Urban water supply dispatching based on improved BP neural network and simulated annealing algorithm

Amidst the swift progression of urbanization and the continuous amelioration of living standards in China, the exigencies placed upon urban water supply systems have escalated markedly. The extant urban water supply dispatching methodology, predicated on the BP neural network, is confronted with sundry obstacles, such as a languid rate of convergence and a propensity to ensnare in local minima. To efficaciously surmount these impediments, this treatise proffers an innovative resolution by amalgamating the simulated annealing algorithm with the BP neural network architecture. By invoking empirical case studies, the paper substantiates the augmented efficacy and pre-eminence of the BP neural network augmented with the simulated annealing algorithm in urban water supply dispatching. The findings indubitably illustrate that the BP-GSA model surpasses both the conventional BP model and the genetic algorithm (GA) model in terms of MSE, MAE, and R 2 . This enhancement markedly amplifies the prognostic precision and stability of the model. Additionally, based on a comprehensive survey, it is evident that the BP-GSA model also shows remarkable advantages in terms of water supply reliability, water supply cost, and overall customer satisfaction.