh
    Davatgar, Naser
    [J]. PEDOSPHERE, 2018, 28 (01) : 157 - 164
  • [3] Evaluation of parameter models for estimating loess soil particle-size distribution
    Zhao, Aihui
    Huang, Mingbin
    Shi, Zhuye
    [J]. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2008, 24 (01): : 1 - 6
  • [4] BUILDING PREDICTIVE MODELS OF SOIL PARTICLE-SIZE DISTRIBUTION
    Samuel-Rosa, Alessandro
    Diniz Dalmolin, Ricardo Simao
    Miguel, Pablo
    [J]. REVISTA BRASILEIRA DE CIENCIA DO SOLO, 2013, 37 (02): : 422 - 430
  • [5] Evaluation of different representations of the particle-size distribution to predict soil water retention
    Nemes, A
    Rawls, WJ
    [J]. GEODERMA, 2006, 132 (1-2) : 47 - 58
  • [6] Evaluation of models for fitting soil particle-size distribution using UNSODA and a Brazilian dataset
    Vaz, Carlos Manoel Pedro
    Ferreira, Ednaldo Jose
    Posadas, Aldolfo Durand
    [J]. GEODERMA REGIONAL, 2020, 21
  • [7] Effect of texture on the performance of soil particle-size distribution models
    Hwang, SI
    [J]. GEODERMA, 2004, 123 (3-4) : 363 - 371
  • [8] FRACTAL FEATURES OF SOIL PARTICLE-SIZE DISTRIBUTION AS INFLUENCED BY SOIL PARTICLE-SIZE CLASSIFICATION SYSTEMS
    Deng, Jifeng
    Ma, Chengzhong
    [J]. FRESENIUS ENVIRONMENTAL BULLETIN, 2019, 28 (11A): : 8949 - 8952
  • [9] Using particle-size distribution models to estimate soil hydraulic properties
    Hwang, SI
    Powers, SE
    [J]. SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 2003, 67 (04) : 1103 - 1112
  • [10] PARTICLE-SIZE AND PARTICLE-SIZE DISTRIBUTION OF WHEAT SAMPLES PREPARED WITH DIFFERENT GRINDERS
    WARD, AB
    SHELLENBERGER, JA
    WETZEL, DL
    [J]. CEREAL CHEMISTRY, 1979, 56 (05) : 434 - 436