Department of Civil and Structural Engineering, Pennine Water Group, University of Sheffield, Sheffield, S1 3JD, UK E-mail:
Water Sci Technol. 2014;69(6):1326-33. doi: 10.2166/wst.2014.024.
Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. To better understand the performance of CSOs, the UK water industry has installed a large number of monitoring systems that provide data for these assets. This paper presents research into the prediction of the hydraulic performance of CSOs using artificial neural networks (ANN) as an alternative to hydraulic models. Previous work has explored using an ANN model for the prediction of chamber depth using time series for depth and rain gauge data. Rainfall intensity data that can be provided by rainfall radar devices can be used to improve on this approach. Results are presented using real data from a CSO for a catchment in the North of England, UK. An ANN model trained with the pseudo-inverse rule was shown to be capable of predicting CSO depth with less than 5% error for predictions more than 1 hour ahead for unseen data. Such predictive approaches are important to the future management of combined sewer systems.
合流制污水溢流（CSO）是合流制城市排水系统中的常见特征，用于在暴雨期间将过量的水排放到环境中。为了更好地了解 CSO 的性能，英国水行业已经安装了大量的监测系统，为这些资产提供数据。本文研究了使用人工神经网络（ANN）替代水力模型来预测 CSO 的水力性能。以前的工作已经探索了使用 ANN 模型来预测使用深度和雨量计数据的时间序列的腔室深度。可以使用降雨雷达设备提供的降雨强度数据来改进这种方法。使用来自英国北部集水区的 CSO 的实际数据呈现结果。结果表明，使用伪逆规则训练的 ANN 模型能够以小于 5%的误差预测 CSO 深度，对于超过 1 小时的未见数据的预测。这种预测方法对于未来的合流制污水系统管理非常重要。
