Seminar Presentation FB 14 Kassel, 9 November, 2012 MSc. Hasan Sirhan Geohydraulic and Engineering...

Seminar Presentation FB 14Kassel, 9 November, 2012

MSc. Hasan SirhanGeohydraulic and Engineering Hydrology 1

Title

Numerical Feasibility Study for Treated Wastewater Recharge as a Tool to Impede Saltwater Intrusion in the Coastal Aquifer of the

Gaza – Palestine

Supervisor

Prof. Dr. rer. nat Manfred Koch

By

MSc. Hasan Sirhan



Saltwater intrusion can be defined as the invaded of seawater inland into fresh

groundwater aquifers as a results of:

• Steeply overexploitation of the aquifer to meet the municipal water

demand as well as extended agricultural activities.

• Destruction of natural barriers had led to reduction or reversal of a

groundwater gradient under unsteady-state conditions, where denser

saline water displace fresh water.



• High chloride concentration is used as an indicator that seawater intrusion is occurring.

Terms describing degree of salinity as used by USGS

Description TDS (mg/l)Fresh < 1000Slightly saline 1000 – 3000Moderately saline 3000 – 10000Very saline 10000 – 35000Brine > 35000

The increase of salinity in water causes:

An increase in blood pressure for people,

Extreme damage to the soil and reduced crops yield,

Corrosion of water metal pipes.



Saline water in aquifers may be derived from any of the following sources

• Upconing of ancient saline water that entered aquifers during past geologic

time into fresh water aquifer.

• Intrusion of seawater into a coastal aquifer.

• Return flows from irrigated lands and human saline waste.

This study deal with the seawater intrusion as a source of salinity in the Gaza

aquifer.



The simplest analyses of seawater intrusion adopt the Ghyben-Herzberg relationship,

which is based on the sharp interface method, assumes that:

The saltwater and freshwater are immiscible and no mixing between the two fluids.

Attributed to a hydrostatic equilibrium existing between the two fluids.

hs = 40 hf

hs = hf ቀ𝜌𝑓 𝜌𝑠−𝜌𝑓 ቁ

Ghyben-Herzberg theory, Hydrostatic equilibrium between freshwater/seawater interface

(Ghyben, 1989; Herzberg, 1901)Salt water occurred

underground at a depth

‘‘hs’’ below sea level

about 40 times the height

of the fresh water above

sea level ‘‘hf ’’.



Presence of salinity in coastal aquifers can be detected by:

Geophysical Techniques: by using the profiling technique of frequency domain

electromagnetics (FDEM).

Geochemical Analysis (Isotops)

Numerical Models

Most popular models for seawater intrusion

Visual MODFLOW Pro 4.2 integrates SEAWAT

SUTRA

FEFLOW



• Not PREVENTING seawater intrusion

• But CONTROLING seawater intrusion

Once the groundwater is contaminated by saline water, it is very difficult to

bring it back to its original quality, thus the clean-up of salinity-polluted aquifers

will be a major challenge for the future.

Does proper management prevent salinization of aquifers?



Research Objectives:

The overall objective of this research is to develop a numerical model to study the problem of saltwater intrusion, using the artificial recharge option as an integrated approach and optimum scenario to impede the seawater intrusion in the Gaza coastal aquifer.

Specific Objectives such as:

• Setting up a conceptual numerical model using a finite difference model of Visual

MODFLOW for the Gaza aquifer.

• Applying the MODFLOW-2000 incorporating with MT3DMS in Visual MODFLOW for

the contaminant solute-transport simulation in the aquifer system.

• Simulate the future migration of the contaminant saline plumes under several

management scenarios and strategies.

• Applying a statistical model to predict groundwater levels using an Artificial Neural

Network (ANN) approach as an alternative tool for traditional physical-based

numerical models.



The Study Area



The Study Area

Gaza Strip



The Study AreaGaza Strip

Geography

Palestine is composed of two-separated

areas, the Gaza strip and the West Bank.

The Gaza Strip is a very small area

located at the eastern coast of the

Mediterranean sea in the southwest of

Palestine.

Its length 40 km while its width varies

between 6 km in the north to 12 km in

the south, with an avg. area of 365Km2.



Population change in the Gaza Strip within the period 1947-2035

1,865,317

2,215,411

2,631,213

3,125,056

3,711,586

1,570,547

1,517,436

1,466,122

1,416,543

1,023,000

963,000

747,200

449,600

454,900

280,0000

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

Years

Popu

latio

n

•The population density in the Gaza

Strip is the highest in the world of

almost 2,802 persons/Km2.

•The average annual growth rate is

3.2%.

• More than 1.5 Million inhabitants

are living now within the area of

365 km2,. By year 2020 the

population will be around 2.3

Million

Demography



• The Coastal Aquifer extends from Gaza in the south to Mount Carmel in the north along some 120 km of Mediterranean coastline, and it is the only source of water supply.

• The Gaza coastal aquifer represents part of the whole coastal aquifer.

• The width of the aquifer varies from 3-10 km in the north to about 20 km in the south.

• Under natural conditions, the groundwater flow in the Gaza Strip is towards the Mediterranean Sea.

Hydrogeology



Geology

•The Upper Sub-Aquifer

The uppermost aquifer

(classified as unconfined A-

aquifer).

•The Middle Sub-Aquifer

This aquifer classified as

confined/unconfined B1/B2-

aquifer.

The Lower Sub-Aquifer

The lower aquifer (classified

as confined/unconfined

C-aquifer).



Under steady state condition the overall aquifer balance of the Gaza Strip can

be represented as:

Balance = Sum (Inflows) – Sum (Outflows).

Inflows

• Effective recharge (rainfall)

• Lateral inflow

• Total return flow and

• seawater intrusion

Outflows

• Domestic abstraction

• Agricultural abstraction

• Groundwater discharge



Lateral InflowLateral Inflow Seawater Intrusion Seawater Intrusion

Recharge (Rain)Recharge (Rain)

Municipal & AgriculturalAbstraction

Municipal & AgriculturalAbstraction

Return Flow:Agriculture,Pipe Leakage &Wastewater

Return Flow:Agriculture,Pipe Leakage &Wastewater

Groundwater Discharge



• The coastal aquifer holds approximately

5000×106 m3 of different groundwater

quality.

• Only 1400×106 m3 of this is freshwater,

with Chloride (Cl-) content of less than

250 mg/l.

• That means approximately 70% of the

aquifer are brackish or saline with a

chloride concentration exceeding 250

mg/l.Only 30% are fresh water found

mainly in the Northern area.

Groundwater quality



A/210

A/180

D/73 E/90E/15

4

E/154A

R/162H

R/270R/31

2R/30

6 J/3 T/52 G/49 H/60 S/82 L/41L/18

4

Al-Naja

r

L/159L/_8

7L/18

7P/15

P/124P/13

9P/13

8

Naser2

0

500

1000

1500

2000

2500

← Gaza →

WHO 250

← North → ← Middle → ← Kh-younis → ← Rafah →

Chloride Concentration in mg/l -Year 2007

Chloride concentration WHO

Well ID

Ch

lori

de

(mg/

l)

This figure represents the chloride concentration at some specified monitoring wells in

the Gaza Strip. It is clear that most of the wells have a chloride concentration more than

the WHO (250 mg/l), where the seawater intrusion had occurred.



The Numerical model

Visual MODFLOW model:

Visual MODFLOW package is a coupled three -

dimensional groundwater flow and

contaminant transport model based on the

finite-difference method and give the most

complete and powerful graphical interface. The

linkage used MODFLOW-2000 (Harbaugh and

McDonald, 1996) and MT3DMS (Zheng and

Wang, 1999).

SEAWAT 2000 package has now been included in Visual MODFLOW, allowing modeling of variable density flow such as seawater intrusion modeling.



Model Setup

The finite-difference grid method in

Visual MODFLOW is formulated as

such:

• The model domain grid contains of

157 rows, 50 columns, and 7 layers.

• The model of Gaza coastal aquifer

has uniform cell sizes of 300 m by

300 m in the horizontal plane.

The model domain with the grid origin and boundaries



Boundary Assigned Neumann boundary condition • A Neumann influx-boundary condition was assigned at the top of the aquifer at the land surface representing groundwater recharge (infiltration). Lateral no-flow boundariesA zero flux imposed on parts of the northern boundary with Israel border, and southern boundary with Egypt. b) Horizontal boundary conditions • A Neumann-type of no-flux boundary conditions: It represents the base of the model boundary 2) Dirichlet boundary conditionAssigned to the residual parts of the left and right boundariesConstant flux boundaryconstant flux representing the lateral inflow to the domainConstant-head boundaryh = 0 m ASL along the coastline.



Neumann influx boundaryNeumann influx boundary

Dirichlet BC.Constant head boundary

Dirichlet BC.Constant head boundary

Neumann no-flow boundaryNeumann no-flow boundary

Dirichlet BC.Constant flux boundary

Dirichlet BC.Constant flux boundary



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S35

S36S38

S39S40

S41

S42

S44

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S47S48

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18

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7

13

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A-I-1

A-I-10A-I-11

A-I-12

A-I-13

A-I-14

A-I-15A-I-16

A-I-17

A-I-19

A-I-20

A-I-21

A-I-22

A-I-23

A-I-24A-I-25

A-I-26

A-I-27A-I-28

A-I-29

A-I-3

A-I-30

A-I-31

A-I-32

A-I-33

A-I-34

A-I-35

A-I-36A-I-37

A-I-38

A-I-39

A-I-4

A-I-40A-I-41

A-I-42

A-I-43

A-I-44

A-I-45

A-I-46 A-I-47

A-I-48

A-I-49

A-I-5

A-I-50

A-I-6A-I-7A-I-8A-I-9

B-I-1

B-I-2 B-I-3

E-I-10E-I-11

E-I-13

E-I-14E-I-15E-I-16

E-I-22

E-I-23E-I-24E-I-25

E-I-6

E-I-8E-I-9

R-I-89

R-I-90

F-I-1

F-I-10

F-I-100

F-I-101

F-I-102F-I-103F-I-104F-I-105

F-I-106F-I-107

F-I-109

F-I-11

F-I-110F-I-111

F-I-112

F-I-113F-I-114

F-I-115

F-I-116

F-I-118

F-I-119

F-I-12

F-I-120F-I-121

F-I-122

F-I-123

F-I-124

F-I-125

F-I-126

F-I-127

F-I-128

F-I-129

F-I-13

F-I-130

F-I-14

F-I-15

F-I-16

F-I-17

F-I-18

F-I-19

F-I-2

F-I-20

F-I-21

F-I-22F-I-23

F-I-25

F-I-26F-I-27F-I-28

F-I-29

F-I-3

F-I-30F-I-31F-I-32

F-I-33F-I-34F-I-35

F-I-36

F-I-37

F-I-38

F-I-39

F-I-4

F-I-40

F-I-41

F-I-42

F-I-43

F-I-44

F-I-47

F-I-48F-I-49

F-I-5

F-I-50

F-I-51

F-I-52

F-I-53

F-I-54

F-I-55

F-I-56F-I-57

F-I-58F-I-59 F-I-6F-I-60F-I-61

F-I-62

F-I-63

F-I-64F-I-65

F-I-66F-I-67

F-I-68F-I-69 F-I-7

F-I-70

F-I-71

F-I-73F-I-74

F-I-75

F-I-76F-I-78 F-I-79

F-I-8F-I-80

F-I-81

F-I-82F-I-83

F-I-84

F-I-85

F-I-86F-I-87

F-I-88

F-I-89

F-I-9F-I-90

F-I-91F-I-92

F-I-93F-I-94

F-I-95

F-I-96F-I-97

F-I-98F-I-99

R-I-1

R-I-10

R-I-11R-I-12

R-I-13R-I-14

R-I-15

R-I-16R-I-17

R-I-18

R-I-19

R-I-2

R-I-20

R-I-21

R-I-22

R-I-23

R-I-24

R-I-25

R-I-26

R-I-27

R-I-28R-I-29

R-I-3

R-I-30

R-I-31

R-I-32

R-I-34R-I-35

R-I-36R-I-37

R-I-38R-I-39

R-I-4

R-I-40

R-I-41R-I-42

R-I-43

R-I-45

R-I-46

R-I-47

R-I-48

R-I-5

R-I-50

R-I-51

R-I-52

R-I-53

R-I-54

R-I-55

R-I-56

R-I-57

R-I-58R-I-59

R-I-6

R-I-60R-I-61R-I-62

R-I-63

R-I-64R-I-65

R-I-66

R-I-68

R-I-69

R-I-7

R-I-70

R-I-71R-I-72R-I-73

R-I-74

R-I-75R-I-76

R-I-77

R-I-78

R-I-79

R-I-80R-I-81R-I-82

R-I-83

R-I-84

R-I-85 R-I-86

R-I-87

R-I-9

R-I-91

R-I-92

R-I-93

E-I-18

E-I-19E-I-20E-I-21

E-I-17

E-I-5

G -I-1G -I-2

G -I-3

G -I-4

G -I-47

G -I-10G -I-11

G -I-12G-I-13G -I-14G -I-15G -I-16

G -I-17G -I-18G -I-19

G -I-20

G -I-21G -I-22

G -I-23G -I-24

G -I-25G -I-26

G -I-27

G -I-28

G -I-29G-I-30G -I-31G -I-32G -I-33G -I-34

G -I-35

G -I-36G -I-37G -I-38

G-I-39

G -I-40

G -I-41

G -I-42

G -I-44G -I-45

G -I-46

G -I-48

G -I-49

G-I-5G -I-6G-I-7

G -I-8G -I-9

H-I-1

H-I-10

H-I-11

H-I-12

H-I-13H-I-14

H-I-15

H-I-16

H-I-17

H-I-19

H-I-2H-I-20H-I-21

H-I-23H-I-24

H-I-25

H-I-26 H-I-27

H-I-28

H-I-29

H-I-3

H-I-30

H-I-31

H-I-32H-I-33

H-I-34H-I-35

H-I-36

H-I-37H-I-38

H-I-39

H-I-4H-I-40

H-I-41

H-I-42

H-I-43H-I-44H-I-45

H-I-46

H-I-47

H-I-48

H-I-49

H-I-5

H-I-50

H-I-51

H-I-52

H-I-53

H-I-54H-I-55

H-I-56

H-I-6H-I-7

H-I-8H-I-9

J-I-1

J-I-10

J-I-11J-I-12

J-I-13J-I-14

J-I-15J-I-16J-I-17J-I-18

J-I-19

J-I-2J-I-20J-I-21

J-I-22

J-I-23

J-I-24

J-I-25

J-I-26J-I-27

J-I-28

J-I-29

J-I-3

J-I-30J-I-31

J-I-34J-I-35

J-I-36 J-I-37

J-I-38

J-I-39

J-I-4

J-I-40

J-I-41J-I-42J-I-43J-I-44

J-I-46

J-I-47

J-I-48J-I-49

J-I-5

J-I-50J-I-51

J-I-52

J-I-53 J-I-54

J-I-55

J-I-56

J-I-57

J-I-58J-I-59

J-I-6

J-I-60J-I-61

J-I-62

J-I-63

J-I-64

J-I-65

J-I-66J-I-67

J-I-68

J-I-69

J-I-7J-I-70

J-I-71

J-I-72

J-I-73

J-I-74

J-I-75

J-I-76

J-I-77

J-I-78

J-I-79

J-I-8

J-I-80

J-I-81J-I-82

J-I-83

J-I-85J-I-86

J-I-87

J-I-88

J-I-9

J-I-90

S-I-1

S-I-10

S-I-11

S-I-12

S-I-13 S-I-14S-I-15S-I-17

S-I-18S-I-19

S-I-2

S-I-20

S-I-21

S-I-22

S-I-23

S-I-24

S-I-26

S-I-27

S-I-28

S-I-29

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Spatial distribution of the pumping wells across the Gaza Strip

Wells abstraction

More than 3850 active water wells

have been used in the model as

internal hydrologic stress and

distributed between agricultural,

municipal, and domestic wells in

year 2000



Model Simulation

The groundwater flow of the aquifer system was simulation in two steps.

Firstly, steady- state water levels for the year 2000 were taken for the steady-

state calibration of

• Horizontal hydraulic conductivity.

• Vertical hydraulic conductivity (10% of Kh)

In the second step transient conditions between years 2001-2007 were used

to calibrate the storage coefficients, the specific yields and Porosity .

Calibrated Parameters

The calibrated are based on trial and error approach,

• It is carried out to check that the model can reasonably well emulate the groundwater

flow system to fit the observed hydraulic heads with an acceptable error.

• The results show the calibrated parameters are well-calibrated.



Results of St-St. Calibration

The calculated versus observed

heads and the summary of steady

state calibration statistics are

graphed and presented in the

following Figures

(A)

(B)

(A) Observed initial heads for year 2000, (B) Resulting heads for steady state simulation for year 2000.



Calculated vs. observed heads and summary of steady state calibration statistics

The results indicate that the

model represent the behavior of

the aquifer quite well under the

existing conditions as such as:

R = 90.4 %

SEE = 0.084 m

RMS = 1.105 m

Normalised RMS = 6.124 %

< 10 % (preferable by many

modeler.



Water Balance

The steady state mass balance was prepared and the total aquifer system inputs and outputs were

calculated and summarized in the table below:

Net Inflows Quantity (Mm3/y)Percent of

Total (%)

Recharge 46.62 44

Lateral inflow 23.88 22.56

Sea intruded 35.39 33.43

Total 105.89 100

Net Outflows (M m3/y) Quantity (Mm3/y)

Wells 104.93 99.09

Discharge to the sea 0.96 0.9

Total 105.89 100

Net balance =In - Out %Discrepancy = 0.00

Summary of year 2000 water balance from model calibration.

Percentage volumetric water balance components



Validation also is applied between the period 2005-2007, since this step is important. The

purpose of model validation is to establish greater confidence in the model.

Observed and calculated heads versus time for well A53.



Cont.

Observed and calculated heads versus time for well E45.



Cont.

Observed and calculated heads versus time for well L47



Model sensitivity analysis

A sensitivity analysis is performed in order to;

• Establish the effect of uncertainty resulting in inaccurate estimation or definition of

boundary conditions, aquifer parameters and stresses on the calibrated model.

The main type of prediction uncertainties is Parameter uncertainties, where it

Can be quantified relatively well for both the hydraulic conductivity and recharge.



Summary

The tasks which have been completed by now:

• Data collection and literatures

• Define the hydrodynamic and the mechanisms of the seawater intrusion evolution in

the Gaza aquifer.

• Set-up of the conceptual groundwater modeling using Visual MODFLOW model

• Steady-state and transient conditions calibration of the groundwater flow model.

• Statistical models to predict groundwater level:

Artificial Neural Network (ANN) approach.

Ongoing works

• Applying the density-independent MODFLOW-2000, incorporating with MT3DMS in

Visual MODFLOW to represent the contaminant solute-transport simulation as saline

plume migration in the aquifer system of the Gaza.

• Achieve the specific objectives that aforementioned .



Published papers

First paper

Prediction of Dynamic Groundwater Levels Using an ANN Approach in the Gaza

Coastal Aquifer, South Palestine

First International Colloquium REZAS12: "Water resources in the arid and semi-arid regions-challenges and prospects. Case of the African continent"

Beni Mellal, Morocco, November 14-16, 2012, Presentation

Hasan Sirhan* and Manfred Koch*

* Department of Geohydraulics and Engineering Hydrology, Faculty of Civil and Environmental Engineering, Kassel University



Second paper

Numerical Modeling of the Effects of Artificial Recharge on Hydraulic Heads in

Constant-Density Ground Water Flow to manage the Gaza Coastal Aquifer, South

Palestine.

Geomatic Science Meeting, Rabat, Morocco, April 8-9, 2013- Presentation

Hasan Sirhan* and Manfred Koch*

* Department of Geohydraulics and Engineering Hydrology, Faculty of Civil and Environmental Engineering, Kassel University



Content:

• Natural Neural Network

• Definition of Artificial Neural Network

• Why Artificial Neural Network

• ANN Properties

• Artificial Neural Networks Learning

• Development of ANN model for prediction of groundwater levels.

What is a Neural Network?



The Neural Network of the human brain can:

• Collect more than 10 billion interconnected “neurons”.

• Transmit information and computes some function (biochemical reactions).

• Takes input as treelike network dendrites.

• Produces (output) and connected to each other by synapses (weights).

• Can learn and makes appropriate decisions.



What is an Artificial Neural Network (ANN)?

• The first studies on Artificial Neural Networks (ANNs) were prompted based on

computers mimic human learning and created in (1943).

• Artificial neural networks are a simplified mathematical model of a natural neural

network inspired by biological nervous of the brain.

• A Computing system which can be model based on the simple quantifiable and highly

interconnected input variables.



Why Artificial Neural Network?

• ANN’s are a relatively new approach for groundwater levels modeling and an

attractive tool for traditional physical-based numerical models.

• It is not necessary to characterize and quantify the physical properties in explicit way

as in the numerical models.

• The system can be model based on the simple quantifiable input variables.



An artificial neural network is a model of reasoning based on the analogy with the human brain.

Biological Neural Network Artificial Neural NetworkSoma Neuron Dendrite Input (receptive zones)

Axon Output

Synapse (mediate the interactions between neurons)

Weight

Analogy between biological and artificial neural networks



ANNs Properties

• Inputs are flexible

Any real values

Highly correlated or independent

• Fast evaluation and less time consumed compared to the traditional (numeric) models.

• In training process, it is highly important to deal with consistent data set of patterns.

• The neural network model act as a black box, therefore the function produced can be

difficult for humans to interpret.



A typical ANN model includes

• N inputs,

• One output,

• A summation block (Adder)

An adder ‘Σ’ for collection of the weight

inputs and biass weight signals, which is

numerical estimate of the connection

strength.

• An activation function.



Approach

• The activation values of the input nodes are weighted and accumulated at each node

in the first layer.

• The weighted input nodes are transformed by an activation function into the node’s

activation value.

• Take output from first layer neurons as input to the next layer, until eventually the

output activation values are found.

The neuron output O is given by the following relationship:

Where

• Wj is the input connection weight,

• Pi is the input,

• X0 is the biass (not an input) and

• W0 is the biass weight.

O = f (net) = f ൫σ 𝑾𝒋 𝑷𝒋𝒏𝒋=𝟏 + 𝑿𝟎 𝑾𝟎൯



Activation function

• The activation function determines the relationship between inputs and outputs of a node and a network.

Sigmoid (logistic) function hyperbolic tangent(tanh) function

linear function

Among them, logistic transfer function is the most popular choice. It has a

nature nonlinearity and it can be used for both hidden and output nodes.



Artificial Neural Networks Learning

The back propagation (BP) neural network

• The back propagation (BP) is considers the most common learning algorithm used for training

MLP network.

• The error back propagation algorithm can:

Computes current output through the network layer by layer (forward pass),

Works backward to correct error (backward pass).

Approach:

• Compute actual output target: O

• Compare to desired output: d

• Determine effect of each weight (w) on error () = d-o

• Adjust weights and correct error



Application of Artificial Neural Network

• Application in hydrology

An approximation of any continuous (non-linear) relationship can be carried out.

• Application in groundwater

Ground water levels predicting can be applied under variable weather conditions

and under pumping conditions.



The Study Area



The Study Area

Gaza Strip



The Study AreaGaza Strip

Geography

Palestine is composed of two-separated

areas, the Gaza strip and the West Bank.

The Gaza Strip is a very small area

located at the eastern coast of the

Mediterranean sea in the southwest of

Palestine.

Its length 40 km while its width varies

between 6 km in the north to 12 km in

the south, with an avg. area of 365Km2.



Development of ANN model

• The objective of the ANN model is to investigate the effects of the hydrological,

meteorological and human factors on the dynamic groundwater levels in the Gaza

coastal aquifer.

The ANN model can generalize a relationship between the output and input variables

having the form of:

Y = f (Xn)

where,

Xn is an n-dimensional input independents including variables x1, x2, . . . , xn; and

Y is an output dependent variable.

• The network is implemented by statistical computational models, where STATISTICA

neural network (SNN) is applied, which was built in STATISTICA software package

version 7.



Distribution of the study wells in the Gaza Strip

Independent input variables

A 770 combination cases were extracted

from 70 study wells.

These data were created from groundwater

time series data recorded between years

2000 and 2010.



Independent input variables, Cont.

In this study ANN were developed to predict average groundwater levels with

Seven predictors as input variables, namely:

• Initial ground water level,

• Recharge from rainfall,

• Distance of the study wells from the shore line,

• Depth to well screen from surface and

• The wells density for each governorate area in the Gaza strip.

The ANN model input variables can be represented in equation as follows:

WLf = f (WLi, Q, R, K, Ds-shore, Depth to scr., Well-density)

• Ground water extraction,

• Hydraulic conductivity,



ANN Model Results

Architecture of initial ANN model

Observed water level vs. simulated water level for initial ANN model

Initial ANN Model was 3MLP

includes:

Input layer = 7 neorons

One hidden layer = 8 neorons

Output layer = 1 neoron

With a correlation coefficient (R) of 96.6 %.

The model was fits well between the predicted and observed output values.



Sensitivity Analysis

A sensitivity analysis has been demonstrated to:

• Describe how much model output values are affected by changes in model

input values.• Give a strong confirmation for the usefulness and the un-influential individual

input variables.

• The basic sensitivity figure is the error ratio, for each variable, the network is

executed as if that variable is unavailable (excluded).

Sensitivity analysis results

• Both the independent variables of depth to well screen and hydraulic

conductivity are the most un-influential variables affecting groundwater levels

due having a small error ratio.



Final ANN Model

Based on the results derived from sensitivity analysis, the final neural network models

were formatted using all the retained five input variables (neurons) namely,

• Initial water level (WLo),

• Abstraction (Q),

• Recharge rate,

• Distance from sea shore line (Ds), and

• Well density (W-density).

The attained network was (4MLP), with:

• An input layer of 5 neurons • A second hidden layer with 20 neurons • A sigmoid activation function in between the layers.

Architecture of initial ANN model

• A first hidden layer with 30 neurons• One output layer with one neuron



The attained model results indicate that the model was fits well between the

predicted and observed output values showing a correlation coefficient (R) of

96.9 %.

Observed water level vs. simulated water level for final ANN model

Simulated water level vs. the Observed water level on year 2000.



Simulated water level vs. the Observed water level on year 2005.

Simulated water level vs. the Observed water level on year 2010

The ANN model showed a particular best fit of simulated water level vs. predicted water levels,

so that the model can simulate the aquifer system relatively good.



Response Graph

Represents a relationship

between the independent

variables and the output

dependent variable

individually by a number

of plateaus.



Fig.17.a: Response surface of WLi & Q

Fig.17.b: Response surface of R & Q

Fig.17.c: Response surface of Ds & Q

Fig.17.d: Response surface of W-density & Q

Response Surface

Represents the relationship

between two independent

variables with the output

dependent variable in a

three-dimensional slice



Conclusion

The attained optimal network model was fits well between the predicted and observed

output values of water levels showing an overall correlation coefficient (R) of 96.9 %.

The attained model represented a reasonably non-linear relationship between:

The individual independent variable and dependent variable as showed in the

response graph.

A two independent variables with the output dependent variable relationship in a three-

dimension as showed in the response surface.

The results indicated that the model simulation represents the behavior of the aquifer quite

well under the existing conditions of the influencing independent variables.



End

Thank you

Seminar Presentation FB 14 Kassel, 9 November, 2012 MSc. Hasan Sirhan Geohydraulic and Engineering...

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