Estimation of Saturation Percentage of Soil Using Multiple Regression, ANN, and ANFIS Techniques

The saturation percentage (SP) of soils is an important index in hydrological studies. In this paper, artificial neural networks (ANNs), multiple regression (MR), and adaptive neural-based fuzzy inference system (ANFIS) were used for estimation of saturation percentage of soils collected from Boukan region in the northwestern part of Iran. Percent clay, silt, sand and organic carbon (OC) were used to develop the applied methods. In additions contributions of each input variable were assessed on estimation of SP index. Two performance functions, namely root mean square errors (RMSE) and determination coefficient (R), were used to evaluate the adequacy of the models. ANFIS method was found to be superior over the other methods. It is, then, proposed that ANFIS model can be used for reasonable estimation of SP values of soils.