Uncertainty analysis regarding evaluating effective parameters on the hydraulic jump characteristics of different shape channels

Abstract

The hydraulic jump phenomenon is a beneficial tool in open channels for dissipating the extra energy of the flow. The sequent depth ratio and hydraulic jump length critically contribute to designing hydraulic structures. In this research, the capability of Support Vector Machine (SVM) and Gaussian Process Regression (GPR) as kernel-based approaches was evaluated to estimate the features of submerged and free hydraulic jumps in channels with rough elements and various shapes, followed by comparing the findings of GPR and SVM models and the semi-empirical equations. The results represented the effect of the geometry (i.e., steps and roughness elements) of the applied appurtenances on hydraulic jump features in channels with appurtenances. Moreover, the findings confirmed the significance of the upstream Froude number in the sequent depth ratio estimating in submerged and free hydraulic jumps. In addition, the immersion was the highest contributing variable regarding the submerged jump length on sloped smooth bed and horizontal channels. Based on the comparisons among kernel-based approaches and the semi-empirical equations, kernel-based models showed better performance than these equations. Finally, an uncertainty analysis was conducted to assess the dependability of the best applied model. The results revealed that the GRP model possesses an acceptable level of uncertainty in the modeling process.