Investigate of Learning Machines Performance in Estimation of Circular Bottom Intake Discharge Coefficient

By the development of technology and the advancement of technology, many intelligent methods have emerged for estimating the discharge coefficient of different hydraulic structures. One of the structures used in power plants is bottom intake structure. The task of this structure is to transfer the flow to the collecting channel. The advantages of these structures are their stability against dynamic and static loads due to their low level alignment. In the present study, four intelligent algorithms capable of extreme learning machine (ELM), general regression neural networks (GRNN), multivariate adaptive regression spline (MARS) and M5 tree model have been evaluated in modeling of discharge coefficient of bottom intake. The modeling results showed that the ELM algorithm is more accurate than the other algorithms in both training (70% of data) and test (30% of data) periods. In addition, R2 coefficient for the mentioned algorithm was up to 3.74% higher than the other algorithms used. Also the DDR criterion and modeling error histogram showed the superiority of the ELM algorithm. Finally, the computational speed of the algorithms used was compared, which ELM algorithm was 2.557 times faster than the other algorithms. Therefore, the ELM algorithm has high potential for modeling the discharge coefficient in overflows due to its good accuracy and high speed.