Author, Subjects, Keywords

Cited Author

 
 
   » By Author or Editor
 » Browse Author by Alphabet
 » By Journal
 » By Subjects
 » Malaysian Journals
 » By Type
 » By Year
 » By Latest Additions
 
 
   » By Author
 » Top 20 Authors
 » Top 20 Article
 » Top Journal Cited
 » Top Article Cited
 » Journal Citation Statistics
 » Usage Since Sept 2007


 
 
 

Login | Create Account

Techniques To Develop Forecasting Model On Low Cost Housing In Urban Area

Noor Yasmin Zainun, and Muhd. Zaimi Abd. Majid, (2002) Techniques To Develop Forecasting Model On Low Cost Housing In Urban Area. Malaysian Journal of Civil Engineering, 14 (1). pp. 36-46.

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
356Kb

Official URL: http://web.utm.my/ipasa/images/stories/MJCE/2002/vol_14_no_1/TECHNIQUES%20TO%20DEVELOP%20FORECASTING%20MODEL%20ON%20LOW%20COST%20HOUSING%20IN%20URBAN%20AREA.pdf

Affiliations

Universiti Technologi Malaysia, Faculty of Civil Engineering, Construction Technology & Management Center
Universiti Technologi Malaysia, Faculty of Civil Engineering, Construction Technology & Management Center

Abstract

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.

Item Type:Journal
Keywords:urban area, accuracy, artificial neural network, forecasting
Subjects:T Technology, Engineering
ID Code:6535

Aiken, M. Wailer, B. and Greer, T. (2001) “Forecasting Construction Demand With A Neural Network”. [online]

Available http://www.sbaer.uca.edu/docs/proceedingsII/98dsi064.htm

Chander, R. (1976) “Housing Needs versus Effective Demand in Malaysia” Department of Statistic of Malaysia.

Goh, B. H. (1998) “Forecasting residential construction demand in Singapore: a comparative study of the

accuracy of time series, regression and artificial neural network technique” Engineering Construction and Architectural Management, 5, no.3 pp.261-275.

Hossein Arsham (2001) “Time Series Analysis and Forecasting Techniques-Europe Mirror Site”[online]

Available http://ubmail.ubalt.edu/~harsham/stat-ata/opre330Forecast.htm

Klerfors, D. (1998) “Artificial Neural Networks” [online] Available http://hem.hj.se/~de96klda/NeuralNetworks.htm

Kamal Salih (1976) “Socio-economic Consideration in the Planning, Design and Implementation of Human Settlements and Housing For Lower Income Groups in Malaysia” The Conference of Human Settlements for the Rakyat in the Lower

Income Group, Paper No. 5.

Myers, J.M. and Smith, S.C. (2001) “Housing Starts: a Year 2000 Forecast Using Long Term Interest Rates and Total Consumption Spending” [online] Available http://www3.wcu.edu/~mulligan/NCE99-13.htm

Yang and Packer (1997) “Applying Artificial Neural Networks to UK Construction Demand Forecasting (Private Sector)” COBRA ’97, pp. 1-16.

Repository Staff Only: item control page
 
 
 
 
 
 
© Copyright 2007 MyAIS Faculty of Computer Science and Information Technology, University of Malaya – All Rights Reserved. Malaysian Abstracting and Indexing System is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.