Although failures in water distribution systems are inevitable, compromising the levels of
service to the consumer during failures is not acceptable. It is common for a failure in the
distribution system to cause a reduction in pressures resulting in a reduced nodal flow to
consumers. In order to predict the reduction in the levels of service as a result of the
reduced flows, it is important to relate pressure changes with nodal outflows during
failure events.
Conventional network analysis methods do not allow the nodal outflow to be adjusted as
a result of pressure reduction, as the models in general are demand driven. Modified
network analysis is required where pressure dependent outflow functions are used.
However many limitations associated with pressure dependent demand functions have
been reported in the literature and these limitations restrict their applicability to real
networks.
In this paper a new method based on artificial neural networks is presented for relating
pressures and nodal outflows. The proposed method involves the detailed analysis of
several typical secondary networks, to provide sufficient data to train the neural network.
Once trained the neural network is used to calculate the reduced outflows to nodes, based
on the secondary network type, time of failure and the resulting reduced pressure. The
paper presents an example application to demonstrate the method and its strength over
previous methods. Includes 18 references, tables, figures.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 520 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 19 |
| Published : | 04/01/2005 |
