Combining deterministic modelling with artificial neural networks for suspended sediment estimates

Makarynskyy, Oleg; Makarynska, Dina; Rayson, Matthew D.; Langtry, Scott

doi:10.1016/j.asoc.2015.05.044

Cited by 29 publications

(7 citation statements)

References 33 publications

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“…Due to issues such as the above, a combination of ANN with hydrodynamic model nodes could prove useful and is often used to take advantage of the ANN advantages [53,54].…”

Section: Adcp-e and Beyondmentioning

confidence: 99%

“…As ANNs use far less computation power than models, a series of nodes could be created by a coarse grid model, replacing ADCPs, enabling the network to be trained from multiple points and predict the surrounding currents. Alternatively, the method used by Makarynskyy et al for suspended sediment prediction could be used [54], where a model was used for a short initial period, on which the ANN was trained. In this case, the network would associate modelled subsurface currents with HF radar surface currents, after which, just the radar input would be required, saving huge computation cost.…”

Section: Adcp-e and Beyondmentioning

confidence: 99%

“…The residual signal between the two was also plotted, showing the difference between the harmonic prediction and the real measurements. Due to issues such as the above, a combination of ANN with hydrodynamic model nodes could prove useful and is often used to take advantage of the ANN advantages [53,54]. As ANNs use far less computation power than models, a series of nodes could be created by a coarse grid model, replacing ADCPs, enabling the network to be trained from multiple points and predict the surrounding currents.…”

Section: Comparison To Harmonic Analysismentioning

confidence: 99%

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Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements

Bradbury

Conley

2021

Remote Sensing

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An extensive record of current velocities at all levels in the water column is an indispensable requirement for a tidal resource assessment and is fully necessary for accurate determination of available energy throughout the water column as well as estimating likely energy capture for any particular device. Traditional tidal prediction using the least squares method requires a large number of harmonic parameters calculated from lengthy acoustic Doppler current profiler (ADCP) measurements, while long-term in situ ADCPs have the advantage of measuring the real current but are logistically expensive. This study aims to show how these issues can be overcome with the use of a neural network to predict current velocities throughout the water column, using surface currents measured by a high-frequency radar. Various structured neural networks were trained with the aim of finding the network which could best simulate unseen subsurface current velocities, compared to ADCP data. This study shows that a recurrent neural network, trained by the Bayesian regularisation algorithm, produces current velocities highly correlated with measured values: r2 (0.98), mean absolute error (0.05 ms−1), and the Nash–Sutcliffe efficiency (0.98). The method demonstrates its high prediction ability using only 2 weeks of training data to predict subsurface currents up to 6 months in the future, whilst a constant surface current input is available. The resulting current predictions can be used to calculate flow power, with only a 0.4% mean error. The method is shown to be as accurate as harmonic analysis whilst requiring comparatively few input data and outperforms harmonics by identifying non-celestial influences; however, the model remains site specific.

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“…Due to issues such as the above, a combination of ANN with hydrodynamic model nodes could prove useful and is often used to take advantage of the ANN advantages [53,54].…”

Section: Adcp-e and Beyondmentioning

confidence: 99%

Section: Adcp-e and Beyondmentioning

confidence: 99%

Section: Comparison To Harmonic Analysismentioning

confidence: 99%

See 1 more Smart Citation

Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements

Bradbury

Conley

2021

Remote Sensing

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“…Hassan et al (2015) employed ANN to estimate weekly sediment load based on discharge and temperature data. Makarynskyy et al (2015) estimated suspended sediment concentration in marine areas. Currents and waves modeled by deterministic numerical models were used as ANN inputs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of erosional rates for cohesive sediments in annular flume experiments using artificial neural networks

et al. 2018

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Artificial neural network is used to predict development of suspended sediment concentration in annular flume experiments on cohesive sediment erosion. Natural sediment for the experiments was taken from the River Rhine and subjected to a consecutive increase in the bed shear stress. The development of the suspended particulate matter (SPM) was measured and then utilized for artificial neural network training, validation, and testing, including independent testing on new data sets. Several network configurations were examined, in particular, with and without autoregressive input. Additionally, relative importance of auxiliary physical-chemical parameters was analyzed. Artificial neural network with autoregressive input showed very high precision in the SPM prediction for all independent test cases achieving average mean squared error 0.034 and regression value 0.998. It was found that for an abundant training sample, the SPM parameter itself is enough to obtain high quality prediction. At the same time, physical-chemical parameters may provide some improvement to the artificial neural network prediction in cases that comprise values unprecedented in the training sample. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited

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“…The following applications in various calculations of ANN in sediment load or transport can be developed by [15] for prediction of suspended sediment using ANN GA conjunction model with Markov chain approach at flood conditions, combining deterministic modelling with ANN for suspended sediment estimates [16]; integrative neural networks model for prediction of sediment rating curve parameters for ungauged basins [17]; a review sediment load change [18]; stream flow discharge and sediment rate relation using ANN [19]; evaluation of transport formulas and ANN models to estimate suspended load transport rate [20]; daily suspended sediment load prediction using ANN and support vector machines [21]; estimate sediment load in ungauged catchments using ANN [22]; suspended sediment modeling using genetic programming and soft computing techniques [23]; estimation of daily suspended sediments using support vector machines [24] and prediction of bed material load transport using neural network [25].…”

Section: Introductionmentioning

confidence: 99%

The application of backpropagation neural network method to estimate the sediment loads

et al. 2017

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Abstract. Nearly all formulations of conventional sediment load estimation method were developed based on a review of laboratory data or data field. This approach is generally limited by local so it is only suitable for a particular river typology. From previous studies, the amount of sediment load tends to be non-linear with respect to the hydraulic parameters and parameter that accompanies sediment. The dominant parameter is turbulence, whereas turbulence flow velocity vector direction of x, y and z. They were affected by water bodies in 3D morphology of the cross section of the vertical and horizontal. This study is conducted to address the non-linear nature of the hydraulic parameter data and sediment parameter against sediment load data by applying the artificial neural network (ANN) method. The method used is the backpropagation neural network (BPNN) schema. This scheme used for projecting the sediment load from the hydraulic parameter data and sediment parameters that used in the conventional estimation of sediment load. The results showed that the BPNN model performs reasonably well on the conventional calculation, indicated by the stability of correlation coefficient (R) and the mean square error (MSE).

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Combining deterministic modelling with artificial neural networks for suspended sediment estimates

Cited by 29 publications

References 33 publications

Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements

Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements

Prediction of erosional rates for cohesive sediments in annular flume experiments using artificial neural networks

The application of backpropagation neural network method to estimate the sediment loads

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