Amir Hosein Azimi - Department of Water Engineering, faculty of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
Saeid Shabanlou - Department of Water Engineering, faculty of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
Behrouz Yaghoubi - Department of Water Engineering, faculty of Agriculture, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

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 ۰.۶۵۷, ۳.۵۰۷, ۰.۰۵۲ and ۰.۹۸۵. 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.

Extreme Learning Machine, Hydraulic Jump Length, Rough floor, Sensitivity analysis, Sloping flume