JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3    Issue : 11    Date : November 2008

Water Demand Prediction using Artificial Neural Networks and Support Vector Regression
Ishmael S. Msiza, Fulufhelo V. Nelwamondo, and Tshilidzi Marwala
Page(s): 1-8
Full Text:
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Abstract
Computational Intelligence techniques have been proposed as an efficient tool for modeling and
forecasting in recent years and in various applications. Water is a basic need and as a result, water
supply entities 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 modeling 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 Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). In this
study it was observed that ANNs perform significantly better than SVMs. This performance is
measured against the generalization ability of the two techniques in water demand prediction.

Index Terms
Support Vector Machines, Neural networks