Considering the common use of side weirs in irrigation and drainage networks, estimation of the discharge of the side weirs has always given a consideration by water engineering researchers. Another issue about side weirs is the change in flow conditions in the weir and downstream channel. To optimize the flow conditions in the side weir, this structure is established in a converging channel to reduce the channel width and compensate the reduced discharge. The geometrical parameters assumed as variables in this study are: weir length, weir height, convergence angle and downstream channel width. About 248 experiments were performed. Three neural network models were used to estimate the discharge from the side weir. The model was constructed using MATLAB, and the dimensionless variables that were the geometrical and hydraulic ratios of the model were selected as input parameters. Four ratios were selected as inputs to the model to estimate the discharge coefficient and the discharge from the weir. Considering the outputs of the model, the neural-fuzzy networks have the least error compared to the other models, and this model estimates the discharge of side weir overflowing with 99.8% accuracy.

考虑到边堰在灌溉和排水网络中的普遍使用，估计边堰的流量一直是水利工程研究人员考虑的问题。关于侧堰的另一个问题是堰和下游通道中流动条件的变化。为了优化侧堰中的流动条件，在会聚通道中建立该结构以减小通道宽度并补偿减少的排放。在这项研究中假定为变量的几何参数为：堰长，堰高，会聚角和下游河道宽度。进行了约248次实验。使用了三个神经网络模型来估算侧堰的流量。该模型是使用MATLAB构建的，选择作为模型几何和水力比的无量纲变量作为输入参数。选择四个比率作为模型的输入，以估算流量系数和堰的流量。考虑到模型的输出，与其他模型相比，神经网络的误差最小，该模型估计侧堰溢流的精度为99.8％。