Journal of Environmental Treatment Techniques
2015, Volume 3, Issue 4, Pages: 215-222
J. Environ. Treat. Tech. ISSN:
2309-1185
Journal weblink: http://www.jett.dormaj.com
Comparing the Estimation of Suspended Load using Two
Methods of Sediments Rating Curve and Artificial Neural
Network (A Case Study: Cham Anjir Station, Lorestan Province)
Bahador Abbaspour 1 , Amir Hamzeh Haghiabi 2
1- M.S. Student of Water Structures, Water Engineering Department, Lorestan University
2- Associate Professor ,Water Engineering Department, Lorestan University
Received: 15/01/2015
Accepted: 17/05/2015
Published: 30/12/2015
Abstract
It is significantly important to predict and estimate the sediment load of the rivers to manage rivers and dam reservoirs in
water projects. In this study, the suspended load of the river is predicted using artificial neural network. In this paper, it is
attempted to evaluate the performance of artificial neural networks in predicting the suspended sediments. Using ANN
(Multilayer Layer Perceptron Model), the suspended sediment in hydrometric station of Cham Anjir river of Khorramabad
has been predicted and the results have been compared with sediment rating curve. Based on the obtained results, ANN
presents acceptable results in simulating the suspended load in Cham Anjir station, in such a way that it is of higher accuracy
compared to sediment rating curve. The results showed that ANN could be employed to estimate the sediment suspended
load with appropriate accuracy and more confidence compared to the rating curve. Here, it should be noted that neural
network could not predict the peaks accurately, and this is regarded as a weak point of this model.
Keywords: Suspended Sediment, ANN, Sediment Rating Curve, Khorramabad River.
1 Introduction 1
concentration
and
he
indicated
that
multi-layer
As
an
intensifying
process,
erosion
and
perceptron models performed better than the generalized
sedimentation result in the loss of agricultural fertile soil
regression neural networks and radial basis function
and cause irreparable damages to constructive water
networks [9].
projects, such as sediment accumulation behind dams
Kisi (2005) employed ANN to model the suspended
and reducing their useful volume, destructing the
sediment load of flow. He also used sediment
structures, damaging the coasts and harbors, reducing the
rating
curves
and a
multi-regression
model to
capacity and increasing the maintenance cost of irritation
predict the sediment load and showed that ANN
canals to name a few. On the other hand, sediment
produced the best results [10].
transfer affects the water quality indices with regard to
Using the measurement data of sediment suspended
potable and agricultural water. Therefore, estimating the
load and with the help of sediment rating models, it is
amount of sediment is required in soil protection
possible to predict the transfer sediment. However,
projects, designing and implementing water structures,
estimating the sediment using this model always
watershed and utilizing water resources [7].
associated some errors [2]. In addition, other researches
Jain (2001) used ANN to introduce the relation of
have shown that estimating the suspended sediment
sediment density in Mississippi river and proved that it
using rating curves always associates with errors and it
gives more reliable results in comparison with other
appears that the ANN model leads to fewer errors using
methods [8].
rating curves [12]. Therefore, it is required to utilize
Cigizoglu (2002) compared the results from ANN
smart methods such as ANN in estimating the amount of
with sediment rating curves to predict the density
suspended load. To simulate the suspended load using
of suspended sediments [3].
ANN, two algorithms of back-propagation training
The history of scientific investigations in the field of
algorithm and radial functions, they used the values
suspended sediment transfer in rivers is over a century, in
obtained in these two methods to draw the sediment
such a way that the first sampling of suspended load
diagram and they realized that back-propagation training
from the rivers was conducted in 1845 in Mississippi
algorithms estimate the discharge of river’s sediment
River [16].
load more accurately [13]. Dehghani et al. 2009
Kisi (2004) used different ANN techniques for
compared the estimation of suspended load using two
predicting and estimating daily suspended sediment
methods of sediment rating curve and ANN. The results
showed that ANN could be utilized more accurately and
confidently to estimate the sediment suspended load
Corresponding author: Bahador Abbaspour, M.S.
compared to rating curve with and without data
Student of Water Structures, Water Engineering
classification [6]. Using the data of water discharge and
Department,
Lorestan
University
E-mail:
sediment discharge simultaneously, in Galinak station in
b.a1366@yahoo.com.
Talghan River, along with some geomorphologic
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Journal of Environmental Treatment Techniques
2015, Volume 3, Issue 4, Pages: 215-222
parameters of the catchment area of this river, ANN was
values of total sediment load. The reason might be the
used to model the estimation of daily suspended
effect of suspended load on total load value, and the
sediment. The results of this study showed the higher
suggested equations are not able to calculate it [7].
accuracy of NN relative to regression models [15]. The
Melesse et al. (2011) estimated Suspended sediment
common sampling method in most hydrometric stations
loads for three major rivers (Mississippi, Missouri and
is measuring the sediment suspended load. Then, the bed
Rio Grande) in USA using MLP modeling approach.
load is estimated as a percent of suspended load with
Results from ANN model were compared with results
regard to geomorphologic conditions of the river.
from multiple linear regressions (MLR), multiple non-
However, the results of this procedure are not reliable
linear
regressions
(MNLR)
and
Autoregressive
[12]. It is required to have accurate information about the
integrated moving average (ARIMA) using correlation
amount of sediment load of the whole river in many
coefficient (R), mean absolute percent error (MAPE) and
water resources projects such as estimating the
model efficiency (E). The results show that the ANN
sedimentation volume in dam reservoirs. In order to
predictions for most simulations were superior compared
determine the total load of the floodway, numerous
to predictions using MLR, MNLR and ARIMA
procedures have been proposed, each one of which are
[14].Therefore, there is a need to present new
applicable in special conditions. Some of these relations
procedures, which could estimate total sediment load
are presented based on extending the equations of bed
more accurately, without requiring special conditions to
load. However, in addition to the need for special
be applied. The aim of this study has been improving the
conditions, the calculated value of total sediment load
estimation of total sediment load of the rivers using ANN
using these relations is different from the measured
(multilayer perceptron and radial base function).
Nomenclature

discharge of suspended sediment (tons/day)
୫ ୬
minimum value of observed data

discharge of its corresponding flow (m 3 /s)
୫ୟ୶
maximum value of observed data
a , b
coefficients of the equation
ୱ୲
mean of estimated data
 ୭ୠୱ
observational discharge
୭ୠୱ
mean of observational data

ୱ୲
estimated discharge of neural network
ୱ୲
estimated data
Target discharge of normalized suspended sediment
୭ୠୱ
observational data
Output estimated discharge of suspended sediment from neural

correlation between data
network
  Root mean square error
expressive of normalized data
Nash coefficient
expressive of observational data
2 Material and Methods
responses from the network [5]. The artificial neural
2.1 Features of Catchment under Study
network consists of the following sections:
This
study
was
conducted
in
Cham
Anjir
1-
Input layer: In this layer, inputs are connected
hydrometric station in the catchment of Khorramabad
to the outside world and the next layer of the network.
River, regarding the detailed statistics of the available
No processing is performed in this layer.
concentration. The station is located in 33 o and 22 north
2-
Hidden layer: a layer in which processing
latitude with 1122 meter height and upstream catchment
occurs. The network could consist of one or more middle
area of 1280 km 2 . The statistical details of the catchment
layers. The designer, often through trial and error
under study are shown in Table 1 and the catchment
process, obtains the number of layers and the number of
location is shown in Fig. 1.
nodes in each layer.
3-
Output layer: in this layer, the outputs are
2.2 Artificial Neural Network
connected to the outside world, in which the outside
Artificial neural network is a variant of artificial
vectors are mapped and established. Often, back-
intelligence, which performs similar to human brain,
propagation learning rule is employed to train the
albeit general and incomplete. In fact, ANN is an idea for
Multilayer Layer Perceptron (MLP) Neural Networks. In
data processing, which is inspired by bio-neural-system
other words, the topology of the network is completed
and processes the data similar to the brain. This system
through back-propagation learning rule. Neural networks
consists of numerous processing elements called
generally lack good interpolation. Therefore, this should
neurons, which act coordinately to solve a problem.
be considered when selecting the training pattern. To do
These neurons learn the problem’s process through an
so, the patterns are classified into training and test
example (observational data). In other words, they
patterns, before the neural network starts working. The
transfer data to the network structure through processing
training patterns should cover the data space as far as
experimental data, knowledge or hidden rules. In fact,
possible to produce better learning. It is obvious that the
ANN is a math model, which is capable of modeling and
more the training patterns, the higher the generalization
creating nonlinear math relations for interpolation. In
capability of promoting the network. Although training is
fact, ANN is trained through a limited series of real data
a process that occurs during a relatively long time, but
and if the parameters, which affect the phenomenon
after generalization, it could present an output in lieu of
under study, are selected accurately and given to the
each input rapidly.
network, it could be expected to receive rational
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2015, Volume 3, Issue 4, Pages: 215-222
Fig. 1: The location of catchment area under study .
Table 1: Statistical details of flow discharge and sediment data in Cham Anjir station.
variable
mean
minimum
maximum
Standard deviation
flow discharge (m 3 /s)
8.84
0.33
34.89
6.30
Sediment discharge (tons/day)
123.87
0.469
1597.844
217.1286
Fig. 2: The structure of Artificial Neural Network.
2.3 Sediment Rating Curve


(1)
Among common methods in estimating discharge of
suspended sediment load in hydrometric stations is
In addition, various methods are proposed to increase
creating a connection between discharge data of the
the accuracy of estimating the sediment through rating
sediment and their corresponding discharge data, which
curve, among which is classifying the data in different
is obtained through fitting of the curve between flow
shapes. Classifying the data could be used in the form of
discharge values and their corresponding suspended
annual, seasonal, monthly, intermediate categories,
sediment Eq. (1).
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2015, Volume 3, Issue 4, Pages: 215-222
classifieds discharge, similar hydrological cycle, and
୭ୠୱ
ୱ୲
periods of low and high precipitation.
(5)
ୱ୲
୭ୠୱ
2.4 Data and Methods Used
R 2 shows the correlation between data and the more
In this study, the data and information available in
and close to one is R , the more correlation exists among
2
Cham Anjir hydrometric station, Khorramabad, have
observational and estimated data. RMSE shows how
been used. To do so, 473 corresponding data of flow
much the estimated values deviate from observational
discharge
and sediment
discharge
are
measured
data and the less is RMSE, the less is the deviation
simultaneously. In artificial network methods, first, some
among the data and the more accurate are the results.
data are selected to train the network, which are
Nash coefficient (r 2 ) is a statistical index, which shows
expressive of the conditions of the problem and other
the conformation ratio in hydrologic models. The value
data are used to test the performance of trained network.
of this index is between 1 and
. When it approaches
The main point in selecting the training data is that it
one, the model has a better performance; if it is zero, it
should cover a wide range of assorted data. In this study,
means that the model corresponds to the mean of the
70% of the data were used as training data, data 15% for
data; and when it is negative, the mean of the data is
testing and 15% for testing the accuracy of the neural
more efficient than the model [5].
network model. An important fact in training neural
networks is normalizing the data before being used in the
model. This fact leads to better and faster training of the
3 Results and Discussion
model, especially when the changing range of the inputs
3.1 Results from Artificial Neural Network
is high, because entering data in raw form reduces the
The results from the final model of neural network,
speed and accuracy of the network. To normalize the data
used in this study, are shown in Fig. 3, 4 and 5 for test
in this study Eq. (2), is employed.
stage data along with related observational data.
As can be seen in Fig. 3, the neural network has
lower or more estimation compared to observational
୫ ୬
(2)
୫ୟ୶
୫ ୬
values, which could be one of the weak points of neural
network in estimating the suspended sediment. In other
Neural network was designed after selecting training
words, the neural network has simulated the suspended
data, testing and validation. To model the neural
sediment related to the base and normal discharge, well.
network, the software of MATLAB (Matrix Laboratory)
However, in simulating the sediment in flood events, it
was used. Among 473 data available, 331, 71 and 71 data
has not seen much successful. Results from Zhu et.al
were
used
for
training,
testing and
validation,
show this fact well.
respectively. To achieve a suitable structure of artificial
neural network, different models are designed and tested
3.2 Results from Sediment Rating Curve
with different hidden layers and nodes and the related
As the study continued, the sediments were estimated
results were compared. After creating and testing
through sediment rating curve to compare the results of
different structures of the network and evaluating the
neural network with the results of the common sediment
obtained results, the final structure of the employed
rating curve method. In sediment rating curve, a
neural network for this study was selected, which was
regression relation is usually extracted between the data
multilayer perceptron and back-propagation training
corresponding to the flow and sediment discharge. Then,
method was used, which was simulated using MATLAB.
based on this relation, the daily discharge value of
The input and output layer of the network, which results
suspended sediment is estimated for those days that the
in water discharge and output layer of sediment
concentration of sediment is not measured.
discharge, was used to evaluate the results from different
In this study, the discharge and sediment data related
models of artificial neural network and sediment rating
to those days, which were used for modeling neural
curve method in addition to comparing the final results
network for training, were drawn on a coordinates system
with observed values, along with 3 statistical parameters
to determine the sediment rating curve and obtain the line
as follows:
and equation related to the relation between discharge
and suspended sediment. Fig. 6 shows the line and
ሺ(
equation related to the rating curve. After obtaining the
ୱ୲
ୱ୲ ) (
୭ୠୱ
୭ୠୱ )ሻ

(3)
line equation in sediment rating curve method, data of the
ට ሺ
flow discharge related to the days, used in test stage of
ୱ୲
ୱ୲
୭ୠୱ
୭ୠୱ
neural network, are placed in this equations, in such a
୭ୠୱ
ୱ୲
way that all of the discharge data of test stage are placed
 
(4)
separately in the equation resulted from the sediment
rating curve.
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Journal of Environmental Treatment Techniques
2015, Volume 3, Issue 4, Pages: 215-222
Fig. 3: Results from the simulation of suspended sediment using neural network against observational values
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
y = 0.7992x + 0.0168
0.2
R² = 0.86326
0.1
0
0
0.2
0.4
0.6
0.8
1
1.2
observed suspended sediment (tons/day)
Fig. 4: The diagram of the results from the simulation of suspended sediment using neural network against observational values.
Then, the related
corresponding sediment is
The results of different statistical parameters such as
calculated. In Fig. 7, the values of estimated sediment
R 2 , RMSE and r 2 , based on rating curve and artificial
(based on sediment rating curve) are placed against the
neural network, are shown in table 2. According to the
measured values. Comparing Fig. 4 and 7, which show
Table, it could be observed that the neural network
the results of neural network and the common method of
method presents the results with less error and higher
sediment rating curve, respectively, against observational
correlation.
data, shows the relative superiority of neural network
method compared to sediment rating curve method.
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Journal of Environmental Treatment Techniques
2015, Volume 3, Issue 4, Pages: 215-222
Fig. 5: Results from neural network in each stage of training, testing and validation.
1800
1600
1400
1200
1000
800
600
y = 1.7745x 1.7111
400
R² = 0.6677
200
0
0
5
10
15
20
25
30
35
40
flow discharge (m3/s)
Fig. 6: Sediment rating curve and the line and equation obtained to estimate the sediment using this method.
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Journal of Environmental Treatment Techniques
2015, Volume 3, Issue 4, Pages: 215-222
900
800
700
600
500
400
y = 3.7293x0.6677
300
R² = 0.6677
200
100
0
0
200
400
600
800
1000
1200
1400
1600
1800
observed sediment (tons/day)
Fig. 7: The values of estimated suspended sediment based on sediment rating curve against observational values.
Table 2: Calculated statistical parameters related to neural network and rating curve.
Statistical parameters
Rating curve
Neural network
R 2
0.6677
0.87247
RMSE
0.152
0.0187
r 2
0.654
0.945
4 Conclusions
Ahvaz Seventh National Conference of River
In this study, the application of neural network
Engineering
method and rating curve in estimating the amount of
2- Achite, M., and S. Ouillon, 2007. Suspended
suspended sediment in Cham Anjir stations was
Sediment Transport in a Semiarid Watershed, J.
investigated. To do so, two parameters were used as
Hydro, 84: 3, 187-202.
inputs and the model was simulated. The neural network
3- Cigizoglu H.K., 2002. Suspended sediment estimation
presents acceptable results for simulation in Cham Anjir
for rivers using artificial neural networks and
station in such a way that one of the statistical parameters
sediment
rating
curves.
Turkish
Journal
of
to compare the acc uracy of the model’s estimations
Engineering Environmental Sciences 26(1):27-36.
relative to observational data is R 2 . R 2 , obtained from the
4- Dastorani, M.T. and H. Afkhami, 2011. Evaluation of
neural network, nearly equals 87% and R 2 resulted from
the application of artificial neural networks on
sediment rating curve is nearly 67%, which shows the
drought prediction in Yazd (Iran), Journal of Desert,
relative superiority of neural network compared to the
16 (1): 39- 49.
rating curve. In fact, predicting and estimating this
5- Dastorani, M. T., Kh. Azimi Fashi, A. Talebi, and M.
phenomenon is difficult and sometimes inaccurate.
R. Ekhtesasi, 2012. Estimating Suspended Sediment
Since, in predicting the amount of sediments in the rivers
Using Artificial Neural Network (A Case Study:
different factors involve and in addition, it is a of a
Jamishan Catchment Area, Kermanshah Province),
complicated nature: many problems related to suspended
Journal of watershed management, N. 6.
sediments in rivers could be solved through this method,
6- Dehghani, A., M. A. Zanganeh, and N. Koohestani,
since artificial intelligent methods are used to analyze
2009, Comparing the Estimation of Suspended Load
those problems for which there is not enough knowledge
Using Two Methods of Sediment Rating Curve and
or clear description. Certainly, it should be considered
Artificial Neural Network, (A Case Study: Doogh
that neural network could not predict the peak points and
River, Golestan Province), Journal of Agricultural
this is considered as one of its weak points.
Sciences and Natural Resources, 16 (1-A): 1-12
7- Emami, S. A., 2000. Sediment Transfer, Jahad
Acknowledgement
Daneshgahi Publications, Amir Kabir Technical
The authors gratefully acknowledge the Lorestan
University, Tehran, First Edition, 716 Pages.
Regional Water Company for data contribution.
8- Jain S.K., (2001). Development of integrated sediment
rating curves using ANNs. Journal of Hydraulic
Engineering ASCE 127(1):30- 37.
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