Volume 1, Issue 2 — Year 2024 — Article e100018

ISSN (Online): 3115-8129 Biannually

Flow-Sediment Dynamics Analysis in Circular Channels using Gaussian and ANFIS based Systems

Article Type: Research
Pages: e100018
DOI: https://doi.org/10.22034/CGEL.1.2.e100018
Crossmark
Open Access
Authors
Affiliations
  1. Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz 5158913791, Iran
  2. Robotics & Soft Technologies Research Center, Tabriz Branch, Islamic Azad University, Tabriz 5158913791, Iran
Corresponding author
Email: vafaei@iau.ac.ir
ORCID: 0000-0002-9112-2403
Received: 11 August 2024 / Accepted: 15 October 2024 / Published: 25 December 2024
Abstract

This study develops Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Gaussian Process Regression (GPR) models to estimate sediment transport in circular channels used in water and wastewater systems and compares their performance with two widely applied machine-learning techniques: Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The modeling framework incorporates key hydraulic and sediment parameters, including shear velocity, hydraulic radius, particle-size ratios, and mobility indices, to explore their nonlinear influence on sediment discharge. The results show that ANFIS and GPR consistently outperform ANN and SVM, offering more stable and reliable predictions across both smooth and rough bed conditions. ANFIS demonstrates the highest accuracy due to its hybrid learning structure, while GPR provides strong generalization with minimal overfitting. ANN delivers moderate accuracy but is highly sensitive to data variability, whereas SVM tends to underpredict sediment transport, particularly at higher flow intensities. The best-performing models for ANFIS and GPR were achieved using the input set (u*, d₅₀/R, λs, and Fd), highlighting the strong interaction between flow intensity and particle mobility. Sensitivity analysis identifies the particle Froude number (Fd) as the most influential parameter. Additionally, smoother channel beds improve prediction accuracy for all models, while increased roughness reduces sediment transport efficiency and introduces higher predictive uncertainty.

Keywords

Sediment transport, Closed-conduit flow, Data-driven modeling, Flow–particle interactions, Bed surface condition

Data availability statement

The data supporting the findings of this study are available within article. No publicly archived dataset was generated.

Funding

This research received no external funding.

Highlights
  • ANFIS and GPR outperform ANN and SVM models
  • ANFIS provides highest accuracy in sediment prediction
  • Flow and particle parameters strongly influence transport
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How to cite
Poursorkhabi, R. V., & Naseri, A. (2024). Flow-Sediment Dynamics Analysis in Circular Channels using Gaussian and ANFIS based Systems. Civil and Geoengineering Letters, 1(2), e100018. https://doi.org/10.22034/CGEL.1.2.e100018
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