Prediction of hydraulic conductivity from the soil grain size data using SICM intelligent model

Document Type : Research paper

Authors

1 Ph.D Candidate, Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

2 Assistant Professor, Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

3 Associate Professor, Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

10.22034/hydro.2022.13884

Abstract

Permeability is one of the parameters affecting water flow in porous environments such as rock and soil mass and is of special importance in geotechnical studies, e.g., the location of important structures such as urban trains. Accordingly, determination of permeability is one of the main goals in geotechnical studies. Also, it is an important parameter in solving geotechnical problems such as seepage, settlement measurement, stability analysis, etc. Due to fact that direct methods (field and laboratory) of measuring permeability are expensive, highly specialized, time-consuming and unreliable, and due to the nonlinear behavior and heterogeneous and anisotropic conditions in hydrogeological environments which cause inherent uncertainty in the methods of direct measurement of this parameter, various artificial intelligence methods which work more accurately than the above methods  have recently been proposed  to compensate for some of these shortcomings. In this study, two individual artificial intelligence methods including Least-Squares Support Vector Machine (LSSVM) and Wavelet-Artificial Neural Network (WANN) were used on lines 1 and 2 of Tabriz Urban Train to predict hydraulic conductivity based on grain size data; then the results of these two individual models were combined by an Artificial Neural Network (ANN) and improved the results under the name of Supervised Intelligent Committee Machine (SICM). Comparison of test step results of the three models presented in this study showed that all the three models had a relatively good performance in predicting hydraulic conductivity, but SICM model with Root Mean Squared Error (RMSE)= 0.000161 cm/sec and Determination Coefficient (R2)= 0.83 provided better results than the individual models.

Keywords

References

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Hydrogeology
Volume 7, Issue 1
Publisher
September 2022
Pages 69-80

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