Water Demand Prediction for Touristic Mecca City in Saudi Arabia using Neural Networks
Saudi Arabia is an arid country which depends on
costly desalination plants to satisfy the growing residential water
demand. Prediction of water demand is usually a challenging task
because the forecast model should consider variations in economic
progress, climate conditions and population growth. The task is
further complicated knowing that Mecca city is visited regularly by
large numbers during specific months in the year due to religious
occasions. In this paper, a neural networks model is proposed to
handle the prediction of the monthly and yearly water demand for
Mecca city, Saudi Arabia. The proposed model will be developed
based on historic records of water production and estimated visitors-
distribution. The driving variables for the model include annuallyvarying
variables such as household income, household density, and
city population, and monthly-varying variables such as expected
number of visitors each month and maximum monthly temperature.
[1] Bougadis J, Adamowski K, Diduch R ''Short-term municipal water
demand forecasting.'' Hydrological Process, 19:137-148, 2005.
[2] Davis WY, Water demand forecast methodology for California water
planning areas work plan and model review. Research Report.
California Bay-Delta Authority, CA, USA, 2003.
[3] Khatr KB, Vairavamoorthy K, Water demand forecasting for the city of
the future against the uncertainties and the global change pressures: Case
of Birmingham, EWRI/ASCE: Conference: Kansas, USA May 17-21,
2009.
[4] Zhou SL, McMahon TA, Walton A, Lewis J ''Forecasting daily urban
water demand: A case study of Melbourne.'' Journal of Hydrology,
236:153-164, 2000.
[5] Pao Y, Adaptive pattern recognition and neural networks, Addison-
Wesely, New York, NY, 1989.
[6] Liu J, Hubert HG, Xu J, ''Forecast of water demand in weinan city in
china using WDF-NN model,'' Physics and chemistry of the earth, 28,
219-224, 2003.
[7] Paliwal M, Kumar U, ''Neural networks and statistical techniques: a
review of applications,'' Expert systems with applications, 36, 2-17,
2009.
[8] Arbués F, Garc├¡a-Vali├▒as MA, Mart├¡nez-Espi├▒eira R, ''Estimation of
residential water demand: a state of the art review.'' Journal of
Socioeconomics. 32:81-102, 2003.
[9] CDSI, Central Department for Statistics and Information, Riyadh, Saudi
Arabia 2010.
[1] Bougadis J, Adamowski K, Diduch R ''Short-term municipal water
demand forecasting.'' Hydrological Process, 19:137-148, 2005.
[2] Davis WY, Water demand forecast methodology for California water
planning areas work plan and model review. Research Report.
California Bay-Delta Authority, CA, USA, 2003.
[3] Khatr KB, Vairavamoorthy K, Water demand forecasting for the city of
the future against the uncertainties and the global change pressures: Case
of Birmingham, EWRI/ASCE: Conference: Kansas, USA May 17-21,
2009.
[4] Zhou SL, McMahon TA, Walton A, Lewis J ''Forecasting daily urban
water demand: A case study of Melbourne.'' Journal of Hydrology,
236:153-164, 2000.
[5] Pao Y, Adaptive pattern recognition and neural networks, Addison-
Wesely, New York, NY, 1989.
[6] Liu J, Hubert HG, Xu J, ''Forecast of water demand in weinan city in
china using WDF-NN model,'' Physics and chemistry of the earth, 28,
219-224, 2003.
[7] Paliwal M, Kumar U, ''Neural networks and statistical techniques: a
review of applications,'' Expert systems with applications, 36, 2-17,
2009.
[8] Arbués F, Garc├¡a-Vali├▒as MA, Mart├¡nez-Espi├▒eira R, ''Estimation of
residential water demand: a state of the art review.'' Journal of
Socioeconomics. 32:81-102, 2003.
[9] CDSI, Central Department for Statistics and Information, Riyadh, Saudi
Arabia 2010.
@article{"International Journal of Earth, Energy and Environmental Sciences:54049", author = "Abdel Hamid Ajbar and Emad Ali", title = "Water Demand Prediction for Touristic Mecca City in Saudi Arabia using Neural Networks", abstract = "Saudi Arabia is an arid country which depends on
costly desalination plants to satisfy the growing residential water
demand. Prediction of water demand is usually a challenging task
because the forecast model should consider variations in economic
progress, climate conditions and population growth. The task is
further complicated knowing that Mecca city is visited regularly by
large numbers during specific months in the year due to religious
occasions. In this paper, a neural networks model is proposed to
handle the prediction of the monthly and yearly water demand for
Mecca city, Saudi Arabia. The proposed model will be developed
based on historic records of water production and estimated visitors-
distribution. The driving variables for the model include annuallyvarying
variables such as household income, household density, and
city population, and monthly-varying variables such as expected
number of visitors each month and maximum monthly temperature.", keywords = "Water demand forecast; Neural Networks model;
water resources management; Saudi Arabia.", volume = "6", number = "5", pages = "258-5", }