The goal of a water supply system is the distribution of water with the desired quality, quantity and continuity of supply of drinking water to consumers. In some cases, the water distribution system is unable to provide safe water due to water quality failures that may have devastating consequences. If the prognostic analysis and necessary actions are carried out on the basis of timely risk assessment results, the frequency and, as a result, water quality failure (WQF) can be reduced. Failure to meet water quality requirements in the urban distribution network is based on the standard of drinking water. In this study, a qualitative modeling water distribution system model has been developed to assess the risk of water quality failure in anytown water distribution system. In this research, we also use an analysis based on the demand due to the lack of network reliability during the removal of pollution. Assessing the risk of water quality failures in a water supply network based on criteria as ways to cause water quality problems. These criteria include biofilm formation including velocity, water age, tube roughness and temperature, distribution of chemical and corrosive substances including the following criteria: soluble solubility, dissolved oxygen, temperature and pH, contaminant influence on fracture including pressure, negative pressure, degradation and performance, non-purification It includes the following remaining chlorine standards, depreciation of equipment, human error and DBP (chloroform). Using numerical results simulating this model, the probability of occurrence of some of the sub-criteria was determined by the simulator model. In the next step, their weights were obtained using the best-worst-fuzzy (FBWM) multi-criteria analysis method. Firstly, for each criterion, the best and worst sub-criteria for paired comparison with expert opinions and other sub-criteria were determined with the best and worst of each comparison criterion. The fuzzy numbers were determined by their pairwise comparison for linear programming for obtaining weights using Lingo software. Finally, using the AutoCad software, the area of ??the network loops was obtained. Then, the surface coverage of each tube was determined and considering the hypothetical density for each section of the population covered by the pipes. Given the hypothetical category, the severity of the risk of the criteria was calculated and multiplied by the probability of occurrence. As a result, the risk of water network failure was achieved. In the next stage, using FVIKOR method, the risk of failure of water quality in the pipes and grooves was ranked based on three parameters R, Q, S, using the Excel program, where the Q parameter was cited as the final answer. The final risk of each unit and unit was determined in the form of a deviation that the most risk of water quality failure was related to pipe 34 and node 7 with values of 85% and 83% respectively, and the lowest of pipe 35 and node 17 respectively with 0.1% and 0.1%. In the final stage, the sensitivity analysis was performed on the Q parameter in such a way that in four steps each time the risk of one of the criteria was zero. Eventually, the mean parameter risk Q was obtained. At this stage, it was observed that the most sensitive is the rate of emission of corrosive chemicals, which is the main pH underlying criterion. Keywords: Water Distribution System, Water Quality Failure, Multi-criteria Analysis of Best-Worst, Fuzzy Vectors