Modelling flow dynamics in water distribution networks using artificial neural networks - A leakage detection technique

Abstract

Computational approaches can be used to detect leakages in water distribution networks. One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics can be compared with water demands in a particular district metered area. The objective of this study was to model flow dynamics in four district metered areas of the City of Harare, Zimbabwe using the ANNs technique in an effort to detect systems leakages. A multi-layer feed-forward back-propagation artificial neural network was used for modelling the flow and simulate water demand using a Matlab Neural Network Toolkit. The difference between actual water consumed (metered consumption) and the simulated water demand for a district metered area represents the water leakage in the water distribution network of the district metered area. It was discovered that an ANN could be trained and be used to forecast flow with up to 99% confidence. Thus, ANNs technique is a flexible and efficient approach to detection of leakages in water distribution networks.

Keywords: Artificial neural network; Leakage detection technique; Water distribution; Leakages