Performance Evaluation of MLP and RBF Neural Networks to Estimate the Soil Saturated Hydraulic Conductivity

Abstract

Soil saturated hydraulic conductivity is considered one of the physical soil properties that is very important in modeling of water movement and environmental studies. This study aimed to compare the performance of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) in neural networks for estimation of the soil saturated hydraulic conductivity. For this, the data of 27 drilled cased borehole permeameter with three kinds of geometry water flow through the soils and the soil texture properties were used as the input parameters for models. The effectiveness of neural networks to estimate the soil saturated hydraulic conductivity were calculated and compared based on mean squared error (MSE), root mean squared error (RMSE) and coefficient determination (R2). According to the above indicators, for all three types of drilled cased borehole permeameter surveyed in this study, the results show MLP neural networks had better performance than RBF neural networks in estimation of the soil saturated hydraulic conductivity and for wells with the horizontal, vertical and horizontal-vertical flow, which the amount of coefficient determination were respectively for all of them 0.94, 0.97 and 0.85.