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.

Predicting saturated and near-saturated hydraulic conductivity using artificial neural networks and multiple linear regression in calcareous soils

“…In accordance with our results, Moosavi et al [ 24 ] in soils of Fars Province, Iran, stated the capability of different approaches to predict sorptivity coefficient could be ranked as MLPNNs > RBFNNs > MLR regarding their accuracies and computational times. Furthermore, Shams Emamzadeh et al [ 91 ] in soils of Tehran Province, Iran, found MLPNNs predicted K 0 was more accurate than that of RBFNNs. While, Rezaei Arshad et al [ 36 ], reported more capability of RBFNNs to predict K 0 compared to MLPNNs and MLR methods in soils of Khuzestan Province, Iran.…”

Estimating Soil Water Retention Curve by Extreme Learning Machine, Radial Basis Function, M5 Tree and Modified Group Method of Data Handling Approaches

“…The result of the RBF in the present study is in agreement with the results of Gao and Liu (2010), who successfully applied the RBF network method for the prediction of the SWRC. Many researchers have applied the RBF method to predict the SWRC (Achieng, 2019;Bayat et al, 2011;Bayat, Neyshaburi, Mohammadi, Nariman-Zadeh, Irannejad, & Gregory, 2013), soil saturated hydraulic conductivity (Emamzadeh et al, 2016), specific surface area (Bayat, Ersahin, & Hepper, 2013) and soil cation exchange capacity (CEC; Ghorbani et al, 2015). Also, Alavi et al (2009) utilized the RBF method for predicting proctor parameters.…”