1. Water Demand Model for the City of Makkah, Saudi Arabia AbdelHamid Ajbar, Emad Ali Chemical Engineering Department, King Saud University, Riyadh, Saudi Arabia

2. Motivations for Water Demand Model Saudi Arabia Arid country: little rain and no surface water Depends heavily on costly desalination plants Growing populations and economic activity Weak infrastructure management (Leak and Large Unaccounted-for-water)

3. Potable water resources Desalination => 80% ◦ Thermal desalination plant − 70% ◦ RO desalination – 30% Underground water => 20%

4. City Characteristics Makkah city, with a population of around 1.5 millions. Makkah is a focal point for local and international religious tourism The annual population’s growth estimated at 3% puts considerable strains on available water resources. The residential per capita water consumption in the city is estimated in 2010 to be 250 l/day

5. Econometric Water Demand model The standard functional population model for estimating total water use: Q = Nq Q is the total annual water use, N the population number and q is the water use per capita. The water use (q) is assumed to depend on a number of explanatory variables (X i ).

6. Explanatory Variables The selection of the explanatory variables is conditioned by the availability of historical data and also by the anticipated importance of the variable: – The household median income (I ) – The household size (i.e. persons per house) (Hs) – The maximum monthly temperature (T). – The monthly visitor flux (V)

7. Model Shortcomings Household and Income data are yearly based Temperature varies monthly Visitors vary monthly

8. Further Challenge Visitors distribution is well defined on the basis of lunar months Average monthly temperature is consistent with the solar months

9. Suggested Remedy Use NN model – allow for multi-rate variables Q = f(N,I,H,V,T) Model prediction should be based on lunar months Estimate the lunar monthly temperature using correlation between lunar and solar systems

10. Touristic Characteristics

11. Combined Tourist Flux

12. Monthly Temperature Distribution

13. Neural Network Structure

14. NN model Training

15. NN Model validation

16. Thank You