Amir Hosein Azimi; Saeid Shabanlou; Behrouz Yaghoubi
Abstract
In this paper, the hydraulic jump length on a slope rough floor is simulated through the extreme learning machine (ELM). Then, the parameters affecting the hydraulic jump on the slope rough bed are detected. After that, five different ELM model are developed so as to determine the influenced factor. ...
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In this paper, the hydraulic jump length on a slope rough floor is simulated through the extreme learning machine (ELM). Then, the parameters affecting the hydraulic jump on the slope rough bed are detected. After that, five different ELM model are developed so as to determine the influenced factor. Next, the results obtained from different ELM models are analyzed. The comparison of the results with the experimental data proves the acceptable accuracy of the mentioned numerical models. Regarding the results from the numerical method, the superior ELM model estimates the hydraulic jump length in terms of the flow Froude number, the ratio of bed roughness, the ratio of sequent depths and bed slope. The values of the root mean square error (RMSE), mean absolute percent error (MAPE), scatter index (SI) and correlation coefficient (R) for the superior model are respectively obtained 0.657, 3.507, 0.052 and 0.985. Based on the simulation, the flow Froude number at upstream is introduced as the most effective parameter in predicting the jump length on the sloping rough floor.
Ehsan Yarmohammadi; Fariborz Yosefvand; Ahmad Rajabi; Saeid Shabanlou
Abstract
In this paper, for the first time, the discharge coefficient of triangular plan form weirs is simulated by the extreme learning machine (ELM). ELM is one of the powerful and rapid artificial intelligence methods in modeling complex and non-linear phenomena. Compared to other learning algorithms such ...
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In this paper, for the first time, the discharge coefficient of triangular plan form weirs is simulated by the extreme learning machine (ELM). ELM is one of the powerful and rapid artificial intelligence methods in modeling complex and non-linear phenomena. Compared to other learning algorithms such as back propagation, this model acts rapidly in the learning process and provides a desirable performance in processing generalized functions. In this study, the Monte Carlo simulation is used for examining capabilities of numerical models. Also, the k-fold cross validation method with k=5 is utilized for evaluating abilities of the ELM models. Then, six ELM models are introduced by means of the parameters affecting the discharge coefficient of triangular plan form weirs. After that, the superior model is identified by analyzing the results of the mentioned models. The superior model predicts discharge coefficient values with reasonable accuracy. This model simulates the discharge coefficient as a function of the flow Froude number, vertex angle of the triangular plan form weir, the ratio of weir length to its height, the ratio of flow head to weir height and the ratio of channel width to weir length. For the best model, the Mean Absolute Error, Root Mean Square Error and determination coefficient are computed 1.173, 0.012 and 0.967, respectively. Furthermore, examination of the influence of the input parameters indicates that the flow Froude number is the most influenced factor in modeling the discharge coefficient. Also, the error distribution showed that roughly 86 % of the superior model results had an error less than 2 %. Furthermore, a practical equation was provided to compute the discharge coefficient.
Mohammadali Izadbakhsh; Reza Hajiabadi
Abstract
In this paper, the discharge coefficient of weirs is simulated by the extreme learning machine (ELM). To this end, seven different ELM models are introduced by the input parameters. Also, the most optimal number of the neurons in the hidden layer is computed 7. Furthermore, different activation functions ...
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In this paper, the discharge coefficient of weirs is simulated by the extreme learning machine (ELM). To this end, seven different ELM models are introduced by the input parameters. Also, the most optimal number of the neurons in the hidden layer is computed 7. Furthermore, different activation functions of the ELM model are assessed and the sigmoid activation function is taken into account as the most optimal one. Besides, the seven defined ELM models are analyzed and the superior model is introduced. This model approximates the discharge capacity with better performance in comparison with the other ELM models. It should also be noted that the superior ELM model is in terms of the dimensionless factors including Fr, HT/P, Lc/W, A/w, w/P. For the superior ELM model, the R2, VAF and NSC are respectively estimated 0.897, 89.626 and 0.892. Furthermore, the MAE and RMSE statistical indices for the ELM model are respectively estimated 0.024 and 0.031. Also, the most effective input parameters for modeling the discharge capacity of labyrinth weirs using the ELM are detected through the conduction of a sensitivity analysis, meaning that the HT/P is identified as the most influenced input parameter. Lastly, an applicable equation for computing the discharge capacity of labyrinth weirs is suggested which can be used by hydraulic and environmental engineers.
