The Application of Fuzzy Neural Network Algorithm in Intelligent Scheduling of Urban Water Supply System

In the urban water supply system, facing the problems of water shortage, demand fluctuation and water supply safety, the traditional scheduling method is difficult to meet the growing demand for intelligence. Therefore, this study aims to apply the fuzzy neural network algorithm to optimize the intelligent scheduling of the urban water supply system. First, a neural network model based on fuzzy logic is constructed, integrating multiple input factors such as water source, user demand, pipe network status and meteorological data. Then, through data preprocessing, historical water supply data is cleaned and standardized to ensure the accuracy of model training. Then, the model is trained with historical data, the model parameters are optimized by cross-validation method, and the fuzzy rules are automatically adjusted by genetic algorithm to achieve refined scheduling. In the experiment, the model is tested by water supply data from different locations. The average water supply efficiency of the optimized scheduling model is 1.51. The application of fuzzy neural network algorithm in the intelligent scheduling of urban water supply system effectively solves the shortcomings of traditional methods and provides a new idea for achieving more efficient and intelligent water supply management.