Deep learning-based intelligent management for sewage treatment plants

It is generally believed that intelligent management for sewage treatment plants (STPs) is essential to the sustainable engineering of future smart cities. The core of management lies in the precise prediction of daily volumes of sewage. The generation of sewage is the result of multiple factors from the whole social system. Characterized by strong process abstraction ability, data mining techniques have been viewed as promising prediction methods to realize intelligent STP management. However, existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects. To address this challenge, a deep learning-based intelligent management mechanism for STPs is proposed, to predict business volume. Specifically, the grey relation algorithm (GRA) and gated recursive unit network (GRU) are combined into a prediction model (GRA-GRU). The GRA is utilized to select the factors that have a significant impact on the sewage business volume, and the GRU is set up to output the prediction results. We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.