Sudden pollutant discharge in vegetated compound meandering rivers

Farzadkhoo, Maryam; Keshavarzi, Alireza; Hamidifar, Hossein; Javan, Mitra

doi:10.1016/j.catena.2019.104155

Cited by 14 publications

(5 citation statements)

References 27 publications

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“…Consequently, estimation of the longitudinal dispersion coefficient could be useful in the management of water quality in rivers and optimal pollution control strategies [19], [20]. Seo and Cheong [11] stated that the K x is affected by three groups of factors, including hydraulic river features, vegetation, fluid properties and geometric patterns of river reach [21], [22]. The longitudinal dispersion coefficient measurements showed that the most effective parameters are channel width (B), flow depth (H ), bed shear velocity (U * ) and cross-sectional average flow velocity (U ) [23].…”

Section: Introductionmentioning

confidence: 99%

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

et al. 2020

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In this paper, a new hybrid model is proposed using Subset Selection by Maximum Dissimilarity (SSMD) and adaptive neuro-fuzzy inference system (ANFIS) hybridized with the firefly algorithm (FFA) to predict the longitudinal dispersion coefficient (K x). The proposed framework (ANFIS-FFA), combines the specific structures and strengths of both ANFIS and FFA approaches. The FFA is used to derive the optimum ANFIS parameters. The K x data set includes 503 cross-sectional data point from small to large rivers. For pre-processing of the data set, the SSMD method is used, which is superior to the classical trial and error method. The database covers a wide range of river width (0.2-867m), and depths (0.034-19.9 m). Fifteen different combinations of river width (B), depth (H), flow velocity (U) and shear velocity (U *) are implemented as inputs to create fifteen estimative models. The output of the ANFIS-FFA model is compared with the ANFIS and previously published equations to check the performance of the proposed model. The results show that the highest accuracy is attained by the M1 model, with all geometric and hydrodynamic parameters as input variables in comparison with ANFIS and previous equations. The R 2 value, RMSE, MAE and NSE for ANFIS-FFA model are 0.67, 113.14 m 2 /s, 48 m 2 /s, and 0.63 for proposed dimensional model, and 0.35, 874.5, 520.8, and 0.1 in non-dimensional ANFIS-FFA model, respectively. These values were 0.37, 463.34 m 2 /s, 85.69 m 2 /s, and −5.19 for dimensional ANFIS model, and 0.11, 3269.88, 1932.09 and −11.54 for non-dimensional ANFIS model, respectively. Overall, hybridization caused 81%, 75%, 76% improvement in R 2 , RMSE and MAE. In another contribution of the paper, by using the matrix form of developed ANFIS-FFA optimized parameters, a novel explicit calculation procedure for estimation of K x is derived. Based on the results, the proposed ANFIS-FFA model exhibits significant improvements than the classical ANFIS and highlights that optimizing by nature-inspired optimization algorithms plays a critical role in strengthening the ANFIS estimations generality. INDEX TERMS Longitudinal dispersion coefficient, ANFIS-FFA, maximum dissimilarity method, natural rivers, adaptive neuro-fuzzy inference system.

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Section: Introductionmentioning

confidence: 99%

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

et al. 2020

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“…In particular, the turbulent ejection events dominate the outer bank region at the apex section, which could cause the incipient of sediment. Farzadkhoo et al studied the effects of vegetation density and the relative flow depths on the pollutant (passive scalar) transport in a compound meandering channel [22]. They found that both the maximum Turbulent Kinetic Energy (TKE) and the maximum dimensionless longitudinal dispersion coefficient occurred at the apex of the bend region for all vegetation densities.…”

Section: Introductionmentioning

confidence: 99%

A Numerical Study on the Influence of Riparian Vegetation Patch on the Transportation of Suspended Sediment in a U-Bend Channel Flow

Wang,

Yu,

et al. 2024

Fluids

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Bend sections are ubiquitous in natural sandy river systems. This study employs Computational Fluid Dynamics–Discrete Phase Model (CFD-DPM) methodology to analyze particle transport dynamics in U-bend channel flows, focusing on the distinctions between partially vegetated (Case No.1) and non-vegetated (Case No.2) scenarios. The research aims to unravel the intricate relationships among bending channel-induced secondary flow, vegetation blockage, and particle aggregation, employing both quantitative and qualitative approaches. (I) The key findings reveal that vegetation near the inner walls of curved channels markedly diminishes the intensity of secondary circulation. This reduction in circulation intensity is observed not only within vegetated areas but also extends to adjacent non-vegetated zones. Additionally, the study identifies a close correlation between vertical vortices and particle distribution near the channel bed. While particle distribution generally aligns with the vortices’ margin, dynamic patch-scale eddies near vegetation patches induce deviations, creating wave-like patterns in particle distribution. (II) The application of the Probability Density Function (PDF) provides insights into the radius-wise particle distribution. In non-vegetated channels, particle distribution is primarily influenced by secondary flow and boundary layers. In contrast, the presence of vegetation leads to a complex mixing layer, altering the particle distribution pattern and maximizing PDF values in non-vegetated free flow subzones. (III) Furthermore, the research quantifies spatial–temporal sediment heterogeneity through PDF variance. The findings demonstrate that variance in non-vegetated channels increases towards the outer wall in bending regions. Vegetation-induced turbulence causes higher variance, particularly in the mixing layer subzone, underscoring the significance of eddy size in sediment redistribution. (IV) The study of vertical concentration profiles in vegetated U-bend channels offers additional insights, while secondary flow in non-vegetated channels facilitates upward sediment transport and vegetation presence, although increasing the Turbulent Kinetic Energy (TKE), restricts channel space, and impedes secondary flow, thereby reducing vertical particle suspension. Sediment concentrations are found to be higher in the lower layers of vegetated bends, contrary to the pattern in non-vegetated bends. These findings highlight the complex interplay between vegetation, secondary flow, and sediment transport, illustrating the reduced effectiveness of secondary flow in promoting vertical particle transportation in bending channels due to the vegetation obstruction.

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“…Furthermore, Farzadkhoo et al ( 2019b ) investigated the effect of rigid vegetation on the LDC estimation in a compound open channel. According to the results, floodplain vegetation caused the depth-averaged longitudinal velocity and LDC values to decrease and increase, respectively, compared with nonvegetated conditions.…”

Section: Introductionmentioning

confidence: 99%

Reliability-based design and implementation of crow search algorithm for longitudinal dispersion coefficient estimation in rivers

Ghaemi

Zhian

Pirzadeh

et al. 2021

Environ Sci Pollut Res

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The longitudinal dispersion coefficient (LDC) of river pollutants is considered as one of the prominent water quality parameters. In this regard, numerous research studies have been conducted in recent years, and various equations have been extracted based on hydrodynamic and geometric elements. LDC’s estimated values obtained using different equations reveal a significant uncertainty due to this phenomenon’s complexity. In the present study, the crow search algorithm (CSA) is applied to increase the equation’s precision by employing evolutionary polynomial regression (EPR) to model an extensive amount of geometrical and hydraulic data. The results indicate that the CSA improves the performance of EPR in terms of R2 (0.8), Willmott’s index of agreement (0.93), Nash–Sutcliffe efficiency (0.77), and overall index (0.84). In addition, the reliability analysis of the proposed equation (i.e., CSA) reduced the failure probability (Pf) when the value of the failure state containing 50 to 600 m2/s is increasing for the Pf determination using the Monte Carlo simulation. The best-fitted function for correct failure probability prediction was the power with R2 = 0.98 compared with linear and exponential functions.

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Sudden pollutant discharge in vegetated compound meandering rivers

Cited by 14 publications

References 27 publications

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

A Numerical Study on the Influence of Riparian Vegetation Patch on the Transportation of Suspended Sediment in a U-Bend Channel Flow

Reliability-based design and implementation of crow search algorithm for longitudinal dispersion coefficient estimation in rivers

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