Evaluation of Adaptive Neuro Fuzzy Inference System and Regression-Based Pedo Transfer Functions for Estimating Saturated Hydraulic Conductivity

Document Type : Research Paper

Author

Assistance Professor, Faculty of Agriculture and Environmental Science, University of Mohaghegh, Ardabili, Iran

Abstract

Measurement of soil hydraulic properties such as soil saturated hydraulic conductivity is one of the most important physical properties of soil. In this study, adaptive neuro fuzzy inference system (ANFIS) and pedo-transfer functions (PTFs) are used to estimate soil saturated hydraulic conductivity. The model inputs included soil texture, the percent of silt, clay, and sand. To evaluate the performance of the model, parameters of root mean square error (RMSE), percentage of relative error (ε), mean absolute error (MAE) and the coefficient of determination (R2) were used, which for (ANFIS) model were determined as 0.557, 0.627%, 0.844, and 0.997, respectively. Accuracy of PTFs methods decreased from Ferrer-Julià (2004), Roseta (UNSODA(lab)-SSC), Dane (1994), Cosby (1984), Puckett (1985), and Campbell (1994). Among PTFs methods, Ferrer-Julià (2004) had more accuracy with a regression coefficient of (R2 =0.89) and (RMSE =2.1). Performance evaluation of the models showed that the ANFIS model compared with PTFs was able to predict soil hydraulic conductivity with more accuracy.

Keywords

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References

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Iranian Journal of Soil Research
Volume 35, Issue 4
February 2022
Pages 413-428

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