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

Abstract

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.

Keywords:

• water quality
• urban water management
• decision support systems
• data mining
• drinking water

Acknowledgements

The authors would particularly like to thank Mr. Hieue T. Nguyan for data preparation and initial experimentation. They also acknowledge the DWI in making publically available the annual reports.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Pipe Dreams project funded by the UK Science and Engineering Research Council, grant EP/G029946/1.