Vol. 12, Special Issue 6 (2023)

The foundation for understanding diverse soil processes is how soil particles work. One of the fundamental and unchanging physical characteristics of soil that is intrinsic is its PSD. By defining the PSD using mathematical models, one can acquire a better understanding of soil texture. The aim of this study was to evaluate the fitting ability of 4 models i.e. the Skaggs, Fooladmand, Fredlund, and Gray model on 30 soil samples from Pune district, Maharashtra and to determine the best model among them for the PSD of silty clay and silty clay loam texture soil samples. PSD models were fitted to experimental data using evaluation criterions like root mean square error (RMSE), coefficient of determination (R2), adjusted coefficient of determination (Adj-R2), Akaike's information criterion (AIC), mean absolute error (MAE), mean absolute percentage error (MAPE) and Chi- square. The result showed that the Fooladmand model were the best suited models for predicting the particle size distribution of silty clay texture soils while In silty clay loam soil texture of Pune district Fredlund model is best fitted for prediction of PSD.