%0 Journal Article
	%A Asar Khan and  Peter D. Widdop and  Andrew J. Day and  Aliaster S. Wood and  Steve and  R. Mounce and  John Machell
	%D 2008
	%J International Journal of Physical and Mathematical Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 21, 2008
	%T Artificial Neural Network Model for a Low Cost Failure Sensor: Performance Assessment in Pipeline Distribution
	%U https://publications.waset.org/pdf/1643
	%V 21
	%X This paper describes an automated event detection and location system for water distribution pipelines which is based upon low-cost sensor technology and signature analysis by an Artificial
Neural Network (ANN). The development of a low cost failure sensor which measures the opacity or cloudiness of the local water
flow has been designed, developed and validated, and an ANN based system is then described which uses time series data produced by
sensors to construct an empirical model for time series prediction and
classification of events. These two components have been installed,
tested and verified in an experimental site in a UK water distribution
system. Verification of the system has been achieved from a series of
simulated burst trials which have provided real data sets. It is concluded that the system has potential in water distribution network
management.
	%P 690 - 696