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

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

doi:10.1109/IJCNN.2007.4370923

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

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

University of the Witwatersrand

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 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 language	English
Title of host publication	The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
Pages	13-18
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2007.4370923
Publication status	Published - 2007
Externally published	Yes
Event	2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States Duration: 12 Aug 2007 → 17 Aug 2007

Publication series

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

Conference

Conference	2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/Territory	United States
City	Orlando, FL
Period	12/08/07 → 17/08/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 6 Clean Water and Sanitation

ASJC Scopus subject areas

Software

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10.1109/IJCNN.2007.4370923

Cite this

Msiza, I. S., Nelwamondo, F. V., & Marwala, T. (2007). Water demand forecasting using multi-layer perceptron and radial basis functions. In The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings (pp. 13-18). Article 4370923 (IEEE International Conference on Neural Networks - Conference Proceedings). https://doi.org/10.1109/IJCNN.2007.4370923

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title = "Water demand forecasting using multi-layer perceptron and radial basis functions",

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.",

author = "Msiza, \{Ishmael S.\} and Nelwamondo, \{Fulufhelo V.\} and Tshilidzi Marwala",

year = "2007",

doi = "10.1109/IJCNN.2007.4370923",

language = "English",

isbn = "142441380X",

series = "IEEE International Conference on Neural Networks - Conference Proceedings",

pages = "13--18",

booktitle = "The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings",

note = "2007 International Joint Conference on Neural Networks, IJCNN 2007 ; Conference date: 12-08-2007 Through 17-08-2007",

}

Msiza, IS, Nelwamondo, FV & Marwala, T 2007, Water demand forecasting using multi-layer perceptron and radial basis functions. in The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings., 4370923, IEEE International Conference on Neural Networks - Conference Proceedings, pp. 13-18, 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, United States, 12/08/07. https://doi.org/10.1109/IJCNN.2007.4370923

Water demand forecasting using multi-layer perceptron and radial basis functions. / Msiza, Ishmael S.; Nelwamondo, Fulufhelo V.; Marwala, Tshilidzi.
The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings. 2007. p. 13-18 4370923 (IEEE International Conference on Neural Networks - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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

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