Msiza, ISNelwamondo, Fulufhelo VMarwala, T2009-05-122009-05-122008-11Msiza, 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 1-81798-203Xhttp://hdl.handle.net/10204/3374This 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)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 predictionenWater demand predictionArtificial neural networksSupport vector machinesModelling and digital scienceComputational intelligenceWater scarcityMulti-layer perceptronArticleMsiza, I., Nelwamondo, F. V., & Marwala, T. (2008). Water demand prediction using artificial neural networks and support vector regression. http://hdl.handle.net/10204/3374Msiza, IS, Fulufhelo V Nelwamondo, and T Marwala "Water demand prediction using artificial neural networks and support vector regression." (2008) http://hdl.handle.net/10204/3374Msiza I, Nelwamondo FV, Marwala T. Water demand prediction using artificial neural networks and support vector regression. 2008; http://hdl.handle.net/10204/3374.TY - Article AU - Msiza, IS AU - Nelwamondo, Fulufhelo V AU - Marwala, T AB - 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 DA - 2008-11 DB - ResearchSpace DP - CSIR KW - Water demand prediction KW - Artificial neural networks KW - Support vector machines KW - Modelling and digital science KW - Computational intelligence KW - Water scarcity KW - Multi-layer perceptron LK - https://researchspace.csir.co.za PY - 2008 SM - 1798-203X T1 - Water demand prediction using artificial neural networks and support vector regression TI - Water demand prediction using artificial neural networks and support vector regression UR - http://hdl.handle.net/10204/3374 ER -