International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 04, April 2019, pp. 2033–2047, Article ID: IJCIET_10_04_211
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJCIET&VType=10&IType=4
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
Scopus Indexed
USE OF ARTIFICIAL NEURAL NETWORKS
FOR SIMULATING ADHAIM RIVER BASIN,
IRAQ
Laith B. Al-badranee
PhD Candidate, Cairo University, Faculty of Engineering,
Department of Irrigation and Hydraulics
Ahmed H. Solman
Assistant Professor, Cairo University, Faculty of Engineering,
Department of Irrigation and Hydraulics
Hesham M. Bekhit
Professor of Water Resources, Cairo University, Faculty of Engineering,
Department of Irrigation and Hydraulics
ABSTRACT
Adhaim River is one of Tigris river tributaries, contributing by 5555% of the Tigris
River yield, with annual flow rate of 0.832 BCM. To simulate Adhaim River flow,
Artificial Neural Networks (ANNs) was used, 21 years’ hydrological and
meteorological data were collected and analyzed from Iraqi ministry of water
resource. Catchment delineation was estimated using Geographical Information
Systems (GIS( technique, to obtain the best simulation of the runoff, five different
methods for estimated average rainfall and five formulas for scaled all data was
examined. ANNs technique parameters was obtained by the most appropriate
performance criteria of graphical and statistical approaches, such as number of
neurons, layers, and epoch values. ten types of transfer functions, different learning
rate. As a result, four models were developed with different time steps (i.e., 15, 20, 25,
and 30 days). The study shows that the most appropriate ANNs algorithm was
Levenberg-Marquradt with back propagation using one hidden layer and three
transfer functions namely 'tansig', 'logsig', and 'trainlm', The networks’ performance
varied with different time step involved in the study; however, the 30 days was almost
better than other networks.
Key words: ANNs, Levenberg-Marquradt, scaling factor, performance criteria, Adhaim, Iraq
Cite this Article: Laith B. Al-badranee, Ahmed H. Solman and Hesham M. Bekhit, Use of
Artificial Neural Networks for Simulating Adhaim River Basin, Iraq, International Journal of
Civil Engineering and Technology 10(4), 2019, pp. 2033–2047.
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1. INTRODUCTION
Iraq faces serious water shortage issue. Ambitious development plans, Rapid population
growth, and deterioration of natural resources pose the risk of hunger and poverty increasing
in the region; So, it was important to require better, more forward-looking development
planning to prevent that risk. Simulation Models can provide a powerful tool for decision
makers to test and examine different development scenarios. Yet there is a lack of such
modeling tool that examines and manages the resources of the basin in an integrated fashion.
Several techniques can be used to build such tools. Artificial Neural Networks (ANNs) which
is applied in this paper is one of these techniques that used to simulate the rainfall-runoff
relationship within Adhaim River Basin.
Most of the previous studies that covered Adhaim River Basin was focusing only on
geological or morphological aspect [1] and few of them covered the hydrological interaction
within the basin. One of the early attempts to study the hydrology of the basin was conducted
by using the Surface in Filtration Base flow (SFB) conceptual rainfall-runoff model
(Boughton, 1984) which was applied to simulate stream flow for Adhaim river basin. Three
versions of the model were tested: the original Australian three-parameters (SFB), the
modified five parameters version (SFB-5) and modified six parameters (SFB-6) model. The
five parameters version (SFB-5) provided better performance runoff simulation than others
models (Abdullah & Al-Badranih, 2000).
Dawood, 2007, used Dynamic Regression model (DR) for forecasting the discharge of
Adhaim river. In his study, the Auto Correlation Function (ACF) was used to determine the
stationary level of the time series. In addition, Partial Auto Correlation Function (PACF) was
used to identify a suitable Auto Regression Integrated Moving Average (ARIMA) model for
time series of rainfall and discharges for the river. The model successfully forecasted the
discharges [3].
A monthly modified Stanford rainfall-runoff model was used to develop Adhaim river
runoff curves through simulating runoff processes. The runoff curves are developed by
inserting various equations related to runoff calculations and coefficients, runoff curves
accordingly provide a better and more accurate estimate for runoff coefficients [4].
Other researchers studied hydro-geographical analyses, geology, topography, climate,
morphology, soil, land use, natural vegetation, sedimentation and soil erosion for Adhaim
river basin to estimated how these factors influence on spatial and temporal distributions of
Adhaim river flow [5].
The Soil and Water Assessment Tool (SWAT) model was used to evaluate the impacts of
climate change on water resources in Adhaim Basin. The study showed that climate change
could lead to increasing variability which is contributing to more severe floods and droughts.
The results showed that more sever conditions may occur into the future [6].
Most of the previous models require a lot of data, the access to such required data is often
unavailable or difficult, and requires long time for collection, In addition to the difficulty of
printing hardcopy data without errors. Therefore, there is a need for a model that could
simulate the relation between rainfall and runoff with Acceptable simulation and less demand
for the data. Artificial Neural Networks (ANNs) can play such role as it is highly
recommended in literature for similar cases. ANNs have been applied for rainfall-runoff
modeling since 1980 [7].
Most recent researches recommend the use of artificial neural networks when the
relationship between the input variable is not obvious, in addition, they can be used when data
availability is limiting factor. Accordingly, simulation and the forecast ANNs will be adapted
for this study. This paper also will examine determine parameters and variables the that can
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Use of Artificial Neural Networks for Simulating Adhaim River Basin, Iraq
be used to build successful ANNS model and will suggest a recommended framework for
building model for Adhaim river basin.
2. METHOD AND MATERIEL
2.1. Brief Description of the Study Area
Adhaim River catchment located within the Iraqi borders, with area about 9896 square
kilometers. It rises from the mountainous areas whiten Sulaymaniyah Governorate in the east
of Iraq with elevation (1400-1600 meter above sea level). Adhaim River Basin have main
five tributaries namely (Tuz kharmato chi, Kuri chi, Taweek chi, Khassah chi, and Zygetoon
chi), where all these tributaries converge with each other’s near the Hemrin mountain
forming one main stream namely Adhaim river, Adhaim River flow toward southern west till
its conflict with Tigris river at point Located 15 kilometers south of Balad city at elevation
150 meters above mean sea level, the length of Adhaim river with the longest tributary
Taweek chi is 232 km, its contributes 1.55% (0.823 BCM /Year) of the Tigris River annual
flow [8,9].
Geographical Information Systems (GIS) was used for watershed delineation that derived
from the digital elevation data (SRTM DEM of 90 m), these analyses explain the role of main
valley’s contribution to the hydrology of the basin, as seen in Figs. 1 and 2. [10]
Figure 1 Adhaim basins with its tributaries sub-catchment delineation upstream of Naros flow
gauging station, with rainfall station using ArcGIS
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Figure 2 Adhaim basins with its tributaries sub-catchment delineation
2.2. Data Description
The collected data for all stations within the study area were spread at a different time horizon
and covered different periods of time. Only overlapped periods, which covered a period of 21
years, were considered in this study. Adhaim river has five tributaries, but unfortunately, none
of these tributaries has streamflow records except one station which is Naros station.
Therefore, the available streamflow data was limited to Naros station which has daily stream
flow gauging and located at latitude 34°28`N, longitudinal 44°31`E with elevation 163 meter
above mean sea level. Daily gage height readings have been taken twice a day and average of
the two values adopted for computing the daily flow rate, stream flow data were obtained
from the Ministry of water resource in Iraq [11-13], the second type is rainfall data, four
rainfall stations were selected within Adhaim basin, namely "hawejah, Kirkuk, jimjamal, and
tuz kharmato”, most of the precipitation in the Adhaim basin ranging from (187-360) mm
(Ministry of Communication). All data were entered manually; the data was divided into two
main sets, one is used for the training phase, while the second data set is used for the
validation and test phases of the network results. An ideal set of data to be used as
a training data set should include higher and lower extremes that help the ANN network to int
erpolate and predict within these values, it was decided to use time step equal five
days, starting from fifteen days’ data up to thirty days as a proposed division, all data
set with 70% of the available data for training data set, 15 % for validation data set,
and 15 % for test data set. Since the amount of data cannot be listed here, a brief
statistical analysis found to be more appropriate, as seen in Table 1 and 2.
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Use of Artificial Neural Networks for Simulating Adhaim River Basin, Iraq
Table 1 Flowrate statistical analysis for Adhaim river at Naros station
Maximum
Water
Minimum
Water
Standard Average
annual
Discharge
year of
Discharge
year of
deviation discharge discharge
(m3/s)
occurrence
(m3/s)
occurrence
(m3/s)
(m3/s)
Percentage
October
169
1966
0
1955
2.7
4
0.92
November
3520
1960
0
1955
49
21
6.93
December
969
1961
1.48
1966
39.6
37
8.95
January
1040
1959
2.1
1972
57.4
51
14.27
February
787
1971
4.1
1975
48.5
47
13.48
March
2850
1973
1.9
1955
78.5
68
21.57
April
1010
1973
2.3
1958
74.4
47
20.87
May
606
1962
0.84
1958
23.7
20
9
June
62
1971
0
1955
2.2
5.431
1.71
July
11
1956
0
1955
0.81
3
0.87
August
5
1956
0
1955
0.6
2.7
0.65
September
4
1955
0
1955
0.57
2.8
0.65
Month
Table 2 Main monthly statistical parameter of Adhaim basin on for 21 years.
Rainfall station
Main statistical parameters
Kirkok
Hawejah
36.32
49.0
1.8
4.2
336
32.64
44.7
1.8
3.8
286
Average (mm)
Standard deviation (mm)
Skewness coefficient (cs)
Kurtosis coefficient (ck)
Maximum reading
Tuz kharmato Jimjamal
25.23
38.2
2.3
8.3
308
42.0
59.5
1.7
3.5
393
The standard deviation is a measure for data scattering around its mean, based on
statistical analysis Jimjamal rainfall station has more scatter than other, skewness coefficient
is second test which measure the symmetry of the data about central axis. For perfect
symmetry, this coefficient must be zero. Since all their values are positive, stations data are
skewed to the right, kurtosis coefficient is third test which measure the peak ness of the data.
Tuz kharmato rainfall station have the maximum skew to the right, with max peak [15, 16].
2.3. Model Formulation.
To build a simulation hydrological model for Adhaim river basin, many variables and method
was tested to select the best methods and optimum values for model at training phase,
including.
2.3.1. Average rainfall method
Five methods were used to estimate the average rainfall data on the Adhaim basin, which are
used as data input for the model in the training phase separately, as follows;

Arithmetic mean method(AM), its summation of rainfall station records divided by its number

Thiessen polygon method (T5P5), were created using ArcGIS after delineating the watershed.

PAvg .   (0.298* pTuz )  (0.346* p jimjamal )  (0.198* pkirkok )  (0.1569* phawejah )

(1)
where
=Average rainfall data for Adhaim catchment area(mm)
PAvg .
PJimjamal. = rainfall data for Jimjamal station.
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PTuz. = rainfall data for Tuz Kharmato station.
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Laith B. Al-badranee, Ahmed H. Solman and Hesham M. Bekhit
PKirkok. = rainfall data for Kirkok station.

PHawejah = rainfall data for Hawejah station.
C. Distance Weighting method(D5W5) [17, 18].

PAvg .   (0.3085* pTuz )  (0.2034* p jimjamal )  (0.4159* pkirkok )  (0.072* phawejah )
 Two axis's method (T5A5) [19].

PAvg .   (0.276* pTuz )  (0.248* p jimjamal )  (0.291* pkirkok )  (0.183* phawejah )


(2)
(3)
 Hybrid method (H5M5) (Thiessen polygon method for each sub-catchment), To find the
percentage of the area that divided by Thiessen polygon line in each sub
catchment relative to the whole area of the sub catchment.as illustrate in
equation (4-8). The results from the equations above, multiplied by the
weighted ratio that arises from area of each sub catchments by the whole basin
as illustrated in equation (9), as seen in Fig. (3).
Figure 3 Thiessen polygon weight-factors in Adhaim tributaries sub basins.

pZygetoon   (0.4909* phawejah )  (0.2184* PTuz )  (0.2905* Pkirkok )


(4)

)
pKhassah   (0.102 * phawejah )  (0.2911* PJimjamal )  (0.5733* Pkirkok )

pTaweek   (0.2211* pTuz )  (0.7358* PJimjamal )  (0.0759* Pkirkok
(5)
(6)
PKuri   ( pTuz )

pTuz   (0.6047* PTuz )  (0.3952* PJimjamal )

(7)

(8)
PAvg .   (0.1647* pTuz )  (0.07 * PKuri )  (0.3185* pTaweek )  (0.1589* pKhassah )  (0.2866* pZygetoon )

(9)
where
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Use of Artificial Neural Networks for Simulating Adhaim River Basin, Iraq
p Zygetoon
Average rainfall for Zygetoon sub-catchment (mm)
pKhassah
Average rainfall for Khassah sub-catchment (mm)
pTaweek
Average rainfall for Taweek sub-catchment (mm)
PKuri
Average rainfall for Kuri sub-catchment (mm)
pTuz
Average rainfall for Tuz Kharmato sub-catchment (mm)
2.3.2. Data Scaling
Five formula were used to scaled all data entering to the model to make the transformed
values of the data lay between -1, and +1, The idea behind that is to make the
transformed values of the data lay between specified boundaries, between 0 and
1.(equation 10,12, and 13), where the data lay between -1, 1, (equation 11, and
14)Scaling factor equation (S.F.eq.) that used is listed below [20, 21],after ANN
technique run, The model results are transformed back to its original state by
reverse data processing.
S.F.eq. (1)
xobsi 
S.F.eq. (2)
xobsi 
xobs
xobsmax
( xobs  xobsavg )
x
(10)
(11)
obs
S.F.eq. (3)
xobsi 
( xobs  xobsmin )
( xobsmax  xobsmin )
S.F.eq. (4)
xobsi  a 
S.F.eq. (5)
xobsi 
xobs
(b * xobsmax )
( xobs  xobsavg )
( xobs * k )
(12)
(13)
(14)
where
xobsi
: Transformed vector of the data,
xobs : Original observed data,
xobsmax : Maximum value observed in data series,
xobsavg : Average value observed in data series,
xobsmin : Minimum value observed in data series,
x
obs
: Standard Deviation observed data,
a and b : Constants , 0.2 and 1.2 respectively,
k: Empirical Parameter.
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2.3.3. Transfer Functions
Ten different types of Transfer Functions are tested in this study, namely as fallow.
Hyperbolic tangent sigmoid transfer function (tansig), Log sigmoid transfer function
(logsig),Hard limit transfer function (hardlim), Symmetric hard limit transfer function
(hardlims), Positive linear transfer function (poslin), Linear transfer function (purelin), Radial
basis transfer function (radbas), Saturating linear transfer function (satlin), Symmetric
saturating linear transfer function (satlins), Soft max transfer function (softmax) [22, 21].
2.3.4. Performance Criteria
Two main Performance criteria used to test the Artificial Neural Network (ANNs)
performance ability for comparison of the error between the observed and the simulated
model output, first graphical method that allows viewing the entire data set at once. The
shortcoming of this procedure is that it depends on personal judgment, second performance
criteria is statistical and mathematical methods which are more narrowly focused on a
particular aspect of the data and often try to compress that information into a single number,
two performance evaluation criteria have been used in this study. These criteria are listed
below5
Correlation coefficient ( R ): Define as a numerical measure of statistical relationship
between given data set of observations, and simulated data, they all assume values in
the range from −1 to +1, where +1 indicates the strongest possible agreement [23].
R


( (qobsi * qsimi )  n * (qobs
* qsim
))
(15)




( ((qobs
) 2  n * (qsim
) 2 ) *  ((qsim
) 2  n * (qsim
)2 )
i
Root mean square error ( RMSE ): Defined as the square root of the average of squared
errors between predicted and observed values, value of 0, would indicate a perfect fit to the
data. In general, a lower value is better than a higher one [19].
RMSE 
 (q
obsi
 qsim i ) 2
(16)
n
where:
qobsi

qobs
: Observed flows at time i.
: Mean observed flows.

qsim
: Simulated flows value at time i.
i

q sim
: Mean simulated flows.
n: no of record.
Finally, main suggested procedure can be summarized as illustrated in Fig. (4).
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Use of Artificial Neural Networks for Simulating Adhaim River Basin, Iraq
Figure 4 Flow chart algorithm illustrates the steps for model formulation.
3. RESULTS AND DISCUSSION
By applying steps in Fig. 4 to build the ANNs Model and evaluating quotients according to
the performance standards, the results can be summarized as follows.
 Average Rainfall data five methods were used to estimate Average Rainfall (see
section 2.3.1), using equation from number 1 to 9, according to (Fig.4, step 3),
the results can have summarized as seen in Table 3.
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Table 3 Correlation coefficient (R) verses different rainfall methods for different time step
time step
15 days
20 days
55days
30 days
Train
Validation
Test
overall
RMSE
Train
Validation
Test
overall
RMSE
Train
Validation
Test
overall
RMSE
Train
Validation
Test
overall
RMSE
Correlation coefficient (R) for different rainfall methods
Arithmetic
Thiessen
Distance
Two axis's
Hybrid
Mean
polygon
Weighting
method
method
(AM)
(TP)
(DW)
(TA)
(HM)
0.77
0.81
0.71
0.72
0.77
0.68
0.66
0.73
0.63
0.7
0.6
0.6
0.68
0.73
0.77
0.75
0.74
0.71
0.709
0.76
0.0032
0.0047
0.0025
0.0031
0.0024
0.72
0.72
0.75
0.77
0.75
0.79
0.79
0.62
0.61
0.72
0.76
0.72
0.61
0.52
0.61
0.74
0.73
0.73
0.71
0.74
0.01
0.0036
0.0072
0.0033
0.0018
0.77
0.73
0.78
0.83
0.75
0.60
0.68
0.68
0.63
0.77
0.64
0.71
0.71
0.6
0.72
0.74
0.71
0.76
0.77
0.76
0.0085
0.0031
0.0065
0.0081
0.0011
0.84
0.84
0.84
0.83
0.91
0.79
0.77
0.73
0.80
0.72
0.76
0.74
0.7
0.76
0.64
0.83
0.82
0.81
0.82
0.85
0.0024
0.002
0.002
0.0024
0.0018
 Scaling factor
Five scaling factors formula were applied to normalized data, using equation from number 10
to 14, according to (Fig.4- step 4), based on maximum (R-value) estimated from Table 3, the
obtained results can have summarized as seen in Table 4.
Table 4 Correlation coefficient (R) for different scaling factor formula for different time step
15 days
Maximum
obtain (R)
From
Table 3.
Hybrid
method
(HM)
20 days
Thiessen
polygon
(TP)
25 days
Two axis's
method
(TA)
Time step
(days)
30 days
Arithmetic
Mean
(AM)
Train
Validation
Test
overall
RMSE
Train
Validation
Test
overall
RMSE
Train
Validation
Test
overall
RMSE
Train
Validation
Test
overall
RMSE
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Correlation coefficient (R) for different scaling factor
formula
S.F.eq.
S.F.eq.
S.F.eq.
S.F.eq.
S.F.eq.
(1)
(2)
(3)
(4)
(5)
0.77
0.73
85748
0.75
99700
0.7
0.65
85736
0.66
997.0
0.77
0.69
85646
0.75
9977
0.76
0.788
85745
0.75
99774
0.0024
8554
858857
85857
9999.0
8576
8575
8578
0.76
0.75
8557
8575
8574
0.68
0.72
8575
8557
8565
0.73
0.61
8574
8573
8576
0.71
0.74
85885
858845
858835
85884
0.0018
8575
8585
8585
0.77
0.75
8575
8569
8563
0.743
0.77
8568
8567
856
0.739
0.72
8575
8578
8574
0.756
0.76
8559
8585
858888
85889
0.0011
0.91
8586
8587
8585
99.0
0.72
857
8575
8587
9970
0.64
8565
8563
8563
9974
0.85
8583
8585
8579
99..
0.0018
85659
85886
85859
9999..
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Use of Artificial Neural Networks for Simulating Adhaim River Basin, Iraq

Number of neuron
There is no specific rule to determine the optimal number of neurons that can achieve balance
between training, validation and testing phases. as an example, for 30 days, at 4-5 neurons the
best correlation coefficient was achieved for three phases, by increase the number of neuron
the correlation coefficient increase in training stage, but it well decries in validation and test at
the same time.
Figure 5 Correlation coefficient (R) verses numbers of neurons for different time step.

Performance criteria
Two main Performance criteria used to evaluate the Artificial Neural Network (ANNs)
performance ability for comparison of the error between the observed and the simulated
model output for different time steps.
First performance criteria are graphical method, as seen in Figs 6 to 9, which show
Adhaim river graphical performance criteria of observed versus simulated discharge for 21
years with different time steps (15, 20, 25, and 30 days). As well as the scatter diagram for the
same time steps for three phases respectively.
Second one are statistical performance criteria of Adhaim river as shown in Tables 3-4,
best results obtain from ANNs runs with different time step relationships between number of
neuron for different type of scaling method.
Validation: R=0.72935
Output ~= 0.55*Target + 0.014
Output ~= 0.64*Target + 0.013
Training: R=0.79939
0.8
Data
Fit
Y=T
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
0.6
0.8
0.6
0.5
0.4
0.3
0.2
0.1
0
0.8
Data
Fit
Y=T
0.7
0
0.2
Target
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
0.6
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.8
0
0.2
Target
0.4
0.6
0.8
Test: R=0.77051
0.8
0.6
0.4
0.2
0
0
0.8
All: R=0.77416
Data
Fit
Y=T
0.2
0.4
0.6
Target
Target
0.6
1
1
Data
Fit
Y=T
Output ~= 0.55*Target + 0.014
0.6
0.8
Output ~= 0.47*Target + 0.01
Output ~= 0.55*Target + 0.014
Output ~= 0.64*Target + 0.013
Data
Fit
Y=T
0.7
Test: R=0.77051
Validation: R=0.72935
Training: R=0.79939
0.8
0.4
Target
0.8
1
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
Target
All: R=0.77416
1
1
Output ~= 0.55*Target + 0.014
Output ~= 0.47*Target + 0.01
Figures 6 Hydrographs and Scatter plot of observed and simulated flow for Adhaim river at Naros
station for (training, validation, and testing phase) for Fifteen days’ time step.
Data
Fit
Y=T
0.8
0.6
0.4
Data
Fit
Y=T
0.8
0.6
0.4
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0.2
0
0
0.2
0.4
0.6
Target
0.8
1
2043
0.2
0
0
0.2
0.4
0.6
Target
0.8
1
editor@iaeme.com
Laith B. Al-badranee, Ahmed H. Solman and Hesham M. Bekhit
Validation: R=0.72951
1
Output ~= 0.51*Target + 0.018
Output ~= 0.57*Target + 0.015
Training: R=0.75446
1
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
1
0
0.2
0.4
Target
Test: R=0.6127
Validation: R=0.72951
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
1
0
0.2
0.4
Target
0.6
0.8
Test: R=0.6127
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
1
0
Target
1
0.8
1
1
Output ~= 0.55*Target + 0.016
0.8
Output ~= 0.37*Target + 0.023
Data
Fit
Y=T
0.8
All: R=0.74518
1
1
Output ~= 0.51*Target + 0.018
Output ~= 0.57*Target + 0.015
Training: R=0.75446
1
0.6
Target
0.2
0.4
0.6
0.8
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
1
0
0.2
0.4
Target
0.6
Target
All: R=0.74518
1
1
Output ~= 0.55*Target + 0.016
Output ~= 0.37*Target + 0.023
Figure 7 Hydrographs and scatter plot of observed and simulated flow for Adhaim river at Naros
station for (training, validation, and testing phase) for twenty days
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0
0.2
Target
0.4
0.6
0.8
1
Target
Training: R=0.75692
Validation: R=0.77751
Output ~= 0.56*Target + 0.032
Output ~= 0.57*Target + 0.024
1
0.8
Data
Fit
Y=T
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
0.6
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0.8
0
0.2
Target
Training: R=0.75692
Validation: R=0.77751
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
0.6
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0.8
1
0.8
1
All: R=0.76076
0
0.2
Target
0.4
0.6
0.8
Output ~= 0.57*Target + 0.024
0.7
0.8
1
Output ~= 0.62*Target + 0.012
Output ~= 0.56*Target + 0.032
Output ~= 0.57*Target + 0.024
Data
Fit
Y=T
0.6
Target
Test: R=0.72578
1
0.8
0.4
Data
Fit
Y=T
0.6
0.5
0.4
0.3
0.2
0.1
0
1
0
0.2
0.4
Target
Target
0.6
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
Target
Test: R=0.72578
R=0.76076
Figure 8 Hydrographs
and ScatterAll:plot
of observed and simulated flow for Adhaim river at Naros
station for (training, validation, and testing phase) for Twenty-five - days’
Output ~= 0.57*Target + 0.024
Output ~= 0.62*Target + 0.012
1
Data
Fit
Y=T
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
Target
0.6
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
1
Target
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Use of Artificial Neural Networks for Simulating Adhaim River Basin, Iraq
Validation: R=0.79112
Training: R=0.85194
Data
Fit
Y=T
0.8
Output ~= 1*Target + 0.014
Output ~= 0.73*Target + 0.013
1
0.6
0.4
0.2
0
0
0.2
0.4
0.6
0.8
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
1
0
0.2
Target
0.4
0.2
0
0
0.2
0.4
0.6
0.8
0.8
0.6
0.4
0.2
0
1
Data
Fit
Y=T
0.2
0.4
0.6
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0
Target
0.8
0
0.2
0.4
0.6
Data
Fit
Y=T
0.8
0.6
0.4
0.2
0
0.8
0
Target
Target
Test: R=0.74311
All: R=0.82203
Output ~= 0.75*Target + 0.016
0.6
Output ~= 0.87*Target + 0.019
Output ~= 1*Target + 0.014
Output ~= 0.73*Target + 0.013
0.8
0.8
1
1
Data
Fit
Y=T
0.6
Target
Test: R=0.74311
Validation: R=0.79112
Training: R=0.85194
0.4
0.2
0.4
0.6
0.8
1
Target
All: R=0.82203
Figure 9 Hydrographs and Scatter plot of observed and the ANN estimated flow for Naros station
(training, validation, and testing phase) for thirty - days’.
Output ~= 0.75*Target + 0.016
Output ~= 0.87*Target + 0.019
1
Data
Fit
Y=T
0.8
0.6
Data
Fit
Y=T
0.8
0.6
For Adhaim River Flow Forecasting, four models were built using different time steps, as
they can serve different purposes and use 5-day time steps starting from 15 days. Figures 6 to
9 show for different time steps that the maximum peak predictions are underestimated and the
minimum peak predictions are overestimated, which show some weakness in peak values.
Target
Target
The final Optimum results obtained from ANN runs can be shown in Table 5, which
shows that the correlation coefficient (R) for different validation and testing phase scaling
factor formula is within the range of the training data set, which can be used to build a river
model for any future Adhaim river forecast for a specific time period.
0.4
0.2
0
0
0.2
0.4
0.6
0.4
0.2
0
0.8
0
0.2
0.4
0.6
0.8
1
Table 5 Optimum results obtain from ANNs runs with different time step for Adhaim basin. based on
statistical performance criteria
Testing
overall
0.72
0.71
0.77
0.79
0.77
0.61
0.72
0.74
0.77
0.74
0.76
0.82
0.0013
0.0018
0.0011
0.0011
0.2
0.2
0.2
0.2
0.8
0.8
0.8
0.8
Number of
neuron
Validation
0.79
0.76
0.75
0.85
Number. of
Hidden
layers
Moment rate
value (mc)
Training
D.W.
T.P.
HM
T.A.
Learning
Rate value
(LR)
Best rainfall
methods
S.F.-3
S.F.-1
S.F.-1
S.F.-4
optimum training parameters
RMSE
Best Scaling
factor
Time (days)
15
20
25
30
Correlation coefficient (R)
1
1
1
1
6
5
4
4
4. CONCLUSIONS
The conclusions of this research can be summarized as follow: Why ANNs?
Unlike other models, using ANNs requires only two variable and less data while others
need several variables, On the other hand, there is no particular law to certify selected
parameters, such as number of neurons, type of transfer function, number of hidden layers,
and learning rate value. So, the method used in this model leading to results is trial-and-error,
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Laith B. Al-badranee, Ahmed H. Solman and Hesham M. Bekhit
on this basis, we have developed a procedure to reduce trail to reach the optimum simulation
with minimum effort.
Generally, the performance of ANN models can be undoubtedly superior to conventional
hydrological models in situations that do not require more detailed knowledge of the
hydrological system. [21].
Five scaling factor formula was being used Best correlation coefficient for three phases
obtained from equations number (10,12, and 13) that achieved higher simulation, based on
performance criteria test for each formula.
After application ANNs on Adhaim river and according to final estimated results,
optimum simulation obtained for time step up to fifteen days, trained with LevenbergMarquradt back propagation algorithm, with one hidden layer, three transfer functions mainly
of 'tansig', 'logsig', hidden neurons and linear output 'trainlm', ANNs model was able to
provide a better generalization of the complex, dynamic, non-linear, and fragmented in the
semi-arid region in Adhaim flow rate estimation process, in which the perception and river
flow are very irregular.
Finally, the study revealed the feasibility of adopting the ANNs as a river flow forecasting
tool
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