## 1 Introduction  
Intakes are hydraulic structures that divert the flow and control a part of the flow passing through the channel in water transferring networks [1]. As the flow approaches the intake, the suction pressure at the end of the intake channel creates transverse acceleration in the flow. It causes a part of the flow to deviate from the main path and enter the intake. The rest of the flow continues its path in the main channel [2]. The flow curve caused by a part of the flow moving towards the main channel's intake creates an imbalance between the lateral pressure gradient, the centrifugal force, and the sheer force, which makes vortex flows known as secondary flows [3]. These flows are accompanied by very complex three-dimensional flows within the intake channel [4]. Many analytical and experimental research types have been conducted to understand the stream hydraulics within the intakes [5; 6; 7; 8; 9; 10]. As computer capacity evolves, novel numerical techniques for forecasting the mean velocity field in open channel intake [11; 12]. CFD is more economical than experimental approaches and has enhanced our partial knowledge of the flow in open channels [11; 12]. In addition to CFD, soft computing techniques have been developed to estimate multiphase flows in hydraulics engineering [13; 14; 15; 16; 17; 18; 19]. Soft computing techniques supply intelligent tools to indicate nonlinear input-output mapping and as a result, solve several complex problems. Most of these methods, such as the ANN (Artificial Neural Networks) and the ANFIS (Adaptive Neuro Fuzzy Inference System), do not present a specific equation for predicting the target parameter. Group method of data handling (GMDH) is one type of artificial intelligence approach that presents elementary equations for calculating the problem's target parameter and performing well in nonlinear complex systems [20; 21; 22; 23; 24]. However, many numerical and experimental methods have been utilized to predict and measure the velocity at open channel intakes [25; 26]. There are some problems in entirely studying hydraulic properties at each open channel intakes section for different conditions. For example, the experimental tools are costly because of needing various measurement instrumentations and sometimes destructive measurements. Due to these problems, soft computing techniques can be a desirable alternative for conventional tools to estimate mean velocity in open channel intakes for various conditions. In order to train GMDH technique to predict velocity, the thorough flow field dataset (resulted from ANSYS-CFX simulation) is needed [20; 21; 22; 23; 24; 25; 26]. [PERSON] and [PERSON] (2014) used the GMDH method to predict localized scour downstream of shuice gate using fuzzy system and particle swarm optimization (PSO) algorithm (NF-GMDH-PSO). Using six dimensionless parameters, they presented a functional equation and predicted the scour depth using the proposed method (27). [PERSON] et al. (2015) used GMDH to estimate the rectangular lateral orifice flow coefficient. They also analyzed the sensitivity of each of the dimensionless parameters' effect, useful in estimating the flow coefficient (29). The mean velocity prediction in open channel intakes using the combination of CFD and GMDH is under-researched to the best authors' knowledge. As a result, this paper presents a new perspective to forecast the mean velocity of the flow in different width ratios and intake angles in open channel intakes using CFD and GMDH prediction techniques. The flow pattern was simulated in two water and air phases in a 90-degree intake with an equal width ratio. The results obtained from the ANSYS-CFX model are verified using the experimental results. After verification and making sure that the numerical model performs well at simulating the flow, measures are simulated in the intake at 30 and 60-degree intake angles in different width ratios of the branch channel to the main channel using CFD.