Submit Manuscript | http://medcraveonline.com Introduction Modeling of Hydraulic structures has received much attention in recent years due to these effect on the increasing the hydro system performance. 1 Weirs are the common structure which uses in the most of water engineering projects such as hydropower systems, irrigation and drainage networks and sewage networks. Side weir has many possible uses in the hydraulic engineering feld and has also been investigated as an important structure in hydro systems. 2,3 Side weir is a hydraulic structure placed on the side of the channel and sometime uses as water surface controller structure in the dam an irrigation projects. Whereas the main duty of the side weir is removing the excess fow from the hydro systems. 4,5 Figure 1 shows an schematic scheme of the side weir in subcritical fow condition. Study on the side weir hydraulics conducted by the physical and numerical approaches. In the physical studies feld, researchers tried to improve the performance of side weir by proposing the various shape for the crest of the side weir and compared this performance with a rectangular shape as a standard form for side weir. In this regard, labyrinth, oblique, semi– elliptical, curved plan–form and trapezoidal sharp and broad–crested can be mentioned. 5–13 Researchers who conducted an experimental study on the hydraulic side weir proposed empirical equations for calculating the side weir discharge coeffcient. A summary of the most famous empirical formulas gives in the Table 1. In the numerical modeling feld in addition to solving the government hydraulically equations by numerical approaches and such as Runge Kutta Method, the computational fuid dynamic (CFD) techniques have been used to simulate the fow over side weir. Numerical solving the government equations leads to defne the hydraulic parameters such as water surface profle, distribution of velocity and pressure and fow pattern. 14,15 Another way as numerical modeling are related to use the artifcial neural network (ANN) models for predicting the hydraulic properties of side weir such as discharge coeffcient. In this regard, the Multilayer Preceptor (MLP) neural network, Adaptive Neuro–Fuzzy Inference System (ANFIS) was used by researchers. Developing the ANN models is based on the dataset. It means that to predict the hydraulic phenomenon by neural network techniques, the parameters which are an infuence on the phenomenon should be measured at the previous. The ANN models can use as standalone and also applied as a participant of the numerical methods in numerical simulation to increase the accuracy of the numerical modeling. The results of using the mentioned neural network models indicate that ANN models are more accurate. 10,15–19 In this research, the radial basis function (RBF) neural network which has high performance in pattern recognition and image processing is developed for predicting the side weir discharge coeffcient and its performance is compared with empirical formula and multilayer perceptron neural network as common ANN model which uses by most of the researchers. MOJ Civil Eng. 2018;4(2):93‒98. 93 © 2018 Parsaie . et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and build upon your work non-commercially. Prediction of side weir discharge coeffcient by radial basis function neural network Volume 4 Issue 2 - 2018 Abbas Parsaie, Amir Hamzeh Haghiabi Department of water Engineering, Lorestan University, Iran Correspondence: Abbas Parsaie, Ph.D candidate of hydro structures, Department of water Engineering, Lorestan University, Khorram Abad, Iran, Tel 989163685867, Email Abbas_Parsaie@yahoo.com Received: November 20, 2017 | Published: April 12, 2018 Abstract Weirs are the common structure uses in the most of water engineering projects such as Hydropower systems, irrigation and drainage networks and sewage networks. Side weir has many possible uses in the hydraulic engineering field and also has been investigated as an important structure in hydro systems. In this paper, predicting the side weir discharge coefficient was considered by using the empirical formulas, multi–layer perceptron (MLP) and radial basis function (RBF) neural as a delegate of artificial neural network models. The results indicate that the Emiroglu formula by the correlation coefficient (0.65) and root mean square error (0.03) is accurate among the empirical formulas. Evaluating the performance of the MLP model by the correlation coefficient (0.89) and root mean square error (0.067) and RBF model by the correlation coefficient (0.71) and root mean square error (0.08) show are more accurate in compare to the empirical formulas. When the MLP model was more accurate than RBF models. Keywords: weirs, discharge coefficient, empirical formulas, multi–layer Perceptron, performance MOJ Civil Engineering Research Article Open Access Table 1 Some empirical formulas to calculate the side weir discharge coeffcient Row Author Equation 1 Nandesamoorthy 2 Subramanya et al. 23 0.5 2 1 2 d 1 2 - Fr C = 0.432 1 + 2Fr         0.5 2 1 2 d 1 1 - Fr C = 0.864 2 + 2Fr        