By the effect of pressure or velocity fluctuations, the water supply networks may be damaged. For avoiding of this case, suitable and optimized management of networks is very necessary. In this research, control of pressure and velocity was investigated to prevent problems of water supply network and also hydraulic characteristics were also predicted by Adaptive Neuro Fuzzy Inference System (ANFIS). In this way, first by zoning the city of Kangavar province (as case study) to six sub-zones based on distribution parameters, plotting of water supply networks with 10 years design period and target population of 95,000 based on 22 h in day irrigation and 29.6 square meter per capita at the end of design period were used. Pressure and velocity of network were then analyzed utilizing EPANET software. Based on the results, maximum pressure occurred at 3–3 node in third pressure region which estimate 100 m of water and maximum value of network velocity was estimated as 1.4 m per second. Also, results showed that the discharge was used in model according to diameter of tubes and selected paths in different regions are in appropriate range. Then, using the measured values, the fuzzy neural network was trained with the help of particle swarm algorithm, genetic algorithm, differential algorithm and ant colony algorithm optimized, and the optimal network for velocity prediction was obtained from fuzzy neural network with ant colony algorithm while for head loss fuzzy neural network with particle swarm algorithm was selected as the best model. According to the sensitivity analysis, diameter of pipes was found to be the most effective parameter in predicting velocity and pressure loss. Results showed the high capability of ANFIS in analyzing and predicting hydraulic properties of water supply tube.

由于压力或速度波动的影响，供水网络可能受到损坏。为了避免这种情况，非常必要的是对网络进行适当和优化的管理。在这项研究中，研究了控制压力和速度以防止供水网络出现问题，并且还通过自适应神经模糊推理系统（ANFIS）预测了水力特性。通过这种方式，首先根据分布参数将坎加瓦尔省（按案例研究）划分为六个分区，设计日供水时间为10年，目标人口为95,000的供水网络（基于22 h的日灌溉和设计期末使用的人均面积为29.6平方米。然后使用EPANET软件分析网络的压力和速度。根据结果​​，最大压力出现在第三压力区域的3–3个节点，估计100 m的水，网络速度的最大值估计为1.4 m /秒。此外，结果表明，根据管的直径在模型中使用了放电，并且在不同区域中选择的路径在适当的范围内。然后，利用实测值，通过优化的粒子群算法，遗传算法，微分算法和蚁群算法对模糊神经网络进行训练，并利用蚁群算法从模糊神经网络中获得了最优的速度预测网络。对于头部损失，采用粒子群算法的模糊神经网络被选为最佳模型。根据敏感性分析，发现管道直径是预测速度和压力损失的最有效参数。结果表明，ANFIS在分析和预测供水管的水力特性方面具有很高的能力。