Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data.

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.

Low-cost failure sensor design and development for water pipeline distribution systems.

This paper describes the design and development of a new sensor which is low cost to manufacture and install and is reliable in operation with sufficient accuracy, resolution and repeatability for use in newly developed systems for pipeline monitoring and leakage detection. To provide an appropriate signal, the concept of a "failure" sensor is introduced, in which the output is not necessarily proportional to the input, but is unmistakably affected when an unusual event occurs. The design of this failure sensor is based on the water opacity which can be indicative of an unusual event in a water distribution network. The laboratory work and field trials necessary to design and prove out this type of failure sensor are described here. It is concluded that a low-cost failure sensor of this type has good potential for use in a comprehensive water monitoring and management system based on Artificial Neural Networks (ANN).

A neural network approach to burst detection.

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.