Multivariate time series forecasting of daily urban water demand using reinforcement learning and gated recurrent unit network

The role of water consumption prediction in urban water supply system dispatch is becoming increasingly significant, and reliable daily water demand prediction models are of great importance for the construction of smart water and smart cities. In response to the problems of existing models such as automatic optimization of hyperparameters and non-stationary, non-linear water consumption data, a fresh daily water consumption forecast model is suggested in this research. This model is based on the gated recurrent unit network model, using historical water consumption data, factors affecting water consumption, and reinforcement learning dynamic adjustment of hyperparameter strategies for training. The model is evaluated using a dataset of real water supply facilities. The findings demonstrate that the suggested model is superior to conventional prediction techniques and may contribute to more efficient and sustainable water management practices.