@ARTICLE{Shabanlou,
author = {Azimi, A.H. and shabanlou, S and yosefvand, F. and rajabi, A. and yaghoubi, B. and },
title = {Simulation of Scour Pattern Around Cross-Vane Structures Using Outlier Robust Extreme Learning Machine},
volume = {25},
number = {4},
abstract ={In this research, the scour hole depth at the downstream of cross-vane structures with different shapes (i.e., J, I, U, and W) was simulated utilizing a modern artificial intelligence method entitled "Outlier Robust Extreme Learning Machine (ORELM)". The observational data were divided into two groups: training (70%) and test (30%). Then, using the input parameters including the ratio of the structure length to the channel width (b/B), the densimetric Froude number (Fd), the ratio of the difference between the downstream and upstream depths to the structure height (Δy/hst), and the structure shape factor (φ), eleven different ORELM models were developed for estimating the scour depth. Subsequently, the superior model and also the most effective input parameters were identified through the conduction of uncertainty analysis. The superior model simulated the scour values by the dimensionless parameters b/B, Fd, Δy/hst. For this model, the values of the correlation coefficient (R), the variance accounted for (VAF), and the Nash-Sutcliffe efficiency (NSC) for the superior model in the test mode were obtained 0.956, 91.378, and 0.908, respectively. Also, the dimensionless parameters b/B and Δy/hst were detected as the most effective input parameters. Furthermore, the results of the superior model were compared with the extreme learning machine model and it was concluded that the ORELM model was more accurate. Moreover, an uncertainty analysis exhibited that the ORELM model had an overestimated performance. Besides, a partial derivative sensitivity analysis (PDSA) model was performed for the superior model. },
URL = {http://jstnar.iut.ac.ir/article-1-4107-en.html},
eprint = {http://jstnar.iut.ac.ir/article-1-4107-en.pdf},
journal = {Journal of Water and Soil Science},
doi = {10.47176/jwss.25.4.13933},
year = {2022}
}