The efficiency of urban combined sewer systems during wet weather

Abstract

The intensive development of urban areas results in the sealing of increasingly large areas. In such conditions the existing sewer systems are quite often unable to simultaneously collect sewage along with the additional volume of rainwater. These systems require control of the hydraulic parameters in order to recognize the hydraulic conditions that occur in different operational states. Nowadays, such control may be exercised through the use of models that are capable of prediction as a result of the process of learning from a database of historical events. The study presents the possibilities of using Artificial Neural Networks (ANNs) for the analyses of the time series of waste-water depth and flows in a combined sewer system. The measurement campaign organized in Wrocław (Poland) enabled obtaining data on the hydraulic parameters of the flow of sewage in the sewer systems, and rainfall of various characteristics. The test results demonstrate that algorithms of the MLP (Multi-Layered Perceptron) Artificial Neural Network may be implemented to predict the flow rate in the system. The method presented in the paper may be applied to the daily operation of sewer systems to predict transient flows. The obtained results demonstrate a good and very good accuracy of prediction model.