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<p>This study investigates sediment transport in circular pipes using Gaussian Process Regression (GPR) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The models evaluate how hydraulic parameters and sediment characteristics influence sediment discharge. Among various input combinations, the model with λs, Fd, Dgr, and d50/R achieved the highest accuracy under both smooth and rough pipe conditions. Sensitivity analysis identified the particle Froude number (Fd) as the most influential factor. Predictions were more accurate for smoother pipe beds, as increased roughness reduced sediment transport efficiency. These findings provide a reliable, data-driven approach for estimating sediment transport in water and wastewater systems. </p>

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Data-driven study of flow & sediment in circular channels using GPR & ANFIS

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月12日

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  • Ramin Vafaei Poursorkhabi

摘要

This study investigates sediment transport in circular pipes using Gaussian Process Regression (GPR) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The models evaluate how hydraulic parameters and sediment characteristics influence sediment discharge. Among various input combinations, the model with λs, Fd, Dgr, and d50/R achieved the highest accuracy under both smooth and rough pipe conditions. Sensitivity analysis identified the particle Froude number (Fd) as the most influential factor. Predictions were more accurate for smoother pipe beds, as increased roughness reduced sediment transport efficiency. These findings provide a reliable, data-driven approach for estimating sediment transport in water and wastewater systems.