Applying of an adaptive neuro fuzzy inference system for prediction of unsaturated soil hydraulic conductivity

The unsaturated hydraulic conductivity of soil (Ku) is one of the most principal parameters in the study of water movement in the soil. The field measurement methods of (Ku) are hard and expensive. So, indirect prediction of (Ku) has received considerable attention as published in the research papers to be an alternative approach. However, prediction models for soil hydraulic conductivity are now widely used informative tools for rapid and cost-effective assessment. Thus in this study, an attempt has been made to apply an adaptive neuro-fuzzy inference system (ANFIS) for predicting (Ku). The input variables were ECRatio (electric conductivity of water divided by electric conductivity of soil), SARRatio (sodium adsorption ratio of water divided by sodium adsorption ratio of soil), soil texture index (calculated from clay, sand and silt), suction rate, organic matter in the soil, initial soil moisture content and initial soil bulk density. The Gaussian membership function was the best for the input variables. The Hybrid learning was selected for predicting (Ku) with ANFIS. Three performance functions namely; root mean squared error (RMSE), mean error (ME) and coefficient of determination (R2), were used to evaluate the predictive capability of the suggested (ANFIS). The obtained results for testing data (9 points) indicated that the R2 values relating predicted versus measured estimates of (Ku) was 0.783, ME was found to be 0.118 cm/sec and RMSE was found to be 0.472 cm/sec. As a result, it appears that applying ANFIS suggests a new approach for determining (Ku) along with saving time and cost.