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.

It has been hypothesized that negative pressures caused by transients within water distribution systems may result in ingress of contaminated groundwater through leaks and hence pose a risk to public health. This paper presents results of contaminant ingress experiments from a novel laboratory facility at The University of Sheffield. An engineered leak surrounded by porous media was subjected to pressure transients resulting from the rapid closure of an upstream valve. It has been shown that a pollutant originating externally was drawn in and transported to the end of the pipe loop. This paper thus presents the first fully representative results proving the occurrence and hence, risk to potable water quality of contaminant ingress.

Urban drainage systems are frequently analysed using hydraulic modelling software packages such as InfoWorks CS or MIKE-Urban. The use of such modelling tools allows the evaluation of sewer capacity and the likelihood and impact of pluvial flood events. Models can also be used to plan major investments such as increasing storage capacity or the implementation of sustainable urban drainage systems. In spite of their widespread use, when applied to flooding the results of hydraulic models are rarely compared with field or laboratory (i.e. physical modelling) data. This is largely due to the time and expense required to collect reliable empirical data sets. This paper describes a laboratory facility which will enable an urban flood model to be verified and generic approaches to be built. Results are presented from the first phase of testing, which compares the sub-surface hydraulic performance of a physical scale model of a sewer network in Yorkshire, UK, with downscaled results from a calibrated 1D InfoWorks hydraulic model of the site. A variety of real rainfall events measured in the catchment over a period of 15 months (April 2008–June 2009) have been both hydraulically modelled and reproduced in the physical model. In most cases a comparison of flow hydrographs generated in both hydraulic and physical models shows good agreement in terms of velocities which pass through the system.

The sewer system is a service that is expected to function without interruptions. Continuous assessment, maintenance and rehabilitation are the key to maintaining a required level of service at an acceptable cost. An appropriate and cost effective prioritisation scheme for periodical surveys could be built based on failure data collected by sewerage companies over time. Such a scheme could be achieved using data-driven modelling techniques jointly with engineering knowledge of the failure mechanisms. This paper presents a descriptive analysis performed on a real database containing collapse and blockage incident records for a large sewer system in the UK. Starting from a statistical study of both failure types, the most important variables are identified and a classification scheme is suggested. Then, using a hybrid modelling technique, evolutionary polynomial regression, two different formulas for blockage events and collapse failures are obtained and their engineering interpretation is offered.

Urban flooding damages properties, causes economic losses and can seriously threaten public health. An innovative, fuzzy logic (FL)-based, local autonomous real-time control (RTC) approach for mitigating this hazard utilising the existing spare capacity in urban drainage networks has been developed. The default parameters for the control algorithm, which uses water level-based data, were derived based on domain expert knowledge and optimised by linking the control algorithm programmatically to a hydrodynamic sewer network model. This paper describes a novel genetic algorithm (GA) optimisation of the FL membership functions (MFs) for the developed control algorithm. In order to provide the GA with strong training and test scenarios, the compiled rainfall time series based on recorded rainfall and incorporating multiple events were used in the optimisation. Both decimal and integer GA optimisations were carried out. The integer optimisation was shown to perform better on unseen events than the decimal version with considerably reduced computational run time. The optimised FL MFs result in an average 25% decrease in the flood volume compared to those selected by experts for unseen rainfall events. This distributed, autonomous control using GA optimisation offers significant benefits over traditional RTC approaches for flood risk management.