.png){kind=icon}
返回
Modeling triangular, rectangular, and parabolic weirs using weighted robust extreme learning machine
.png){kind=icon}
OA
AI
DOI：10.1007/s13201-023-01873-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this study, dimensionless parameters influencing the coefficient of discharge (COD) are found and four different WRELM models are developed. After that, a dataset is created for verifying the WRELM models in which 70% of the data are employed to train learning machine models and the remaining 30% to test them. For the mentioned algorithm, the optimal number of hidden layer neurons along with the best activation function is chosen. Additionally, the best value for the regularization parameter of the WRELM algorithm is computed. By analyzing the simulation results, the superior WRELM model and the variables impacting the COD are detected. The superior WRELM model approximates COD values with the minimum error and the highest correlation with laboratory values. For the superior model, the values of the R, MAE and VAF statistical indices are computed to be 0.994, 0.0004 and 98.662, respectively. The analysis of the simulation results indicates that the dimensionless parameters alpha and T/B are the most influencing input parameters. The superior WRELM model results are compared with the algorithm, and it is concluded that the WRELM model is noticeably more efficient. For the superior WRELM model, a partial derivative sensitivity analysis (PDSA) is conducted in which as the input parameter alpha increases, the PSDA value increases as well. Finally, an equation is suggested for estimating COD values.
Keyword:
Coefficient of discharge
Triangular weir
Rectangular weir
Parabolic weir
Weighted robust extreme learning machine
Modeling
期刊
A
IF:
5.7
论文数:
2.2K
被引数:
1.2W
机构
引用论文
Outlier-robust extreme learning machine for regression problems用于回归问题的离群鲁棒极限学习机.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏分享收藏Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate用于预测热带气候月降雨量的新型混合线性随机与非线性极限学习机方法
PREAI
分享收藏Spiking neural networks with Hebbian plasticity for unsupervised representation learning具有赫布可塑性脉冲神经网络的无监督表征学习OAAI
OAAI分享收藏
查看更多内容