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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/3374

Title: Water demand prediction using artificial neural networks and support vector regression
Authors: Msiza, IS
Nelwamondo, FV
Marwala, T
Keywords: Water demand prediction
Artificial neural networks
Support vector machines
Modelling and digital science
Computational intelligence
Water scarcity
Multi-layer perceptron
Issue Date: Nov-2008
Publisher: Academy Publisher
Citation: Msiza, IS, Nelwamondo, FV and Marwala, T. 2008. Water demand prediction using artificial neural networks and support vector regression. Journal of Computers, Vol. 3(11), pp 1-8
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
Description: This is the final published/PDF version of the article and permission by the publisher (Academy Publisher) has been granted for putting it on the CSIR's Open Access Institutional Repository (Research Space)
URI: http://hdl.handle.net/10204/3374
ISSN: 1798-203X
Appears in Collections:Digital intelligence
General science, engineering & technology

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