Estimation of hydraulic jump on corrugated bed using artificial neural networks and genetic programming

• Main Authors: Akram Abbaspour, Davood Farsadizadeh, Mohammad Ali Ghorbani
• Format: Article
• Language: English
• Published: Elsevier 2013-04-01
• Series: Water Science and Engineering
• Subjects: artificial neural networks; genetic programming; corrugated bed; Froude number; hydraulic jump
• Online Access: http://www.sciencedirect.com/science/article/pii/S1674237015302362

Artificial neural networks (ANNs) and genetic programming (GP) have recently been used for the estimation of hydraulic data. In this study, they were used as alternative tools to estimate the characteristics of hydraulic jumps, such as the free surface location and energy dissipation. The dimensionless hydraulic parameters, including jump depth, jump length, and energy dissipation, were determined as functions of the Froude number and the height and length of corrugations. The estimations of the ANN and GP models were found to be in good agreement with the measured data. The results of the ANN model were compared with those of the GP model, showing that the proposed ANN models are much more accurate than the GP models.