Transactions Template


 

  

JOURNAL OF ENGINEERING RESEARCH AND TECHNOLOGY, VOLUME 1, ISSUE 1, FEBURARY 2014 

 

  

24  

Genetic Algorithm Model to Optimize Water Resources Allocation 

in Gaza Strip  

Said M. Ghabayen, Ibrahim M Madi, Khalid A Qahman, and Basim I. Sirdah 

Abstract— Groundwater aquifer is considered the main and only water supply source for all kind of human usage in Gaza Strip 

(domestic, agricultural and industrial). This source is severely deteriorated in both quality and quantity for many reasons, includ-

ing low rainfall, dramatic increase in the urban areas and population, pollution from overland activities, and seawater intrusion. 

In 2011, the Palestinian Water Authority has instituted a plan for integrated management of Gaza water resources that considers 

introducing of new external water resources to the system such as seawater desalination and treatment and reuse of wastewater. 

In this work, a genatic algorithm model was developed to seek the optimal combination of the management scenarioios of Pales-

tinian water authority plan. The optimization code is designed and run using MATLAB R2011b. The objective function maxim-
ized the benefits and minimizes the cost related to the use of different sources of water. The decision variables represents water 

allocation over different users sectors. The benefits from utilizing water for municipal and industrial purposes are based on 
the marginal value of water which is derived from the economic equilibrium point between supply and demand curves. The 

benefits from irrigation water are affected by the relationship between crop yield and salinity. The constraints in the optimiza-

tion model are allowed to iterate between two bounds (upper bound and lower bound) until the optimal value for each variable 

is found. The results show that there is a significant improvement in aquifer’s water levels in the majority area of the Gaza Strip 

for the planning years 2015, 2025, and 2035 providing that the planned phased desalination and wastewater treatment schemes 

are implemented in the specifies time horizon. The results show that the resulted quality of available water for agriculture use 

in term of total weighted average of electrical conductivity is 962 µS/cm in the year 2015, and 876 µS/cm in the year 2025, 

and 842 µS/cm in the planning year 2035. The results also show that the resulted quality of available water for municipal and 
industrial use in term of total weighted average of electrical conductivity is 867 µS/cm in the year 2015, and 685 µS/cm in the 

year 2025, and 631 µS/cm in the planning year 2035. 

 
Index Terms— Gaza Strip, Optimization, Genetic algorithms, water resources allocation, marginal value of water 

I INTRODUCTION

A  Water Resources Optimization 
The availability of freshwater is imperative to economic and 

social development. Therefore, sources of freshwater should be 

managed in a sustainable manner. Sustainable water resource 

systems include three integrated processes, namely the natural 

environment, the socio-economic environment and the man-

agement system. The purpose of the integrated system is not 

only to use natural resources without degrading the quality of 

the water or land, but also to ensure that present and future wa-

ter demands are met irrespective of the changes in circum-

stances [1]. 

The optimization model consists of an objective function or a 

quantity that is maximized or minimized, and a set of additional 

constraints or conditional statements that must be satisfied. In 

Recent years, optimization has been widely used in groundwa-

ter planning and management models. In the past decade, non-

linear programming techniques have been applied to ground-

water management models since these often give rise to highly 

non-convex and non-linear programming problems [2]. 

  

 

 

Most of optimization problems related to the interaction of 

groundwater resources and socioeconomic activities are non-

linear. The non-linearity comes from the complexity ground-

water system. In addition cost functions tend to change no-lin-

early with economy of scale [3]. Genetic algorithms (GA) have 

been used extensively for solving complex and highly non-lin-

ear optimization problems. Genetic algorithms are based on a 

random search scheme based inspired by biological evolution. 

GA is an optimization technique based on the process of bio-

logical evolution [4]. The concept of GA is based on the initial 

selection of a relatively small population. Each individual in the 

population represents a possible solution in the parameter 

space. The fitness of each individual is determined by the value 

of the objective function calculated, based on that set of param-

eters. The natural evolutional processes of reproduction, cross-

over, mutation, and selection, are applied using probability 

rules to evolve the new and better generations. The evolution 

based search algorithms claim to find much better near-optimal 

solution than any other optimization method [5].  

 

 

 

 Head of Environmental Engineering Department, Islamic University of Gaza, Gaza, Palestine.   
 Projectr Manager, Engineering and Environmental Protection Department, United Nations Mission in Darfur (UNAMID). 
 General Director, Environmental Quality Authority, Gaza, Palestine. 
 Research Assistant, Civil Engineering Department, Islamic University of Gaza, Gaza, Palestine. 

 



Genetic Algorithm Model to Optimize Water Resources Allocation in Gaza Strip Said M. Ghabayen, Basim I. Sirdah, and Ibrahim Madi/ 

Research Name (2014) 

 

  

25  

 

B  About the Study Area 
Groundwater aquifer is considered the main and only water 

supply source for all kind of human usage in Gaza Strip (do-

mestic, agricultural and industrial). This source is severely de-

teriorated in both quality and quantity for many reasons, in-

cluding low rainfall, dramatic increase in the urban areas and 

population, pollution from overland activities, and seawater in-

trusion. In 2011, the Palestinian Water Authority has instituted 

a plan for integrated management of Gaza water resources that 

considers introducing of new external water resources to the 

system such as seawater desalination and treatment and reuse 

of wastewater. In this work, a genatic algorithm model was de-

veloped to seek the optimal combination of the management 

scenarioios of Palestinian water authority plan [6]. 

II METHODOLOGY  

The optimization code is designed and run using MATLAB 

R2011b. The code initializes a random sample of individuals 
with different parameters to be optimized using the genetic al-

gorithm approach.  

A  Objective Function 
The objective of the management model is to maximize the to-

tal benefits from the use of water sources for different purposes 

with minimum cost. The objective function can be expressed 

as: 

𝑀𝑎𝑥 𝑍 =  ∑ 𝑄𝑖,𝑗

𝑖=𝑛
𝑗=𝑛

𝑖=1
𝐽=1

∗ (𝐵𝑗 −  𝐶𝑖 )           (1) 

Where; 

I:  indicates a particular source of water from n sources,  

J: denotes the sector where the water is utilized, and 

Q i,j: represent the quantity of water extracted from source (i) 

and  utilized in sector (j), which is represented by the following 

equation: 

Qi,j  = Q 11 + Q 21 + Q 31 +Q 41 + Q 51 + Q 12 + Q 52 + 
Q 62 + Q 7                         
Where;     

Q11: the quantity of water supplied from groundwater wells to 

the municipal and industrial users. 

Q21: the quantity of water supplied from brackish groundwater 

desalination to the municipal and industrial users. 

Q31: the quantity of water supplied from seawater desalination 

to the municipal and industrial users. 

Q41: the quantity of water supplied from imported water from 

Mekorot Company (Israel) to the municipal and industrial us-

ers. 

Q51: the quantity of water supplied from imported water from 

Egypt to the municipal and industrial users. 
Q12: the quantity of water supplied from groundwater wells to 

the agriculture sector. 

 

 

 

 

Q52: the quantity of water supplied from imported water from 

Egypt to the agriculture sector. 

Q62: the quantity of water supplied from reclaimed water to  

the agriculture sector, and 

Q7: the quantity of harvested water from storm water and used 

to replenish the groundwater aquifer. 

Bj: the estimated benefits resulting from utilizing one cubic 

meter of water for municipal and industrial sector and/or 
for agricultural sector (BM&I or BAg). 

Ci: the estimated cost to supply a unit volume of water from 
different sources including physical losses. 

 

B Estimation of Benefits from Municipal and 
Industrial Sectors (BM&I) 

The benefit from utilizing water for municipal and industrial 

purposes (BM&I) is based on the marginal value of water (op-

timal value of water) which is based on the economic equilib-

rium point between supply and demand curves This point cor-

responds to the marginal value of unit volume of good quality 

water which is 1.03 $/m3. The lower value water corresponds 

to the present quality situation [7]. Based on that, the benefit 

of water supply is given by the relation: 

𝐵𝑀&𝐼 = 1.51 − 0.48 𝐸𝐶𝑀&𝐼             (2) 
Where; 

BM&I: economic value of unit water for municipal and indus-

trial purposes, and  

ECM&I: Electrical Conductivity of blended water (mS/cm) 

from different sources which given by the following mass bal-

ance relation: 

𝐸𝐶𝑀&𝐼  =  
∑ 𝑄𝑖 ∗ 𝐸𝐶𝑖𝑀&𝐼

∑ 𝑄𝑖𝑀&𝐼
                (3) 

Where; 

ECi = electrical conductivity of each source used for munici-

pal and industrial purposes. 

∑ QiM&I  = sum of the quantities of water from different 
sources used for municipal and industrial purposes. 

 

C  Estimation of Benefits from Agricultural  

Sector (BAg) 
The benefits from irrigation water (BAg) are affected by the 

relationship between crop yield and salinity. A series of this 

type of relationships were developed for different categories 

of crops as shown in Figure 1. The different categories are 

also illustrated in Table 1[8]. 

For Gaza Strip case and for the purpose of simplification of 

the optimization model, medium curve (average crop) ,be-

tween the four curves shown in Figure 1, is selected to repre-

sent Gaza Strip agricultural sector. The reason for this is that 

no accurate data is available about the crop distribution par-

ticularly for future prediction. In addition to that, we are con-

cerned about the macro-scale picture of problem. 

 



 Genetic Algorithm Model to Optimize Water Resources Allocation in Gaza Strip Said M Ghabayen, Ibrahim M. Madi, Khalid A. 

Qahaman, and Basim I. Sirdah (2014) 

 

  

26  

 
Figure 1: Relative CropYyield vs. Salinity [8] 

 

TABLE 1 

Crops Categories and their Tolerance to Salinity [8] 

Tolerance to Salinity Crop 

Tolerant Barley, Sugar Beet 

Moderately Tolerant Wheat, Wheat Grass, Zucchini, Beet (red), Orange 

Moderately Sensitive Tomato, Cucumber, Alfalfa, Clover, Corn, Potato 

Sensitive Onion, Carrot, Bean, Apple, Cherry,  Strawberry, 

flowers 
 

 

D  Limits and Constraints 
Most of the constraints in GA model are allowed to iterate 

between two bounds (upper bound and lower bound) until the 

optimal value for each variable is found. Quality variables and 

resource variables constraints can be expressed by the follow-

ing inequalities: 

 

𝐸𝐶𝑖,𝑚𝑖𝑛 ≤  𝐸𝐶𝑖 ≤ 𝐸𝐶𝑖,𝑚𝑎𝑥  

𝑄𝑖𝑗,𝑚𝑖𝑛 ≤  𝑄𝑖𝑗 ≤ 𝑄𝑖𝑗,𝑚𝑎𝑥  

These values are either based on the nature or environment 

carrying capacity such as sustainable abstraction quantities 

from the groundwater aquifer or they are based on local po-

lices for allocating the resources for different sectors. Table 2, 

below shows the upper and lower bounds of the different var-

iables used in the GA model. 
 

TABLE 2 

GA Model input variables and limits 

Varia-

ble 

(GA 

Model

) 

Vari-

able 

(text) 

Unit 

Year 2015 Year 2025 Year 2035 

Min Max Min Max Min Max 

X1 Q11 m3 60*1

06 

67*1

06 

40*1

06 

48*1

06 

40*1

06 

48*10

6 X2 Q21 m
3 0 5*10

6 

0 5*10

6 

0 5*106 

X3 Q31 m3 0 13*1

06 

0 72*1

06 

0 130*1

06 

X4 Q41 m3 5*10

6 

15*1

06 

5*10

6 

21*1

06 

5*10

6 

21*10

6 X5 Q51 m3 0 5*10

6 

0 10*1

06 

0 10*10

6 X6 Q12 m
3 60*1

06 

70*1

06 

40*1

06 

50*1

06 

40*1

06 

50*10

6 X7 Q52 m
3 0 5*10

6 

0 10*1

06 

0 10*10

6 X8 Q62 m
3 0 10*1

06 

0 20*1

06 

0 40*10

6 X9 EC1 µS/c

m 

1000 1670 1000 1040 1000 1040 

X10 EC2 µS/c

m 

500 1000 500 1000 500 1000 

X11 EC3 µS/c

m 

500 700 500 700 500 700 

X12 EC4 µS/c

m 

700 1040 700 1040 700 1040 

X13 EC5 µS/c

m 

500 700 500 700 500 700 

X14 Q7 m3 0 20*1

06 

0 30*1

06 

0 40*10

6 Calcu-

lated 

ECT1 µS/c

m 

N/A 1500 N/A 1500 N/A 1500 

Calcu-

lated 

ECT2 µS/c

m 

N/A 1650 N/A 1650 N/A 1650 

 

E Other Constraints 
The sustainable abstraction from the groundwater aquifer is 

estimated at 110 x 106 cubic meter per year [10]. This value 

changes based on the applied strategies for inflows and out-

flows to the aquifer and the availability of other resources and 

are modeled by the following equation: 

𝑄11 + 𝑄12 + 1.5𝑄21 ≤ 𝑄7 + 110 ∗ 10
6         (4) 

Q7 is the quantity of water added from harvesting and infil-

tration so it will increase the upper bound of the aquifer ca-

pacity. 

Based on Metcalf and Eddy (2003) [10] the integrated aquifer 

management plan assumed that at least 25% of irrigation de-

mand should come from the aquifer and the rest can be sup-

plied from treated effluent. This argument can be modeled in 

the following constraint:- 

𝑄62 ≤ 0.75 (𝑄12 + 𝑄52 + 𝑄62)                      (5) 
 

F  Total Water Demand Constraints  
Table 3 bellow summarizes the total water demand of water 

for both domestic & industrial and agriculture. These con-

straints are for the years 2015, 2025 and 2035 [6, 9]. 

 

 

TABLE 3 

GA Model Input Variables and Limits 

Constrains 
Total Water Demand 

Year 2015 Year 2025 Year 2035 

Q11 +  Q21 +  Q31 +  Q41 +  Q51

≥ 

94 MCM 140 MCM 198 MCM 

Q12 +  Q52 +  Q62 ≥ 

 

77 MCM 69   MCM 61   MCM 

 

G  Cost Variables  
The under mentioned variables are the cost variables for the 

model [6]: 

C11: the unit cost for quantity of water supplied from ground-

water wells to the municipal and industrial users. ($ 0.30 /M3), 



Genetic Algorithm Model to Optimize Water Resources Allocation in Gaza Strip Said M. Ghabayen, Basim I. Sirdah, and Ibrahim Madi/ 

Research Name (2014) 

 

  

27  

C21: the unit cost for the quantity of water supplied from 

brackish groundwater desalination to the municipal and in-

dustrial users. ($ 0.75/M3), 

C31: the unit cost for the quantity of water supplied from sea-

water desalination to the municipal and industrial users. ($ 

0.90/M3), 

C41: the unit cost for the quantity of water supplied from im-

ported water from  Mekorot company (Israel) to the municipal 

and industrial users. ($ 0.85/M3), 

C51: the unit cost for the quantity of water supplied from im-

ported water from Egypt to the municipal and industrial users. 

($ 0.80/M3), 

C12: the unit cost for the quantity of water supplied from 

groundwater wells to the agriculture sector. ($ 0.20/M3), 

C52: the unit cost for the quantity of water supplied from im-

ported water from Egypt to the agriculture sector. ($ 0.80/M3), 
C62:  the unit cost for additional treatment for the quantity of 

water supplied from reclaimed water to the agriculture sec-
tor. ($ 0.35/M3), and  

C7: the unit cost for additional treatment for the quantity of 
water harvested and infiltrated into the aquifer groundwater 

($ 0.35/M3). 

III RESULTS AND DISCUSSION  

A  Genetic Algorithm Model Results for Short 

Term Management for Year 2015 
According to the Palestinian Central Bureau of Statistics 

(PCBS) [10], the growth population rate of 3.5% was as-

sumed to estimate the future municipal well abstractions. 

Based on that the projected population of Gaza strip by year 

of 2015 will stand at 1.8 million inhabitants distributed over 

the five governorates. The estimated quantities of water for 

domestic demand  were calculated considering the recom-

mendations of GETAP 2011[6] by considering a benchmark 

water consumption of 135 liters per capita/day for the whole 

of Gaza Strip towards the end of the short-term intervention 

period. 

Table 4 summarizes the distribution of projected population 

of Gaza Strip as well as the demanded quantities of water for 

domestic and agriculture use. As the result of the instability 

of the political situation and the absence of the industrial in-

frastructure, the consumption for industrial demand will con-

sider being 2MCM/year [11]. Considering the required quan-

tities for both domestic and agriculture use with all constrains 

and limits the GA model solved the case for optimal quantities 

by maximizing the total benefits and minimizing the cost. The 

optimal quantities after comparing 100 generations are sum-

marized in Table 5.  
 

 

 

 

 

 

 

 

TABLE 4 

Projected Population and Domestic & Agriculture Water 

Demand in year 2015 

G
o

v
e
rn

o
ra

te
 

Y
e
a
r 

P
o

p
u

la
ti

o
n

 

C
o

n
s
u

m
p

ti
o

n
 

(L
/C

a
p

it
a

/ 
D

a
y

) 

D
o

m
e
s
ti

c
 W

a
te

r 

D
e
m

a
n

d
 p

e
r 

G
o

v
-

e
rn

o
ra

te
  

(m
3
/y

e
a
r)

 

A
g

ri
c
u

lt
u

re
 W

a
te

r 

D
e
m

a
n

d
 F

o
r 

A
ll

 

G
o

v
e
rn

o
ra

te
s
 

(m
3
/y

e
a
r)

 

North 

2015 

501,979 135 17,535,100 

77,000,000 

Gaza 922,078 135 32,209,983 

Middle 381,779 135 13,336,278 

Khan 

Younis 

503,341 135 17,582,699 

Rafah 322,037 135 11,249,383 

Total 2,631,214 135 91,913,443 77,000,000 

 

TABLE 5 

Optimal Water Quantities for Short Term Management in 

Year 2015 

Description Source Variable  Unit Quantity 

Domestic and 

Industrial De-

mand 

Groundwater wells Q11 M3 65*106 

Desalinated water from 

brackish wells 
Q21 M3 1.08*106 

Desalinated Sea Water. Q31 M3 13*106 

imported water from Me-

korot  Company  
Q41 M3 9.90*106 

Imported water from 

Egypt. 
Q51 M3 4.90*106 

Total M3 93.88*106 

Agriculture 

Demand 

Groundwater wells Q12 M3 63*106 

Imported water from 

Egypt 

Q52 M3 4.96*106 

Reclaimed water Q62 M3 9.28*106 

Total M3 77.24*106 

Harvested 

Water 

Harvested water from 

storm water 
Q7 M3 19.87*106 

Electrical 

Conductivi-

ties of water 

from different 

sources 

Water from  aquifer Ec1 µS/cm 1000 

Desalinated  water from  

brackish wells 
Ec2 µS/cm 500 

Desalinated seawater. Ec3 µS/cm 500 

Imported water from Me-

korot Company. 
Ec4 µS/cm 700 

electrical conductivity of 

water imported from Egypt 
Ec5 µS/cm 500 

Final Quality 

of water In 

terms of Elec-

trical Con-

ductivity 

Calculated average electri-

cal conductivity for do-

mestic water 

ECT1 µS/cm 867 

Average electrical conduc-

tivity for irrigation  water 
ECT2 µS/cm 962 



 Genetic Algorithm Model to Optimize Water Resources Allocation in Gaza Strip Said M Ghabayen, Ibrahim M. Madi, Khalid A. 

Qahaman, and Basim I. Sirdah (2014) 

 

  

28  

Best Function Value $ 77 *106 

 

 

The results show that there is a significant improvement in aq-

uifer’s water levels in the majority area of the Gaza Strip espe-

cially in the middle area. The levels will gradually to increase 

to reach 4 meters below MSL in north area and 11 meters below 

MSL in south area.  As for quality, the results show that the total 

average of electrical conductivity for the domestic and agricul-

ture uses for water are 867 µS/cm and 962 µS/cm respectively. 

This means that the total dissolved solids (TDS) for domestic 

use is around 500 mg/liter and 580 mg/liter for agriculture use. 

Figure 2 shows the the results of the simulations for the water 

levels in the aquifer using SEAWAT model when adopting the 

optimized scenario for short term planning in year 2015. 

 

 
Figure 2: Predicted Water Level for Optimized Scenario (year 2015) 

 

B  Genetic Algorithm Model results for Medium Term 

Management for Year 2025 

The estimated quantities of water for domestic demand  were 

calculated considering the recommendations of GETAP 

2011[6]  by considering a  benchmark water consumption  of 

150 liters per capita/day for the whole of the Gaza Strip towards 

the end of the medium-term intervention period. Table 6 sum-

marizes the distribution of projected population of the Gaza 

Strip as well as the demanded quantities of water for domestic 

and agriculture use. As the result of the instability of the politi-

cal situation and the absence of the industrial infrastructure, the 

consumption for industrial demand will consider to be 

2MCM/year [11]. Considering the required quantities for both 

domestic and agriculture use with all constrains and limits, the 

GA model solved the case for optimal quantities by maximiz-

ing the total benefits and minimizing the cost. The optimal 

quantities after comparing 100 generations are summarized in 

Table 7 of Genetic Algorithm model interface for year 2025.  

TABLE 6 

Projected Population and Domestic & Agriculture Water 

Demand in year 2025 

G
o

v
e
rn

o
ra

te
 

Y
e
a
r 

P
o

p
u

la
ti

o
n

 

C
o

n
s
u

m
p

ti
o

n
 

(L
/C

a
p

it
a

/ 
D

a
y

) 

D
o

m
e
s
ti

c
 W

a
te

r 

D
e
m

a
n

d
 p

e
r 

G
o

v
-

e
rn

o
ra

te
  

(m
3
/y

e
a
r)

 

A
g

ri
c
u

lt
u

re
 W

a
te

r 

D
e
m

a
n

d
 F

o
r 

A
ll

 

G
o

v
e
rn

o
ra

te
s
 

(m
3
/y

e
a
r)

 

North 

2015 

501,979 150 27,483,350 

69,000,000 
Gaza 916,655 150 50,186,861 

Middle 373,197 150 20,432,535 

Khan 

Younis 

456,331 150 24,984,122 

Rafah 322,035 150 17,631,416 

Total 2,570,197 150 140,718,284 69,000,000 

 

TABLE 7 

Optimal Water Quantities for Medium Term Management in 

Year 2025 

Description Source Variable  Unit Quantity 

Domestic and 

Industrial De-

mand 

Groundwater wells Q11 M3 48*10
6 

Desalinated water from 

brackish wells 
Q21 M3 5*10

6 

Desalinated Sea Water. Q31 M3 67*106 

imported water from Me-

korot  Company  
Q41 M3 10*10

6 

Imported water from 

Egypt. 
Q51 M3 10*10

6 

Total M3 140*106 

Agriculture 

Demand 

Groundwater wells Q12 M3 49*10
6 

Imported water from 

Egypt 

Q52 M3 9.7*106 

Reclaimed water Q62 M3 20*106 

Total M3 78.7*106 

Harvested 

Water 

Harvested water from 

storm water 
Q7 M3 18*10

6 

Electrical 

Conductivities 

of water from 

different 

sources 

Water from  aquifer Ec1 µS/cm 1000 

Desalinated  water from  

brackish wells 
Ec2 µS/cm 500 

Desalinated seawater. Ec3 µS/cm 500 

Imported water from Me-

korot Company. 
Ec4 µS/cm 700 

electrical conductivity of 

water imported from 

Egypt 

Ec5 µS/cm 500 

Final Quality 

of water In 

Calculated average electri-

cal conductivity for do-

mestic water 

ECT1 µS/cm 685 



Genetic Algorithm Model to Optimize Water Resources Allocation in Gaza Strip Said M. Ghabayen, Basim I. Sirdah, and Ibrahim Madi/ 

Research Name (2014) 

 

  

29  

terms of Elec-

trical Con-

ductivity 

Average electrical conduc-

tivity for irrigation  water 
ECT2 µS/cm 876 

Best Function Value $ 79 *106 

 

 

 

The results show that there is a significant improvement also in 

aquifer’s water levels in the majority area of the Gaza Strip es-

pecially in the middle area. The levels will gradually to increase 

to reach 4 meters below MSL in north area and 8 meters below 

MSL in south area. As for quality, the results show that the total 

average of electrical conductivity for the domestic and agricul-

ture uses are water is 685µS/cm and 876 µS/cm respectively. 

This means that the TDS for domestic use is around 400 mg/li-

ter and 530 mg/liter for agriculture use. Figure 3 shows the wa-

ter levels of aquifer throughout the Gaza Strip by adopting the 

optimized scenario for medium term planning in year 2025. 

Figure 3 shows the the results of the simulations for the water 

levels in the aquifer using SEAWAT model when adopting the 

optimized scenario for short term planning in year 2025. Figure 

3 clearly dominstrate the imporovement in the water level in 

the aquifer compared to the year 2015. 

 

C  Genetic Algorithm Model results for Short Term 

Management for year 2035 

The estimated quantities of water for domestic demand  were 

calculated considering the recommendations of GETAP 

2011[6]  by considering a  benchmark water consumption  of 

150 liters per capita/day for the whole of the Gaza Strip towards 

the end of the long-term intervention period. Table 8 summa-

rizes the distribution of projected population of the Gaza Strip 

as well as the demanded quantities of water for domestic and 

agriculture use. As the result of the instability of the political 

situation and the absence of the industrial infrastructure, the 

consumption for industrial demand will consider to be 

2MCM/year [11]. 

 

 
Figure 3: Predicted Water Level for Optimized Scenario (year 2025) 

 

Considering the required quantities for both domestic and agri-

culture use with all constrains and limits, the GA model solved 

the case for optimal quantities by maximizing the total benefits 

and minimizing the cost. The optimal quantities after compar-

ing 100 generations are summarized in Table 9 of Genetic Al-

gorithm model interface for year 2035.  

 

TABLE 8 

Projected Population and Domestic & Agriculture Water 

Demand in year 2035 

G
o

v
e
rn

o
ra

te
 

Y
e
a
r 

P
o

p
u

la
ti

o
n

 

C
o

n
s
u

m
p

ti
o

n
 

(L
/C

a
p

it
a

/ 
D

a
y

) 

D
o

m
e
s
ti

c
 W

a
te

r 

D
e
m

a
n

d
 p

e
r 

G
o

v
-

e
rn

o
ra

te
  

(m
3
/y

e
a

r)
 

A
g

ri
c
u

lt
u

re
 W

a
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 F

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(m
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North 

2015 

708,091 150 38,767,982 

61,000,000 
Gaza 1,293,033 150 70,793,556 

Middle 526,431 150 28,822,097 

Khan 

Younis 

643,701 150 35,242,630 

Rafah 454,262 150 24,870,844 

Total 3,625,518 150 198,497,110 61,000,000 

 

TABLE 9 

Optimal Water Quantities for Short Term Management in 

Year 2035 

Description Source Variable  Unit Quantity 

Domestic and 
Groundwater wells Q11 M3 48*10

6 



 Genetic Algorithm Model to Optimize Water Resources Allocation in Gaza Strip Said M Ghabayen, Ibrahim M. Madi, Khalid A. 

Qahaman, and Basim I. Sirdah (2014) 

 

  

30  

Industrial De-

mand 

Desalinated water from 

brackish wells 
Q21 M3 5*10

6 

Desalinated Sea Water. Q31 M3 125*106 

imported water from Me-

korot  Company  
Q41 M3 10*10

6 

Imported water from 

Egypt. 
Q51 M3 10*10

6 

Total M3 198*106 

Agriculture 

Demand 

Groundwater wells Q12 M3 50*10
6 

Imported water from 

Egypt 

Q52 M3 10*106 

Reclaimed water Q62 M3 30*106 

Total M3 80*106 

Harvested 

Water 

Harvested water from 

storm water 
Q7 M3 18*10

6 

Electrical 

Conductivities 

of water from 

different 

sources 

Water from  aquifer Ec1 µS/cm 1000 

Desalinated  water from  

brackish wells 
Ec2 µS/cm 500 

Desalinated seawater. Ec3 µS/cm 500 

Imported water from Me-

korot Company. 
Ec4 µS/cm 700 

electrical conductivity of 

water imported from 

Egypt 

Ec5 µS/cm 500 

Final Quality 

of water In 

terms of Elec-

trical Con-

ductivity 

Calculated average electri-

cal conductivity for do-

mestic water 

ECT1 µS/cm 631 

Average electrical conduc-

tivity for irrigation  water 
ECT2 µS/cm 842 

Best Function Value $ 86 *106 

 

The results show that there is a significant improvement in wa-

ter levels in aquifer in the majority area especially in the middle 

area. The levels reach to a significant of 13 meters above MSL 

in the eastern area  and show a significant increase also in both 

north with 3 meters below MSL and in south area with 4 meters 

below MSL. As for quality, the results show that the total aver-

age of electrical conductivity for the domestic and agriculture 

uses are  water is 631 µS/cm and 842 µS/cm  respectively . This 

means that the TDS for domestic use is around 380 mg/liter and 

500 mg/liter for agriculture use. Figure 4 shows the water levels 

of aquifer throughout Gaza Strip by adopting the optimized 

scenario  for long term planning in year 2035. 

Figure 4 shows the the results of the simulations for the water 

levels in the aquifer using SEAWAT model when adopting the 

optimized scenario for short term planning in year 2025. Figure 

4 show more imporovement in the water level in the aquifer 

compared to the year 2015, and year 2025. 

 

 
Figure 4: Predicted Water Level for Optimized Scenario (year 2035) 

 

IV CONCLUSIONS 

A Optimal solution for short term planning in Year 
2015   

The optimal quantities for domestic and industrial demands 

are 65*106 M3 from Groundwater, 1.08*106 M3 from desali-

nated brackish wells, 13*106 M3 from  desalinated from sea 

water, 9.90*106 M3 imported water from Mekorot company 

and 4.90*106 M3 from imported water from Egypt. 

The optimal quantities for agriculture demand are 63*106 M3 

from Groundwater, 4.96 *106 M3 of imported water from 

Egypt and  9.28  *106 M3 from reclaimed water  . 

The results of the model show that a quantity  of 19.87 *106 

M3 of harvested water should have injected to aquifer to add  

additional  quantities and to improve the ground water quality 

. 

The results show that the final quality of available water for 

agriculture use  in term of total weighted  average of electrical 

conductivity is 962 µS/cm which is equal to 577 mg/l and 230 

mg/l for TDS and CL- respectively.  

 

B Optimal solution for medium Short term planning in 
Year 2025   

The optimal quantities for domestic and industrial demands are 

48*106 M3 from Groundwater, 5*106 M3 from desalinated 



Genetic Algorithm Model to Optimize Water Resources Allocation in Gaza Strip Said M. Ghabayen, Basim I. Sirdah, and Ibrahim Madi/ 

Research Name (2014) 

 

  

31  

brackish wells,67*106 M3 from  desalinated from sea water, 

10*106 M3 imported water from Mekorot company and 10*106 

M3 from imported water from Egypt. 

The optimal quantities for agriculture demand are 49*106 M3 

from Groundwater, 9.7  *106 M3 from imported water from 

Egypt and   20 *106 M3 from reclaimed water. 

The results of the model show that a quantity  of 18 *106 M3 of 

harvested water should have injected to aquifer to add  addi-

tional  quantities and to improve the ground water quality . 

The results show that the final quality of available water for 

agriculture use  in term of total weighted  average of electrical 

conductivity  is 876 µS/cm which is equal to  526 mg/l and 210 

mg/l for TDS and CL respectively.  

C Optimal solution for long  term planning in Year 
2035 

The optimal quantities for domestic and industrial demands are 

48*106 M3 from Groundwater, 5*106 M3 from desalinated 

brackish wells, 125*106 M3 from  desalinated from sea water, 

10*106 M3 imported water from Mekorot company and 10*106 

M3 from imported water from Egypt. 

The optimal quantities for agriculture demand are 50*106 M3 

from Groundwater, 10*106 M3 imported water from Egypt and  

30 *106 M3 from reclaimed water  . 

The results of the model show that a quantity  of 18 *106 M3 of 

harvested water should have injected to aquifer to add  addi-
tional  quantities and to improve the ground water quality. 

The results show that the final quality of available water for 

agriculture use  in term of total weighted  average of electrical 

conductivity is 842 µS/cm which is equal to 505 mg/l and 202 

mg/l for TDS and CL- respectively. 

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