Affiliations: Associate Professor, Department of Civil Engineering,
Kalasalingam University, Srivilliputtur, Virudhunagar – 626 190, Tamilnadu,
India | Department of Civil Engineering, Mepco Schlenk
Engineering College, Sivakasi – 626 005, Tamilnadu, India
Abstract: Industrial and municipal water distribution networks often have a
considerable amount of water lost in transit, particularly in a country like
India. Several attempts are being made to detect leaks, of which the physical
methods are more commonly employed. However, such methods are reported to be
less efficient and time consuming. This paper presents a Neural Network based
approach to aid the physical methods. The methodology is demonstrated in a
hypothetical unsymmetrical water distribution network. EPANET is chosen to
model the hydraulic behaviour of the system. The effect of
non-operative/defective pressure meter in the leaking pipe is also studied. It
is seen that the proposed methodology performs remarkably well in predicting
the exact leaking pipe, the location of leak in the pipe and the leak size. The
prediction of leak location in the leaking pipe is more sensitive to the
pressure signals from the leaking pipe. Thus, it is concluded that a simple
simulation study can be very effective in considerably reducing the time in
field methods of leak identification.
Keywords: Leak detection, neural network, EPANET, water distribution network
