Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms.

Enes Gul,Ali Danandeh Mehr,Ali Torabi Haghighi,Mir Jafar Sadegh Safari,Zaher Mundher Yaseen

doi:10.1371/journal.pone.0258125

Enes Gul, Ali Danandeh Mehr + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0258125

Copy DOI

Abstract

To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow.

Highlights

For the hydraulic design of urban drainage systems, the sediment transport process must be considered
While most of the studies in the literature used a few data sets for modeling sediment transport at non-deposition with clean bed condition, this study extends the available studies in the literature through utilizing wider range of experimental data taken from six sources which cover wide ranges of channel size and shape, sediment median size and concentration and flow depth
The six existing data sets with a wide range compiled from the literature are used for model development

Summary

Introduction

For the hydraulic design of urban drainage systems, the sediment transport process must be considered. [16] performed experiments in circular cross-sectional shape with two channels and three different granular materials with sizes between 0.2–0.43 mm. It is demonstrated by [1, 2] that four characteristics of fluid, flow, channel, and sediment must be embedded to a sediment transport model. Ð6Þ where V is flow velocity, g is gravity acceleration, ρ is fluid specific mass, υ is fluid kinematic viscosity, R is hydraulic radius, d is median size of sediment, λ is channel friction factor, Cv is sediment volumetric concentration, and ρs is sediment specific mass These parameters can be considered effective sediment transport variables for the modeling. Kurtosis 5.6288 3.4910 7.2243 5.0678 3.8024 and test data does not make a major difference to model performance; the best data split rate was reported as 70% and 30% for training and testing periods

M5P classifiers

Performance criteria

Discussions

Conclusions