Developing a Lagrangian sediment transport model for open channel flows

Baharvand, Saman; Ahmari, Habib; Taghvaei, Poorya

doi:10.1016/j.ijsrc.2022.09.003

Cited by 8 publications

(13 citation statements)

References 37 publications

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“…The two main steps of this study were the development and evaluation of dispersion models for natural streams using soft computing techniques, and combining learning-based dispersion models with a particle tracking model developed by Baharvand et al (2023a). Field data from previous studies were used to develop machine learning models that could predict longitudinal and The model architecture was programmed in Python 3.9.0, a high-level, general-purpose programming language (Rossum, 1995), using several Python-based packages such as NumPy, SciPy, Pandas, Matplotlib, Arcpy, Seaborn (Waskom, 2021), and Scikit-learn.…”

Section: Methodsmentioning

confidence: 99%

“…The present study utilized a particle tracking model with a continuous sediment source to simulate the added sediment load in natural streams. The architecture and governing equations of the PTM are discussed by Baharvand et al (2023a). The hydrodynamic parameters required by the PTM were exported from an HEC-RAS 2D model and calculated using a linear interpolation technique in the flow domain to estimate total displacement of the particles caused by the advection term.…”

Section: Particle Tracking Modelmentioning

confidence: 99%

“…The hydrodynamic parameters required by the PTM were exported from an HEC-RAS 2D model and calculated using a linear interpolation technique in the flow domain to estimate total displacement of the particles caused by the advection term. The details of the integration of the PTM with HEC-RAS 2D are presented in Baharvand (2022) and Baharvand et al (2023b). The advection and dispersion terms were considered as displacement equations for individual particles in the flow domain in the PTM.…”

Section: Particle Tracking Modelmentioning

confidence: 99%

“…The dispersion coefficients in longitudinal and transverse directions were estimated using two methods: 1) empirical equations and 2) ML-based approaches. The performance of the longitudinal dispersion coefficient equation by Elder (1959) (Equation 1) and transverse dispersion coefficient by Gualtieri and Mucherino (2008) (Equation 8) in a PTM developed for open channel flows is assessed by Baharvand et al (2023a). In the present study, these empirical equations were used in the PTM to simulate sediment transport in a natural stream, and the results were compared with the PTM coupled with ML-based dispersion coefficients.…”

Section: Particle Tracking Modelmentioning

confidence: 99%

“…Figure 1 shows the results of sediment transport modeling in a prismatic open channel and the temporal and spatial distribution of suspended sediment concentration in the longitudinal and transverse directions. Figure 1 Temporal and spatial evolution of suspended sediment concentration in the longitudinal and transverse directions of a prismatic rectangular open channel (Baharvand et al 2023a) Despite studies on modeling sediment transport in waterbodies, the effects of several parameters of sediment transport (e.g., size, shape, density, and composition of sediment particles, flow velocity, shear stress, etc.) are not well understood (Lick, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Application of Machine Learning Approaches in Particle Tracking Model to Estimate Sediment Transport in Natural Streams

Baharvand,

Ahmari

2024

Water Resour Manage

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Several empirical equations and machine learning approaches have been developed to predict dispersion coefficients in open channels; however, the ability of some learning-based models to predict these coefficients has not yet been evaluated, and the direct application of machine learning-based dispersion coefficients to Lagrangian sediment transport models has not been studied. In this research, data from previous studies is used to evaluate the ability of ensemble machine learning models, i.e., random forest regression (RFR) and gradient boosting regression (GBR), to predict longitudinal and transverse dispersion in natural streams. The optimal principal parameters of ensemble models were adjusted using the grid-search cross-validation technique, and the machine learning-based dispersion models were integrated with a Lagrangian particle tracking model to simulate suspended sediment concentration in natural streams. The resulting suspended sediment concentration distribution was compared with the field data. The results showed that GBR model, with a coefficient of determination (R 2 ) of 0.95, performed better than the RFR model, with R 2 =0.9, in predicting the longitudinal dispersion coefficients in a natural stream in both training and testing stages. However, the RFR model with R 2 = 0.94 performed better than the GBR (R 2 = 0.91) in predicting the transverse dispersion in testing stage. Both models underestimated the dispersion coefficients in the training and testing stages. Comparison between the PTM with ensemble dispersion coefficients and empirical-based dispersion relationships revealed the better performance of the GBR model compared to the other two methods.

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Section: Methodsmentioning

confidence: 99%

Section: Particle Tracking Modelmentioning

confidence: 99%

Section: Particle Tracking Modelmentioning

confidence: 99%