  Water plays a pivotal role in many physical processes, and most importantly
in sustaining human life, animal life and plant life. Water supply entities
therefore have the responsibility to supply clean and safe water at the rate
required by the consumer. It is therefore necessary to implement mechanisms and
systems that can be employed to predict both short-term and long-term water
demands. The increasingly growing field of computational intelligence
techniques has been proposed as an efficient tool in the modelling of dynamic
phenomena. The primary objective of this paper is to compare the efficiency of
two computational intelligence techniques in water demand forecasting. The
techniques under comparison are the Artificial Neural Networks (ANNs) and the
Support Vector Machines (SVMs). In this study it was observed that the ANNs
perform better than the SVMs. This performance is measured against the
generalisation ability of the two.
