Techniques to develop forecasting model on low cost housing in urban area

The number of people who will live in urban areas is expected to double to more than five billion between 1990 to 2025. Therefore, accurate predictions of the level of aggregate demand for housing are very important. Various forecasting techniques have been developed using probabilistic, statistics,...

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Main Authors: Zainun, Noor Yasmin, Abd. Majid, Muhd. Zaimi
Format: Article
Language:English
Published: Faculty of Civil Engineering 2002
Subjects:
Online Access:http://eprints.utm.my/2063/
http://eprints.utm.my/2063/1/NoorYasminZainun2002_TechniquesToDevelopForecastingModel.pdf
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author Zainun, Noor Yasmin
Abd. Majid, Muhd. Zaimi
author_facet Zainun, Noor Yasmin
Abd. Majid, Muhd. Zaimi
author_sort Zainun, Noor Yasmin
building UTeM Institutional Repository
collection Online Access
description The number of people who will live in urban areas is expected to double to more than five billion between 1990 to 2025. Therefore, accurate predictions of the level of aggregate demand for housing are very important. Various forecasting techniques have been developed using probabilistic, statistics, simulation or artificial intelligent. Hence, there is a need to identify different techniques, in terms of accuracy, in the prediction of needs for facilities. This paper discusses the Artificial Neural Networks (ANN) technique and compaes it with other techniques in forecasting needs of housing in urban area. Investigation on previous research and literature materials will be derived and compared in terms of errors in the accuracy of the technique. The findings of this study indicates that the ANN model performs best overall
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publishDate 2002
publisher Faculty of Civil Engineering
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spelling utm-20632010-09-28T02:33:54Z http://eprints.utm.my/2063/ Techniques to develop forecasting model on low cost housing in urban area Zainun, Noor Yasmin Abd. Majid, Muhd. Zaimi TA Engineering (General). Civil engineering (General) The number of people who will live in urban areas is expected to double to more than five billion between 1990 to 2025. Therefore, accurate predictions of the level of aggregate demand for housing are very important. Various forecasting techniques have been developed using probabilistic, statistics, simulation or artificial intelligent. Hence, there is a need to identify different techniques, in terms of accuracy, in the prediction of needs for facilities. This paper discusses the Artificial Neural Networks (ANN) technique and compaes it with other techniques in forecasting needs of housing in urban area. Investigation on previous research and literature materials will be derived and compared in terms of errors in the accuracy of the technique. The findings of this study indicates that the ANN model performs best overall Faculty of Civil Engineering 2002 Article PeerReviewed application/pdf en http://eprints.utm.my/2063/1/NoorYasminZainun2002_TechniquesToDevelopForecastingModel.pdf Zainun, Noor Yasmin and Abd. Majid, Muhd. Zaimi (2002) Techniques to develop forecasting model on low cost housing in urban area. Jurnal Kejuruteraan Awam, 14 (1). pp. 36-46. ISSN 0128-0147 http://web.utm.my/ipasa/index.php?option=content&task=view&id=771&Itemid=
spellingShingle TA Engineering (General). Civil engineering (General)
Zainun, Noor Yasmin
Abd. Majid, Muhd. Zaimi
Techniques to develop forecasting model on low cost housing in urban area
title Techniques to develop forecasting model on low cost housing in urban area
title_full Techniques to develop forecasting model on low cost housing in urban area
title_fullStr Techniques to develop forecasting model on low cost housing in urban area
title_full_unstemmed Techniques to develop forecasting model on low cost housing in urban area
title_short Techniques to develop forecasting model on low cost housing in urban area
title_sort techniques to develop forecasting model on low cost housing in urban area
topic TA Engineering (General). Civil engineering (General)
url http://eprints.utm.my/2063/
http://eprints.utm.my/2063/
http://eprints.utm.my/2063/1/NoorYasminZainun2002_TechniquesToDevelopForecastingModel.pdf