Journal of Advanced Informatics in Water, Soil, and Structure

Journal of Advanced Informatics in Water, Soil, and Structure

Analyzing the Uncertainty of Artificial Neural Network Models and Support Vector Machines in Estimating Saturated Hydraulic Conductivity

Document Type : Research Article

Authors
Amir Nabizadeh 1
abbas khashei 2
Peyman Afrasiab 1
mohsen pourreza bilondi 2
1 Department of Water Engineering, University of Zabol, Zabol. Iran.
2 Department of Water Engineering, University of Birjand , Birjand . Iran.
10.22048/wss.2025.450399.1011
Abstract
Data-basis simulation models such as artificial neural network (ANN) and support vector machines (LS-SVM) are suitable substitute for expensive and laboratory methods of estimating soil hydraulic properties, including hydraulic saturation conductivity of soil. In this study, the objective is to predict the saturated hydraulic conductivity of soil in the study area, Sistan Dam, using ANN and LS-SVM. To this purpose, the saturated hydraulic conductivity of soil was measured in 112 points by the suction disc method. For selecting input variables among simple characteristics, the multistep regression method was used. In these models, the selection of different transfer and training functions represents a significant source of error. Consequently, a comprehensive analysis was conducted to identify uncertainty sources in simulating the saturated hydraulic conductivity of soil. To determine the best simulation model among neural network functions and support vector machines, a Monte Carlo method was employed to sample and evaluate their performance. This rigorous approach ensures a thorough examination of the strengths and limitations of these models, thereby enhancing the reliability and accuracy of soil conductivity predictions. Finally, the uncertainty in predicting the solution model can be analyzed to determine how much ANN models and support vector machines (LS-SVMs) are reliable. In general, when considering both linear and nonlinear scenarios in ANNs and LS-SVM, linear input methods may not be a suitable approach. This is primarily due to the complex and nonlinear nature of the soil properties and processes being modeled. Therefore, nonlinear input methods are typically preferred to accurately capture the intricate relationships and dynamics of soil systems. This highlights the importance of selecting appropriate input methods to ensure reliable and accurate modelling of soil properties and behaviour. In addition, among the ANN and LS-SVM models used in this study, ANN, with values of content ratio criteria (CR), (with the values of 90, 0.263, and 0.740) high degree of asymmetry index (S) and high and low asymmetry index (T), was of higher certainty and accuracy than the other models. Moreover, Logsig_trainlm and Tansig_trainbfg scenarios in predicting saturated hydraulic conductivity had a satisfactory process and less uncertainty.
Keywords
Uncertainty
Tension disk
Transitional Functions
Monte Carlo
Evaluation Criteria

Subjects

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  • Receive Date 31 March 2024
  • Revise Date 06 January 2025
  • Accept Date 06 January 2025
  • First Publish Date 11 January 2025
  • Publish Date 01 May 2025