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Adaptive Neuro Fuzzy Inference System and Multilayer Perceptron Neural Networks to Estimate Saturated Hydraulic Conductivity by Soil Texture A Case Study for Fath-Ali Irrigation Network in Moghan Plain | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| Article 4, Volume 16, Issue 65, January 2016, Pages 37-54 PDF (559.35 K) | ||
| Document Type: Original Article | ||
| DOI: 10.22092/aridse.2016.105700 | ||
| Abstract | ||
| Direct measurement of soil hydraulic conductivity is time-consuming and expensive. Direct measurement of soil hydraulic properties can be replaced by simple measurement of properties such as soil texture and bulk density using transfer functions and an adaptive neuro fuzzy inference system (ANFIS). The present study used ANFIS and neural network models to estimate saturated soil hydraulic conductivity. The model inputs included percentage of silt, clay, and sand. The architecture for this network contained 3 neurons in the input layer and 11 neurons in the hidden layer using the tangent sigmoid transfer function, and an output layer of neurons with a linear transfer function and 1000 iterations. In all networks, the learning rate and momentum was 0.3. The neuro fuzzy inference system had 27 rules, a Gaussian membership function was used for input data, and a hybrid method was used to optimize the ANFIS model. The root mean square error (mmd-1), percentage of relative error (ε), mean absolute error (cmd-1), coefficient of residual mass, efficiency, and coefficient of determination were used to evaluate the performance of the model. For the ANFIS model, these values were 0.032, 0.62%, 0.18, -0.0000023, 0.999, and 0.997, respectively. The values for the Levenberg-Marquardt training algorithm were 1.22, 1.44%, 1.21, -0.00015, 0.997, and 0.99, respectively. Performance evaluation of the models showed that the ANFIS model predicted soil hydraulic conductivity with greater accuracy than did the neural network and the results of this method were closer to actual measurement results. | ||
| Keywords | ||
| ANFIS; ANN; Estimation; Saturated hydraulic conductivity; Soil Gradation | ||
| References | ||
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Yilmaz, I. and Kaynar, O. 2011. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst. Appl. 38(5): 5958-5966. | ||
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