Identifying Sampling Interval for Event Detection in Water Distribution Networks

It is a generally adopted policy, albeit unofficially, to sample flow and pressure data at a 15-min interval for water distribution system hydraulic measurements. Further, for flow, this is usually averaged, whereas pressure is instantaneous. This paper sets out the findings of studies into the potential benefits of a higher sampling rate and averaging for flow and pressure measurements in water distribution systems. A data set comprising sampling at 5 s (in the case of pressure), 1 min, 5 min, and 15 min, both instantaneous and averaged, for a set of flow and pressure sensors deployed within two DMAs has been used. Engineered events conducted by opening fire hydrants/wash outs were used to form a controlled baseline detection comparison with known event start times. A data analysis system using support vector regression (SVR) was used to analyze the flow and pressure time series data from the deployed sensors and hence, detect these abnormal events. Results are analyzed over different sensors and events. The overall trend in the results is that a faster sampling rate leads to earlier event detection. However, it is concluded that a sampling interval of 1 or 5 min does not significantly improve detection to the point at which it is worth the added increase in power, communications, and data management requirements with current technologies. It was discovered that averaging pressure data can result in more rapid detection when compared with using the same instantaneous sampling rate. Averaging of pressure data is also likely to provide better regulatory compliance and provide improved data for EPS hydraulic modelling. This improvement can be achieved without any additional overheads on communications by a simple firmware alteration and hence, is potentially a very low cost upgrade with significant gains

Case-Based Reasoning Approach For Managing Water Quality Incidents In Distribution Systems

Access to safe drinking water is universally considered as a fundamental human right and customers regard a reliable supply of safe, clean water as the most important aspect of the water supply service. However, water quality failures do occur, with some of the hardest to understand and manage occurring within distribution systems. In the UK, a regulatory process is applied in which water companies must report on significant water quality incidents, their causes, actions, responses, and outcomes. The Drinking Water Inspectorate (DWI) assesses these reports on an annual basis and their findings are made publically available. It is hypothesised here that these reports form a valuable resource that can be ‘data mined’ for improved understanding and to help with future incident management. Developed in the late 1970s, case-based reasoning (CBR) is a knowledge-based problem-solving technique that relies on the reuse of past experience. It is based on the assumption that similar problems have similar solutions and hence new problems can be solved by reusing (and adapting) solutions. The WaterQualityCBR software system, reported on here, was developed as a decision support tool for water companies to deal more effectively with water quality incidents (e.g. water discolouration, contamination and loss of supply) by using information from previous incidents. The tool manipulates a database (compiled in XML) of past significant events from several years DWI reporting. The system can provide information at a strategic level, for example to help inform policy or water company guidance documents. In addition, a complete closed CBR cycle is possible for operational event management providing information from similar cases from the past and, importantly, ranking past actions in response to similar incidents. Examples are provided to illustrate both aspects of the software, demonstrating how the CBR methodology can support decision-making for water utilities in managing drinking water incidents

Case-based reasoning to support decision making for managing drinking water quality events in distribution systems

In order to better leverage past experience of water quality incidents, and to tap into the unique incident database currently being maintained and required by regulatory authorities, a data mining approach is herein proposed. The quality of drinking water is paramount to protecting public health. However water quality failures do occur, with some of the hardest to understand and manage occurring within distribution systems. In the UK, a regulatory process is applied in which water service providers must report on significant water quality incidents, their causes, actions and outcomes. These reports form a valuable resource that can be explored for improved understanding, to help with future incident management and evaluate potential solutions. Case-based reasoning is a knowledge-based problem-solving technique that relies on the reuse of past experience. The WaterQualityCBR software system presented here was developed as such a decision support tool to more effectively manage water quality in distribution systems

Pattern matching and associative artificial neural networks for water distribution system time series data analysis

Peer reviewedPublisher PD