_Applied_  
_sciences_  
_Article_  
**Combination of Group Method of Data Handling**  
**(GMDH) and Computational Fluid Dynamics (CFD)**  
**for Prediction of Velocity in Channel Intake**  
**[PERSON] 1,2,*(), [PERSON] 3(), [PERSON] 4, [PERSON] 4, [PERSON] 5 and [PERSON] 6,7,8,9,*()**  
1 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam  
2 Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douilou, Yunlin 64002, Taiwan  
3 Department of Computer and Network Engineering, Jazan University, Jazan 82822-6649, Saudi Arabia; [EMAIL_ADDRESS]  
4 Department of Civil Engineering, Semnan University, Semnan 023, Iran; [EMAIL_ADDRESS] (H.K.); [EMAIL_ADDRESS] (S.K.)  
5 Department of Civil Engineering, Islamic Azad University, Kermanshah 083, Iran; [EMAIL_ADDRESS]  
6 Faculty of Civil Engineering, Technische Universitat Dresden, 01069 Dresden, Germany  
7 School of Economics and Business, Norwegian University of Life Sciences, 1430 As, Norway  
8 Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary  
9 Thuringian Institute of Sustainability and Climate Protection, 07743 Jena, Germany  
* Correspondence: [EMAIL_ADDRESS] (S.S.B.); [EMAIL_ADDRESS] (A.M.)  
Received: 3 August 2020; Accepted: 20 October 2020; Published: 26 October 2020  
**Abstract:** This paper utilizes computational fluid dynamics as well as a group method of data handling (GMDH) method to predict the mean velocity of intake. Firstly, the three dimensional flow pattern in a 90-degree intake is simulated with ANSYS-CFX at a transverse ratio equal to one (\(W^{\star}_{\text{b}}/W^{\star}_{\text{m}}=1\)) that \(W^{\star}_{\text{m}}\) is the width of the main channel and \(W^{\star}_{\text{b}}\) is the width of the branch channel. The comparison of mean velocity in the simulated intake and experimental channel represents the high accuracy of ANSYS-CFX modeling (mean absolute percentage error (MAPE) = 5% and root mean square error (RMSE) = 0.017). A 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 complex nonlinear systems. In this research, to train and test the GMDH method, input data is needed in all parts of the channel. Since there is not enough laboratory data in all parts of the channel, to increase the benchmarks, the laboratory model is simulated by the Computational Fluid Dynamics (CFD) numerical model. After ensuring the proper accuracy of the numerical results, the built-in CFD numerical model has been used as a tool to create primary benchmarks in the channel points, especially in areas where there is no laboratory data. This generated data has been used in training and testing the GMDH method. The diversion angle with the longitudinal direction of the main channel (\(\theta\)), the longitudinal coordinates in the intake (\(y^{\star}\)), and the ratio of the branch channel width to the main channel (\(W_{\text{r}}\)) have been applied as the input training data in the GMDH method to estimate mean velocity. The results of the statistical indexes used to quantitatively examine this model, (R\({}^{2}\) = 0.86, MAPE = 10.44, RMSE = 0.03, SI = 0.12), indicated the accuracy of this model in predicting the mean velocity of the flow within open channel intakes.  
**Keywords: ANSYS-CFX; group method of data handling (GMDH); computational fluid dynamics (CFD); numerical simulation; machine learning; data science; big data; intake; mean velocity; numerical analysis; computational mechanics; ANSYS**