water demand model for the city of makkah, saudi arabia abdelhamid ajbar, emad ali chemical...
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Water Demand Model for the City Water Demand Model for the City of Makkah, Saudi Arabiaof Makkah, Saudi Arabia
AbdelHamid Ajbar, Emad Ali
Chemical Engineering Department, King Saud University, Riyadh, Saudi Arabia
Motivations for Water Motivations for Water Demand ModelDemand Model
Saudi ArabiaArid 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)
Potable water resourcesPotable water resources
Desalination => 80%
◦Thermal desalination plant − 70%◦RO desalination – 30%
Underground water => 20%
City CharacteristicsCity 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
Econometric Water Demand Econometric Water Demand modelmodelThe standard functional population model for
estimating total water use:
Q = NqQ 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 (Xi).
2121 XXq
Explanatory VariablesExplanatory 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)
Model ShortcomingsModel Shortcomings
Household and Income data are yearly based
Temperature varies monthlyVisitors vary monthly
4321 VTHIq
Further ChallengeFurther Challenge
Visitors distribution is well defined on the basis of lunar months
Average monthly temperature is consistent with the solar months
Suggested RemedySuggested Remedy
Use NN model – allow for multi-rate variables
Q = f(N,I,H,V,T)Model prediction should be based
on lunar monthsEstimate the lunar monthly
temperature using correlation between lunar and solar systems
Touristic CharacteristicsTouristic Characteristics
Combined Tourist FluxCombined Tourist Flux
1 2 3 4 5 6 7 8 9 10 11 120
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Lunar Month
Tot
al v
isit
ors
(x10
00,0
00)
Monthly Temperature Monthly Temperature DistributionDistribution
1 2 3 4 5 6 7 8 9 10 11 1226
28
30
32
34
36
38
40
42
44
Month
Tem
pera
ture
(o C
)
Average Gregorian
2003
2004
2005
2006
Neural Network Neural Network StructureStructure
input layer hidden layer output layer
I
N
H
)(f
)(f
)(f
)(f
)(f
)(f
)(f
T1
T12
V1
V12
D1
D2
D11
D12
w1
w2
w11
w12
NN model TrainingNN model Training
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 510.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
Lunar Month
Wat
er P
rodu
ctio
n (1
06 m3 /D
)Training dataModel prediction
NN Model validationNN Model validation
0 3 6 9 12 15 18 21 24 270.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0.42
0.44
Lunar Month
Wat
er P
rodu
ctio
n (1
06 m3 /D
)Validation data
Model prediction
Thank YouThank You
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