Water demand forecasting using multi-layer perceptron and radial basis functions

Ishmael S. Msiza, Fulufhelo V. Nelwamondo, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Citations (Scopus)

Abstract

Reliable and effective management of an existing water supply entity requires both long-term and short-term water demand forecasts. Conventionally, demographic and statistical models have been employed in modeling water demand forecasts. The technique of artificial neural networks has been proposed as an efficient tool for modeling and forecasting in recent years. The primary objective of this study is to investigate artificial neural networks for forecasting both short-term and long-term water demand in the Gauteng Province, in the Republic of South Africa. Neural network architectures used in this paper are the multi-layer perceptron (MLP) and the radial basis function (RBF). It was observed that the RBF converges to a solution faster than the MLP and it is the most accurate and the most reliable tool in terms of processing large amounts of non-linear, non-parametric data in this investigation.

Original languageEnglish
Title of host publicationThe 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages13-18
Number of pages6
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States
Duration: 12 Aug 200717 Aug 2007

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576

Conference

Conference2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/TerritoryUnited States
CityOrlando, FL
Period12/08/0717/08/07

ASJC Scopus subject areas

  • Software

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