Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/46418
Title: Estimating willingess to pay for urban water supply: A comparison of artificial neural networks and multiple regression analysis
Authors: Ranasinghe, M.
Bee-Hua, G. 
Barathithasan, T.
Keywords: Artificial neural networks
Costs
Multiple regression
Water supply
Willingness to pay
Issue Date: 1999
Source: Ranasinghe, M.,Bee-Hua, G.,Barathithasan, T. (1999). Estimating willingess to pay for urban water supply: A comparison of artificial neural networks and multiple regression analysis. Impact Assessment and Project Appraisal 17 (4) : 273-281. ScholarBank@NUS Repository.
Abstract: A study was carried out to estimate willingness to pay (WTP) for urban water supply, for three types of consumer typically found in urban areas in developing countries-those receiving water through: existing connections; stand posts; and water wells. Two forecasting techniques were applied: artificial neural networks (ANN), a state-of-the-art technique, and multiple regression (MR), a conventional method. A comparison of the forecasting accuracy of the two models was made, using the mean absolute percentage error. The forecasting error of the best ANN model was found to be about half that of the best MR model. The MR models showed that the significant variables determining WTP differ by type of consumer, and from those variables that are typically considered significant for WTP by urban consumers in developed countries.
Source Title: Impact Assessment and Project Appraisal
URI: http://scholarbank.nus.edu.sg/handle/10635/46418
ISSN: 14615517
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

86
checked on Dec 8, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.