Prediction of time-dependent sediment suspension in the surf zone using artificial neural network

Yoon, Hyun‐Doug; Cox, Daniel T.; Kim, Munki

doi:10.1016/j.coastaleng.2012.08.005

Cited by 37 publications

(22 citation statements)

References 18 publications

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“…This diffusive process is likely to cause an increasing contaminant concentration of the overlying water column even after those externally supplied contaminant sources have been removed or heavily reduced, and the associated diffusion flux Table 1 The experimental parameters of natural sediment under 10 g/l I n i t i a l c o n c e n t r a t i o n ( m g / l ) 3 can commonly be estimated based on the measured concentration gradient across the sediment-water interface [5]. Most experimental studies in recent years have focused on static release [6][7][8]. Because of the incompleteness of the static release research, contaminated sediment released under hydrodynamic conditions is a growing area of focus.…”

Section: Introductionmentioning

confidence: 99%

Transport mechanisms of contaminants released from fine sediment in rivers

et al. 2015

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Contaminants released from sediment into rivers are one of the main problems to study in environmental hydrodynamics. For contaminants released into the overlying water under different hydrodynamic conditions, the mechanical mechanisms involved can be roughly divided into convective diffusion, molecular diffusion, and adsorption/desorption. Because of the obvious environmental influence of fine sediment (D 90 = 0.06 mm), non-cohesive fine sediment, and cohesive fine sediment are researched in this paper, and phosphorus is chosen for a typical adsorption of a contaminant. Through theoretical analysis of the contaminant release process, according to different hydraulic conditions, the contaminant release coupling mathematical model can be established by the N-S equation, the Darcy equation, the solute transport equation, and the adsorption/desorption equation. Then, the experiments are completed in an open water flume. The simulation results and experimental results show that convective diffusion dominates the contaminant release both in non-cohesive and cohesive fine sediment after their suspension, and that they contribute more than 90 % of the total release. Molecular diffusion and desorption have more of a contribution for contaminant release from unsuspended sediment. In unsuspension sediment, convective diffusion is about 10-50 times larger than molecular diffusion during the initial stages under high velocity; it is close to molecular diffusion in the later stages. Convective diffusion is about 6 times larger than molecular diffusion during the initial stages under low velocity, it is about a quarter of molecular diffusion in later stages, and has a similar level with desorption/adsorption. In unsuspended sediment, a seepage boundary layer exists below the water-sediment interface, and various release mechanisms in that layer mostly dominate the contaminant release process. In non-cohesive fine sediment, the depth of that layer increases linearly with shear stress. In cohesive fine sediment, the range seepage boundary is different from that in non-cohesive sediment, and that phenomenon is more obvious under a lower shear stress.

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Section: Introductionmentioning

confidence: 99%

Transport mechanisms of contaminants released from fine sediment in rivers

et al. 2015

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“…Although the basic concept of artificial neurons was first proposed in 1943 [1], applications of ANNs have blossomed after the introduction of the back-propagation (BP) training algorithm for feedforward ANNs in 1986 [2], and the explosion in the capabilities of computers accelerated the employment of ANNs. The ANN models have also been used in various coastal and nearshore research [3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

2016

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Abstract:In this study, an artificial neural network (ANN) model is developed to predict the stability number of breakwater armor stones based on the experimental data reported by Van der Meer in 1988. The harmony search (HS) algorithm is used to determine the near-global optimal initial weights in the training of the model. The stratified sampling is used to sample the training data. A total of 25 HS-ANN hybrid models are tested with different combinations of HS algorithm parameters. The HS-ANN models are compared with the conventional ANN model, which uses a Monte Carlo simulation to determine the initial weights. Each model is run 50 times and the statistical analyses are conducted for the model results. The present models using stratified sampling are shown to be more accurate than those of previous studies. The statistical analyses for the model results show that the HS-ANN model with proper values of HS algorithm parameters can give much better and more stable prediction than the conventional ANN model.

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“…The application of ANN in sediment load simulation has been cited in some recent works as well. However, in these works, ANN acted either as a standalone modelling approach (Mustafa et al 2012;Yoon et al 2013) or as an integrated approach with the wavelet technique (Liu et al 2013), support vector machine (Kakaei Lafdani et al 2013), Soil and Water Assessment Tool (Singh et al 2012) and artificial bee colony algorithm (Kisi et al 2012b). …”

Section: Introductionmentioning

confidence: 99%

“…ANN is a powerful soft computational technique that has been widely used in many areas of water resource management and environmental sciences (Cigizoglu 2004;Cigizoglu and Alp 2004;Lekkas et al 2004;Heuvelmans et al 2006;Eslamian et al 2008;Jothiprakash and Garg 2009;Memarian et al 2009;Li et al 2010;Talebizadeh et al 2010;Albaradeyia et al 2011;Singh et al 2012;Asnaashari et al 2013;Yoon et al 2013).…”

Section: Introductionmentioning

confidence: 99%

An expert integrative approach for sediment load simulation in a tropical watershed

Memarian

Balasundram

Tajbakhsh

2013

Journal of Integrative Environmental Sciences

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Prediction of highly non-linear behaviour of suspended sediment flow in rivers is of prime importance in environmental studies and watershed management. In this study, the predictive performance of artificial neural network (ANN) integrated with genetic algorithm (GA) was assessed. GA was used to optimize the parameters and architecture of the ANN. Five simulation scenarios (S1 -S5) were developed using daily time series of suspended sediment discharge, water discharge, precipitation and reservoir level. The scenario S1 was composed of only water discharge input. The scenarios S2-S4 were composed of water discharge input and precipitation records at different stations. The inputs water discharge, precipitation and reservoir level formed the last scenario S5. Assessment metrics such as normalized mean square error, correlation coefficient, Nash-Sutcliffe efficiency and trend accuracy were used to evaluate the performance of ANN-GA on the simulation scenarios. Based on error analysis, differences between various scenarios in terms of error metrics were trivial, especially during the testing process. Meanwhile, S1 and S3 showed better accuracy in predicting the trend of sediment load time series, as compared to other scenarios. Based on error and sensitivity analyses, S1 with the Nash -Sutcliff efficiency and correlation coefficient of 0.56 and 0.81, respectively, was chosen as the most appropriate scenario. All networks showed a weak robustness in estimating large magnitudes of sediment load, mostly attributable to scarcity of large observed values in the training data-set. This weakness was also originated from different non-linear relationships governing the process of sediment detachment and final sediment load by a high storm event, as compared to those by low or medium storm events. Furthermore, there was an obvious sediment load overestimation in the 2008 exemplars due to a high level of daily water discharge and the outlined generalization rules. Nevertheless, ANN -GA showed reliable performance for sediment load simulation in the studied watershed.

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Prediction of time-dependent sediment suspension in the surf zone using artificial neural network

Cited by 37 publications

References 18 publications

Transport mechanisms of contaminants released from fine sediment in rivers

Transport mechanisms of contaminants released from fine sediment in rivers

Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

An expert integrative approach for sediment load simulation in a tropical watershed

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