Evaluation of adaptive neural-based fuzzy inference system approach for estimating saturated soil water content

Abstract

The saturated soil water content (θs) is an important parameter in hydrological studies. In this paper, adaptive neural-based fuzzy inference system (ANFIS) was used for estimation of soil saturation percentage of some flood spreading areas in Iran. Soil particle size distribution (sand%, silt%, and clay%), bulk density and medium porosity (0.2–30 µm) were used to develop saturated soil water content pedotransfer functions (PTFs). Then, contributions of various member functions (MFs) were assessed on estimation of θs. The results showed that the member function type has an important role in performance of ANFIS approach. In the present investigation, Gaussian curve (gaussmf) was found to be superior over the other MFs in estimating θs. In all of the θs PTFs, correlation between estimations of θs and corresponding observations was the low. R2 values between measured and PTF-estimated θs using ANFIS approach did not increase as some input predictors were used in the PTFs (from PTF1 to PTF5). Based on the results, it is suggested that ANFIS model can be applied for reasonable estimation of θs and there is a need for obtain more information of the proposed approach especially for the selection of best member functions. Therefore, a good performance may be obtained when best member function would be selected in addition to the more effective PTF inputs.