Artificial neural network and regression models for flow velocity at sediment incipient deposition

Safari, Mir Jafar Sadegh; Aksoy, Hafzullah; Mohammadi, Mirali

doi:10.1016/j.jhydrol.2016.08.045

Cited by 57 publications

(14 citation statements)

References 34 publications

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“…When Firat et al [42] predicted the scour depth around circular bridge piers based on the data from various studies, the GRNN model performed superior to BPNN and MLR. Among different types of ANNs, BPNN often shows a fair performance because the best model could be obtained by iterant parameters adjustment after calibration in the models [43]. However, the problem of overtraining often accompanies accuracy, because a large number of weights and biases is generated from many iterations [34].…”

Section: Comparison Of Results Obtained By Modelsmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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Freshwater reservoirs are considered as the source of atmospheric greenhouse gas (GHG), but more than 96% of global reservoirs have never been monitored. Compared to the difficulty and high cost of field measurements, statistical models are a better choice to simulate the carbon emissions from reservoirs. In this study, two types of Artificial Neural Networks (ANNs), Back Propagation Neural Network (BPNN) and Generalized Regression Neural Network (GRNN), were used to predict carbon dioxide (CO 2 ) flux emissions from reservoirs based on the published data. Input variables, which were latitude, age, the potential net primary productivity, and mean depth, were selected by Spearman correlation analysis, and then the rationality of these inputs was proved by sensitivity analysis. Besides this, a Multiple Non-Linear Regression (MNLR) and a Multiple Linear Regression (MLR) were used for comparison with ANNs. The performance of models was assessed by statistical metrics both in training and testing phases. The results indicated that ANNs gave more accurate results than regression models and GRNN provided the best performance. With the help of this GRNN, the total CO 2 emitted by global reservoirs was estimated and possible CO 2 flux emissions from a planned reservoir was assessed, which illustrated the potential application of GRNN.

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Section: Comparison Of Results Obtained By Modelsmentioning

confidence: 99%

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Chen

Huang

2018

Water

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“…It ranges from 1 to 100 neurons, as shown in Figure 5. A larger number of neurons was used as the upper limit compared to MLP because the radial basis function in the hidden layer produces a significant non-zero response only when the input falls within a small localized region of the input space [24]. The Gaussian function was used as the transfer function.…”

Section: Resultsmentioning

confidence: 99%

Application of artificial neural networks for hydrological modelling in Karst

Lakušić¹

2018

JCE

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Application of artificial neural networks for hydrological modelling in karstThe possibility of short-term water flow forecasting in a karst region is presented in this paper. Four state-of-the-art machine learning algorithms are used for the one day ahead forecasting: multi-layer perceptron neural network, radial basis function neural network, support vector machines for regression (SVR), and adaptive neuro fuzzy inference system (ANFIS). The results show that the ANFIS model outperforms other algorithms when the root mean square error and mean absolute error are used as quality indicators. Primjena neuronskih mreža za hidrološko modeliranje u krškom području U ovom radu je razmatrana mogućnost kratkoročnog predviđanja protoka vode u krškom području. Četiri suvremena algoritma strojnog učenja su korištena za predviđanje protoka jedan dan unaprijed i to: neuronska mreža s višeslojnim perceptronom, neuronska mreža s radijalnom aktivacijskom funkcijom, metoda potpornih vektora za regresiju (SVR), adaptivni neuroneizraziti (fuzzy) sustav zaključivanja (ANFIS). Rezultati pokazuju da ANFIS model nadmašuje ostale algoritme kada se korijen srednje kvadratne popogreške i srednja apsolutna pogreška upotrijebe kao mjere kvalitete.

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“…In this regard, incipient deposition is a concept linked to the channel design, and identified as the sediment transport mode in which sediment particles are clustered visibly in certain areas at the channel bed [15]. In other words, sediment particles are transported as bed load or accumulated at the channel bed, but without making a permanent deposited bed layer [15][16][17]. Flow velocity is sufficiently low for incipient deposition of sediment.…”

Section: Introductionmentioning

confidence: 99%

Shear Stress-Based Analysis of Sediment Incipient Deposition in Rigid Boundary Open Channels

Ünal

2018

Water

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Urban drainage and sewer systems, and channels in general, are treated by the deposition of sediment that comes from water collecting systems, such as roads, parking lots, land, cultivation areas, and so forth, which are all under gradual or sudden change. The carrying capacity of urban area channels is reduced heavily by sediment transport that might even totally block the channel. In order to solve the sedimentation problem, it is therefore important that the channel is designed by considering self-cleansing criteria. Incipient deposition is proposed as a conservative method for channel design and is the subject of this study. With this aim, an experimental study carried out in trapezoidal, rectangular, circular, U-shape, and V-bottom channels is presented. Four different sizes of sand were used as sediment in the experiments performed in a tilting flume under nine different longitudinal channel bed slopes. A shear stress approach is considered, with the Shields and Yalin methods used in the analysis. Using the experimental data, functionals are developed for both methods. It is seen that the bed shear stress changes with the shape of the channel cross-section. Incipient deposition in rectangular and V-bottom channels starts under the lowest and the highest shear stress, respectively, due mainly to the shape of the channel cross-section that affects the distribution of shear stress on the channel bed.

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Artificial neural network and regression models for flow velocity at sediment incipient deposition

Cited by 57 publications

References 34 publications

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Estimating Carbon Dioxide (CO2) Emissions from Reservoirs Using Artificial Neural Networks

Application of artificial neural networks for hydrological modelling in Karst

Shear Stress-Based Analysis of Sediment Incipient Deposition in Rigid Boundary Open Channels

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