Multi-source data-driven estimation of maximum carrying capacity of urban water storage facilities under extreme conditions

Abstract

With the deepening of urbanization and rapid economic development, urban water storage systems face increasing challenges. In this paper, the behavioral mechanism of urban water storage system is deeply analyzed by using the system dynamics method, and a system dynamics model of the carrying capacity of urban water storage equipment is established. Further, based on the gray correlation theory, a prediction model of the carrying capacity of urban water storage equipment is constructed and accuracy is examined. The study estimated the maximum carrying capacity of urban water storage equipment through performance analysis. The results show that the relative error of the fitted data is deficient, indicating that the model is highly accurate. The empirical Analysis of the carrying capacity index of the urban economy and water environment pollution is high. The prediction results for 2030 show that the carrying capacity of water storage facilities in City M is 0.22, which is already slightly overloaded and faces a severe risk of overloading. The model proposed in this study can not only accurately predict the trend of the carrying capacity of water storage equipment, but also effectively predict the overloading problem of urban water storage equipment, which provides a scientific basis for the optimization and improvement of urban water storage equipment, and an essential support for the formulation of the city’s sustainable development strategy.