Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms

Ebtehaj, Isa; Bonakdari, Hossein

doi:10.1007/s12205-015-0630-7

Cited by 51 publications

(17 citation statements)

References 39 publications

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“…In the experimental correlations, the performance accuracy was very low and unacceptable. Therefore, researchers have started using artificial intelligence in recent years [20][21][22][23] for example; Analysis of crude-oil desalting system [51], velocity prediction in sewer pipes [64]; bed load sediment transport estimation in a clean pipe [65]; monthly inflow prediction [66]; predicting sediment transport in clean pipes [67] and estimate velocity at limit of deposition in storm sewers [68].…”

Section: = ( mentioning

confidence: 99%

Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm

Hazbeh

Alvar

Aghdam

et al. 2021

Journal of Petroleum and Mining Engineering

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Achieving important and effective reservoir parameters requires a lot of time and cost, and also achieving these devices is sometimes not possible. In this research, a dataset including 565 datapoints collected from published articles have been used. The input data for forecasting oil formation volume factor (OFVF) were solution gas oil ratio (Rs), gas specific gravity (γg), API gravity (API0) (or oil density γo), and temperature (T). Two hybrid methods multilayer perceptron (MLP) with artificial bee colony (ABC) and firefly (FF) algorithms to predict this parameter have been introduced in that study and their results have been compered after extraction. After essential investigations in this study, the results show that MLP-ABC gives the best accuracy for predicting OFVF. For MLP-ABC model OFVF prediction accuracy in terms of RMSE < 0.002573 bbl/STB and R2 = 0.998 for this test dataset. After comparing the results of the experimental equations, it was concluded that the Dokla and Osman model gives the best results and Based on Spearman's correlation coefficient relationships all input parameters have a positive effect on OFVF prediction, which are as follows: Rs> T> API> γg and these results show that the effect of Rs is more than other input variables and the effect of γg is the lowest.

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confidence: 99%

Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm

Hazbeh

Alvar

Aghdam

et al. 2021

Journal of Petroleum and Mining Engineering

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“…It is characterized by strong robustness, memory, nonlinear mapping ability and self-learning ability. As one of the commonly utilized prediction algorithms in ANN, the multilayer perceptron (MLP) algorithm stands out in solving various prediction problems because of its strong fitting ability [15], [16]. Therefore, the MLP is adopted for the second difficult point (ii).…”

Section: Introductionmentioning

confidence: 99%

A Novel Stacked Generalization Ensemble-Based Hybrid PSVM-PMLP-MLR Model for Energy Consumption Prediction of Copper Foil Electrolytic Preparation

Liao

Ning

et al. 2021

IEEE Access

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At present, the energy consuming during the electrolytic copper foil preparation accounts for more than 75% of the total energy consumption. In real-life production, the process parameters are set by the operator empirically and the system may not work at the operating point with minimum energy consumption. Therefore, it is critical to establish an effective model for predicting electrolysis energy consumption to guide the parameters design. In this paper, a novel hybrid model (named PSVM-PMLP-MLR) based on stacked ensemble learning is proposed. The model is divided into two parts: the baselearning model and the meta-learning model. The support vector machine (SVM) model and multilayer perceptron (MLP) model with different input structures are established by the former first. Then the particle swarm algorithm is employed to determine the optimal value of SVM parameters and the optimal weight of MLP by minimizing the mean absolute percentage error (MAPE). The multiple linear regression (MLR) is finally employed as a meta-learning machine to compute the final predictions. Experimental results show that the regression coefficient of this model reached 0.987, and compared with the traditional SVM and MLP models, the accuracy of the model is improved by 10.29% and 8.28%, respectively.INDEX TERMS Ensemble learning, electrolytic preparation of copper foil, energy consumption, machine learning.

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“…Considering the complexity of engineering problems and the growing number of engineering studies, new methods called soft computing, were significantly used during recent decade that were more efficient and more accurate in solving complicated and difficult engineering issues and, facilitating studies [10][11][12][13]. Soft computing and artificial intelligence were used by different researchers to estimate and predict different hydraulic and hydrologic problems especially discharge coefficient [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming

Bonakdari¹,

Ebtehaj²,

Gharabaghi³

et al. 2020

Preprint

Self Cite

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This paper proposes a model based on gene expression programming for predicting discharge coefficient of triangular labyrinth weirs. The parameters influencing discharge coefficient prediction were first examined and presented as crest height ratio to the head over the crest of the weir (p/y), crest length of water to channel width (L/W), crest length of water to the head over the crest of the weir (L/y), Froude number (F=V/√(gy)) and vertex angle ( ) dimensionless parameters. Different models were then presented using sensitivity analysis in order to examine each of the dimensionless parameters presented in this study. In addition, an equation was presented through the use of nonlinear regression (NLR) for the purpose of comparison with GEP. The results of the studies conducted by using different statistical indexes indicated that GEP is more capable than NLR. This is to the extent that GEP predicts discharge coefficient with an average relative error of approximately 2.5% in such manner that the predicted values have less than 5% relative error in the worst model.

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Bed load sediment transport estimation in a clean pipe using multilayer perceptron with different training algorithms

Cited by 51 publications

References 39 publications

Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm

Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm

A Novel Stacked Generalization Ensemble-Based Hybrid PSVM-PMLP-MLR Model for Energy Consumption Prediction of Copper Foil Electrolytic Preparation

Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming

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