Abstract

In this paper, the trap efficiency (TE) of retention dams was investigated using laboratory experiments. To map the relation between TE and involved parameters, artificial intelligence (AI) methods including artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were utilized. Results of experiments indicated that the range of TE varies between 30 and 98%; hence, this structure can be recommended to control sediment transport in watershed management plans. Experimental results showed that by increasing the longitudinal slope of streams, TE decreases. This finding was observed for Vf/Vs parameter, as well. By increasing the mean diameter grain size (D50) and specific gravity of sediments (Gs), TE increases. Results of all applied AI models demonstrated that all of them have suitable performance; however, the minimum data dispersivity was observed in SVM outcomes. It is notable that the best performance of transfer, membership and kernel functions were related to tansig, gaussmf and radial basis function (RBF) for ANN, SVM and ANFIS, respectively.

NOTATION

     
  • AI

    artificial intelligence

  •  
  • ANN

    artificial neural network

  •  
  • C

    concentration of sediment

  •  
  • CFD

    computational fluid dynamic

  •  
  • D50

    mean diameter of sediments

  •  
  • DDR

    developed discrepancy ratio

  •  
  • gaussmf

    Gaussian curve membership function

  •  
  • GP

    genetic programing

  •  
  • Gs

    specific gravity

  •  
  • hmax

    maximum depth of flow

  •  
  • logsig

    log-sigmoid transfer function

  •  
  • MARS

    multivariate adaptive regression splines

  •  
  • MFs

    membership functions

  •  
  • MLPNN

    multilayer perceptron neural network

  •  
  • purelin

    linear transfer function

  •  
  • Qin

    discharge of inflow

  •  
  • Qout

    discharge of outflow

  •  
  • R2

    coefficient of determination

  •  
  • radbas

    radial basis transfer function

  •  
  • RBF

    radial basis function

  •  
  • RMSE

    root mean square of error

  •  
  • S

    longitudinal slope of river

  •  
  • SVM

    support vector machine

  •  
  • tansig

    hyperbolic tangent sigmoid transfer function

  •  
  • TE

    trap efficiency

  •  
  • Vf

    volume of flood

  •  
  • Vs

    volume of reservoir

  •  
  • wtaver

    weighted avarage

  •  
  • γs

    density of sediment

  •  
  • γw

    density of flow

INTRODUCTION

Deposition of sediments in dam reservoirs causes a reduction in their useful volumes and, in addition, this phenomenon at the entrance of intake structures causes difficulties in flow diversion (Moradinejad et al. 2017). Sediment transport in hydrosystems sometime causes the distortion of hydraulic structures specifically hydro-mechanical installations such as pump stations, bottom outlets and sluice gates (Madadi et al. 2016). Sediment deposition through water conveyance structures such as irrigation channels reduce their discharge capacity and changes their hydraulic characteristics such as manning factor (Depeweg et al. 2014; Parsaie & Haghiabi 2016a). Recently, the effect of sediment deposition on hydraulic efficiency of weirs has been investigated. Based on the reports, sediment deposition significantly decreases the discharge capacity of weirs (Fahmy 2015). One way to control sediment transport is to develop watershed plans such as retention dams (Cao et al. 2011). By reducing the flood peak and flood velocity, retention dams decrease the power of flow which leads to deposition of sediments and reduction of flow erosivity (Fiener et al. 2005; Camnasio & Becciu 2011; Vorogushyn et al. 2012; Del Giudice et al. 2014; Yazdi & Salehi Neyshabouri 2014; Liu et al. 2015; Nikoo et al. 2015; Yazdi et al. 2016). Performance of a retention dam is defined by trap efficiency (TE) factor. This factor is calculated as the ratio of sediment entering to the outlet sediments from reservoir of a retention dam. Based on reports, retention dams have a high ability to reduce and delay the flood peak. This approach has been proposed as a rational idea among watershed management projects to improve the lifetime of high dam projects (Parsaie et al. 2017a). The main points related to retention dams are site selection and defining these heights. To select the site, a geographic information system and Google Earth have been proposed (Yazdi & Salehi Neyshabouri 2012; Madadi et al. 2015). To study the feasibility of retention dams, in addition to site selection, measuring and estimation of sediment load is necessary (Parsaie & Haghiabi 2017a, 2017b; Parsaie et al. 2017b). To estimate the discharge of flow and sediment load, in addition to field measurements, advanced numerical methods including artificial intelligence (AI) techniques and computational hydraulic packages such as GSTARS and HEC-RAS have been proposed (Hassan-Esfahani & Banihabib 2016; Parsaie et al. 2016a; Parsaie & Haghiabi 2016a). AI techniques including artificial neural network (ANN), support vector machine (SVM), genetic programming, and adaptive neuro fuzzy inference system (ANFIS) have been successfully utilized all around the world to predict river flow and sediment loads (Kiat et al. 2008; Ghani et al. 2011; Baghbanpour & Kashefipour 2012; Azamathulla et al. 2013; Ghani & Azamathulla 2014). Based on the important role of retention dams for controlling floods and sediment transport, in this study, the performance of retention dams in terms of sediment deposition is investigated. To this end, parameters involved in the performance of retention dams are derived using dimensional analysis and a series of experiments is programmed. To accurately model the relation between the influenced parameters and TE, obtained results of experiments are modeled and predicted using AI, including ANFIS, ANN and SVM.

MATERIALS AND METHODS

Dimensional analysis

Different types of hydraulic and sediment parameters influence the TE of retention dams. The main parameters are collected in Equation (1).  
formula
(1)
where and are discharge of inflow and outflow, respectively; and are volumes of reservoir and flood, respectively; is the mean diameter of sediments; is maximum depth of flow at upstream of retention dams; and are specific weights of sediment and flow, respectively; C is the concentration of sediment; S is the longitudinal slope of river; and TE is trap efficiency. Using Buckingham theorem and considering and as repeated parameters, dimensionless parameters are derived as Equation (2).  
formula
(2)
Observing Equation (2), it is declared that some factors cover others. For example, C is the function of slope, because retention dams are constructed on mountainous areas and discharge of outflow depends on upstream flow head; hence, three dimensionless factors, including , are removed from Equation (2) and parameters involved in TE of retention dams are presented in Equation (3).  
formula
(3)

Experimental setup

Regarding Equation (3), a series of experiments were programmed and conducted at the laboratory of the Iranian Institute of Soil Conservation and Watershed Management (SCWMRI). Experiments were carried out in a flume with 0.25 m width, 0.25 m depth and 6 m length. At the entrances of flume, two reservoirs were considered for clear water as flood and sediment load. Three sediment sizes with different properties including Gs = 2.65 with D50 = 0.178 mm, Gs = 1.291 with D50 = 0.271 mm and Gs = 1.523 with D50 = 0.243 mm were utilized for sediments. The concentration of sediments was evaluated to define the performance of retention dams. For example, while the flood discharge was equal to 0.3 (m3/s), the concentration of sediment was considered to be 10%. This means that the volume of sediments was equal to 0.03 (m3). Figure 1 shows a schematic sketch of laboratory model used to study TE of retention dams.

Figure 1

Schematic sketch of laboratory model.

Figure 1

Schematic sketch of laboratory model.

AI techniques

Artificial neural networks

ANN is an artificial intelligent technique, the idea of which was taken from the nervous system of the human brain. An ANN consists of a group of neurons that have interconnected relation. In such a system, input data are introduced to neurons and by conducting a series of mathematical operations on them, they will be mapped to output. A simple form of ANN is shown in Figure 2. As shown in this figure and as stated previously, an ANN consists of neurons categorized in one or more layer(s). The first layer of such network is considered as input layer, the main task of which is to introduce inputs information. Certain mathematical operations are not performed in this layer. The next layer(s) that are categorized as hidden layer(s) are the main part of ANN on which the main mathematical operations are performed. In this part of the network, inputs are multiplied by weights and then are summed by a constant value called bias. As shown in Figure 2, is the weight and is the bias for each neuron. Results of mathematical operations are passed through a function called transfer function. Different types of transfer functions have been introduced, most types of which that have been widely used in hydrology engineering (Araghinejad 2013). The last layer called the output layer is assigned for summarization of mathematical process of network. Outputs of such network are compared with observed data. To minimize the difference between output data and observed data, justifying the values of weights and biases are considered. To this purpose, conventional approaches such as Levenberg–Marquardt technique or advanced optimization methods have been proposed (Heddam 2016a, 2016b, 2016c, 2016d, 2016e).

Figure 2

Sketch of multi-layer ANN architecture.

Figure 2

Sketch of multi-layer ANN architecture.

Adaptive neuro fuzzy inference systems

An ANFIS is a well-known method to map the internal relation of complex systems that are based on dataset. ANFIS is a neural network based on fuzzy logic. This idea applied the ability of both intelligent models. In fuzzy logic, inputs are fuzzed first. This operation that is called fuzzification is carried out using membership functions. Fuzzification process includes three steps: first, choosing the type of membership function; second, fuzzification and third, defuzzification. At this step, different types of membership functions are tested. Figure 3 shows a fuzzification process. As shown in this figure, a fuzzy system with two inputs and one output was presented. Assume that the rule base consists of two fuzzy if-then rules, as follows:  
formula
where A1, A2, B1 and B2 are the membership functions acted on inputs x and y; respectively; p1; q1; r1 and p2; q2; r2 are the parameters of output function. The structure of ANFIS is presented in Figure 3. At the first layer, all inputs variable take grades of membership and in layer 2, all membership grades are multiplied by each other. In layer 3, all grades of members are normalized, and in layer 4, the grant of all rules is computed. In the last layer, output is obtained as the weighted average of grade memberships (Azamathulla et al. 2008, 2009; Noori et al. 2015; Parsaie & Haghiabi 2016b; Parsaie et al. 2016b, 2016c).
Figure 3

ANFIS model structure.

Figure 3

ANFIS model structure.

Support vector regression

SVMs are a set of related supervised learning methods used for classification and regression. In many applications, a non-linear classifier provides better accuracy. In SVM, the input x is first mapped onto an m-dimensional feature space using some fixed (nonlinear) mapping, and then a linear model is constructed in this feature space. The naive way of making a non-linear classifier out of a linear classifier is to map our data from the input space X to a feature space F using a non-linear function

In the space F, the discriminant function is:  
formula
(4)
Using mathematical notation, the linear model (in the feature space) f(x, w) is given by:  
formula
(5)
 
formula
(6)
 
formula
(7)
In the feature space F, this expression takes the following form:  
formula
(8)
 
formula
 
formula
(9)
 
formula
(10)
There are many kernel functions in SVM. Therefore, how to select a good kernel function is also a research issue. However, there are some popular kernel functions for general purposes.
  • I.

    Linear kernel:

  • II.

    Polynomial kernel:

  • III.

    RBF kernel:

  • IV.

    Sigmoid kernel:

Here γ, r and d are kernel parameters. It is well known that SVM generalization performance (estimation accuracy) depends on a good setting of meta-parameters, parameters and as well as the kernel parameters. The choices of and control the prediction (regression) model complexity. The problem of optimal parameter selection is further complicated, since the SVM model complexity (and hence its generalization performance) depends on all three parameters. Kernel functions are used to change the dimensionality of input space to perform classification (Azamathulla et al. 2016; Haghiabi et al. 2017; Parsaie & Haghiabi 2017a, 2017b).

RESULTS AND DISCUSSION

Results of experiments

Results obtained from laboratory experience are presented in Table 1. Variation of TE along the slope and Vf/Vs are shown in Figure 4. As shown in this figure, by increasing the slope and Vf/Vs, the TE decreases. Increasing the slope of main channel leads to an increase in the flow velocity and shear stress. Increasing the flow velocity leads to loss time opportunities of sediment deposition. Increasing the shear stress resulted from increasing the slope of the main channel, in addition to reducing the sediment deposition, sometimes causes the flushing phenomenon. By increasing the concentration of sediment load, the performance of retention dams regarding sediment deposition (TE) as shown in Figure 4, decreases, as well; since, by increasing the sediment concentration, the fall velocity decreases. As shown in Figure 4, by increasing the mean grain size of sediment (D50), TE increases, as well. This finding is repeated for specific gravity (Gs). In other words, by increasing Gs, the performance of retention dams regarding sediment deposition increases. Increasing Gs and D50 leads to an increase in the falling velocity; hence, TE increases. The solid lines in Figure 4 show the trend of TE versus the longitudinal slope and Vf/Vs, considering the mean size of grain of sediment and specific gravity, respectively.

Table 1

Results obtained from laboratory experiences

Run no.SGsRun no.SGs
0.025 1.6 0.0005 1.32 37.235 26 0.045 2.65 0.0002 1.457 96.564 
0.05 1.6 0.0005 1.23 37.688 27 0.06 1.29 0.0005 1.268 80.66 
0.05 1.29 0.0005 1.90 68.939 28 0.025 1.6 0.0005 1.212 36.25 
0.045 1.6 0.0005 1.3 36.755 29 0.05 2.65 0.0002 1.967 90.409 
0.055 1.29 0.0004 1.478 43.786 30 0.04 2.65 0.0002 1.264 90.566 
0.04 1.29 0.0004 1.667 45.184 31 0.04 1.29 0.0004 1.322 58.094 
0.04 1.6 0.0005 1.236 37.855 32 0.06 1.6 0.0003 1.936 98.556 
0.05 1.6 0.0005 1.375 37.821 33 0.025 1.29 0.0004 1.108 71.262 
0.04 2.65 0.0002 1.512 92.346 34 0.045 2.65 0.0002 1.765 97.425 
10 0.055 1.29 0.0005 1.404 59.275 35 0.05 1.29 0.0004 2.083 38.729 
11 0.06 1.29 0.0004 1.667 43.576 36 0.025 2.65 0.0002 1.068 94.654 
12 0.025 2.65 0.0002 1.231 94.666 37 0.05 1.6 0.0005 1.222 37.775 
13 0.045 2.65 0.0002 1.661 92.363 38 0.025 1.6 0.0005 1.196 36.5 
14 0.045 2.65 0.0002 1.562 92.171 39 0.045 2.65 0.0002 1.361 90.412 
15 0.045 2.65 0.0003 1.475 96.878 40 0.05 2.65 0.0002 1.750 90.264 
16 0.04 1.29 0.0005 1.367 77.459 41 0.055 1.29 0.0004 2.112 36.597 
17 0.06 1.29 0.0004 2.341 31.844 0.045 2.65 0.0002 1.325 95.365 
18 0.05 1.29 0.0004 1.448 36.698 43 0.025 1.29 0.0005 0.889 80.557 
19 0.06 2.65 0.0003 1.812 97.777 44 0.025 1.29 0.0004 1.084 77.795 
20 0.025 1.29 0.0005 1.11 81.365 45 0.04 1.6 0.0005 1.238 37.888 
21 0.05 1.29 0.0004 1.999 38.709 46 0.04 1.6 0.0005 1.227 36.72 
22 0.035 1.6 0.0005 1.225 36.95 47 0.025 1.6 0.0005 1.312 37.431 
23 0.04 2.65 0.0002 1.666 92.872 48 0.05 2.65 0.0002 1.448 88.325 
24 0.06 1.29 0.0005 1.329 64.549 49 0.035 1.6 0.0005 1.211 36.55 
25 0.025 2.65 0.0002 1.659 97.665 50 0.03 1.6 0.0005 1.285 36.778 
Run no.SGsRun no.SGs
0.025 1.6 0.0005 1.32 37.235 26 0.045 2.65 0.0002 1.457 96.564 
0.05 1.6 0.0005 1.23 37.688 27 0.06 1.29 0.0005 1.268 80.66 
0.05 1.29 0.0005 1.90 68.939 28 0.025 1.6 0.0005 1.212 36.25 
0.045 1.6 0.0005 1.3 36.755 29 0.05 2.65 0.0002 1.967 90.409 
0.055 1.29 0.0004 1.478 43.786 30 0.04 2.65 0.0002 1.264 90.566 
0.04 1.29 0.0004 1.667 45.184 31 0.04 1.29 0.0004 1.322 58.094 
0.04 1.6 0.0005 1.236 37.855 32 0.06 1.6 0.0003 1.936 98.556 
0.05 1.6 0.0005 1.375 37.821 33 0.025 1.29 0.0004 1.108 71.262 
0.04 2.65 0.0002 1.512 92.346 34 0.045 2.65 0.0002 1.765 97.425 
10 0.055 1.29 0.0005 1.404 59.275 35 0.05 1.29 0.0004 2.083 38.729 
11 0.06 1.29 0.0004 1.667 43.576 36 0.025 2.65 0.0002 1.068 94.654 
12 0.025 2.65 0.0002 1.231 94.666 37 0.05 1.6 0.0005 1.222 37.775 
13 0.045 2.65 0.0002 1.661 92.363 38 0.025 1.6 0.0005 1.196 36.5 
14 0.045 2.65 0.0002 1.562 92.171 39 0.045 2.65 0.0002 1.361 90.412 
15 0.045 2.65 0.0003 1.475 96.878 40 0.05 2.65 0.0002 1.750 90.264 
16 0.04 1.29 0.0005 1.367 77.459 41 0.055 1.29 0.0004 2.112 36.597 
17 0.06 1.29 0.0004 2.341 31.844 0.045 2.65 0.0002 1.325 95.365 
18 0.05 1.29 0.0004 1.448 36.698 43 0.025 1.29 0.0005 0.889 80.557 
19 0.06 2.65 0.0003 1.812 97.777 44 0.025 1.29 0.0004 1.084 77.795 
20 0.025 1.29 0.0005 1.11 81.365 45 0.04 1.6 0.0005 1.238 37.888 
21 0.05 1.29 0.0004 1.999 38.709 46 0.04 1.6 0.0005 1.227 36.72 
22 0.035 1.6 0.0005 1.225 36.95 47 0.025 1.6 0.0005 1.312 37.431 
23 0.04 2.65 0.0002 1.666 92.872 48 0.05 2.65 0.0002 1.448 88.325 
24 0.06 1.29 0.0005 1.329 64.549 49 0.035 1.6 0.0005 1.211 36.55 
25 0.025 2.65 0.0002 1.659 97.665 50 0.03 1.6 0.0005 1.285 36.778 
Figure 4

The variation of TE vs. the slope and Vf/Vs.

Figure 4

The variation of TE vs. the slope and Vf/Vs.

Results of AI models

To develop applied AI models for predicting the TE of retention dams, results obtained from experiments were divided into two groups as training and testing. Training was considered for model calibration and testing for validation. Assigning a dataset to each group was carried out based on the random approach. The main point related to allocating a dataset to each group is the range of dataset, and it is advised that a range of them (training and testing) be close together. After data preparation, which is the first stage of AI modeling, designing the structure of AI models is discussed. The aim of designing AI models is to define their structure, including the number of hidden layer(s), the number of neurons, and equation governing neurons. To design the structure of multilayer perceptron neural network (MLPNN) model, the proposed recommendations by Haghiabi et al. (2017) were considered. They stated that to develop the MLPNN model, at the first stage, one hidden layer that consists of neurons equal to input features is designed. At this stage, the performance of different types of transfer functions is tested. After justifying the transfer function to increase the accuracy of the developed model, increasing the number of hidden layers and/or increasing the number of neurons in each hidden layer may be considered. This approach of development leads to preparing an optimal structure for MLPNN and avoids the increase in computation costs. In this study, the performance of different transfer functions, including tansig, logsig, radbas, and purelin were tested. The optimal achieved structure is shown in Figure 5. As shown in this figure, the developed MLPNN model consisted of two hidden layers, in the first and second hidden layers of which there are five and three neurons, respectively. The best performance of tested transfer function for hidden layer was related to tansig and, for output layer, it was related to purelin. Results of MLPNN model during the training and testing stages are shown in Figure 7. The approach considered for designing the structure of MLPNN model was considered for developing the ANFIS model. The structure of ANFIS model was presented in Table 2. As presented in this table, the Sugeno type was considered for developing the ANFIS model and the weighted average was utilized for defuzzification. The best performance among the tested membership functions was related to gaussmf. Results of ANFIS model in development stages (training and testing) are shown in Figure 7. The approach of developing SVM was the same as MLP and ANFIS. The structure of prepared SVM is shown in Figure 6. To develop the SVM model, different types of kernel functions introduced in the materials and methods section were tested and the best accuracy was related to RBF kernel function. The performance of SVM in calibration and validation stages is shown in Figure 7. Reviewing Figure 7 demonstrated that all applied models have acceptable accuracy in terms of standard error indices including coefficient of determination (R2) and root mean square of error (RMSE). To provide more information about the performance of applied models, another index proposed by (Noori et al. (2011)) was used. This index is called developed discrepancy ratio (DDR) and is defined as Equation (11). Results of DDR in training and testing stages are shown in Figure 8. Evaluating the performance of applied models in terms of DDR index shows that less data dispersivity is related to SVM. This means that results of SVM are more reliable.  
formula
(11)
Table 2

A summary of ANFIS structure

ParameterNMFAnd methodOr methodDefuzz methodAgg methodType
gaussmf prod max wtaver max sugeno 
Gs gaussmf prod max wtaver max 
Vf/Vs gaussmf prod max wtaver max 
D50/(Vf)0.33 gaussmf prod max wtaver max 
ParameterNMFAnd methodOr methodDefuzz methodAgg methodType
gaussmf prod max wtaver max sugeno 
Gs gaussmf prod max wtaver max 
Vf/Vs gaussmf prod max wtaver max 
D50/(Vf)0.33 gaussmf prod max wtaver max 
Figure 5

Structure of developed MLP model.

Figure 5

Structure of developed MLP model.

Figure 6

Structure of developed SVM model.

Figure 6

Structure of developed SVM model.

Figure 7

Results of applied models in training and testing stages.

Figure 7

Results of applied models in training and testing stages.

Figure 8

Results of DDR of applied models in training and testing stages.

Figure 8

Results of DDR of applied models in training and testing stages.

CONCLUSION

Controlling sediment transport in rivers, especially at upstream areas of the river basin, is a rational approach to improve the lifetime of big dams and water engineering projects such as irrigation and drainage networks. Retention dams have been proposed as one of the main hydraulic structures for this purpose. In this paper, the TE of retention dams was investigated using experiments and numerical methods. Experimental results indicated that using retention dams has a high impact on deposition of sediment loads. Hence, using them upstream of rivers, especially in catchments, is strongly recommended for controlling sediment transport. Results of experiments showed that by increasing the longitudinal slope of channel, the TE decreased. This finding was repeated for variation of TE versus the Vf/Vs. These two parameters are vital for the feasibility study of retention dams. The longitudinal slope is derived using the surviving operation and Vf/Vs is a representative of river sediment. In this study, to map the relation between the TE and involved parameters, AI techniques including ANN, ANFIS and SVM were utilized. Results of applied AI models indicated that they have suitable performance for mapping and predicting TE; however, results of SVM were more reliable.

CONFLICT OF INTEREST

No conflict of interest.

REFERENCES

REFERENCES
Araghinejad
,
S.
2013
Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering
.
Springer
,
Houten, The Netherlands
.
Azamathulla
,
H. M.
,
Deo
,
M. C.
&
Deolalikar
,
P. B.
2008
Alternative neural networks to estimate the scour below spillways
.
Advances in Engineering Software
39
,
689
698
.
Azamathulla
,
H. M.
,
Ghani
,
A. A.
&
Zakaria
,
N. A.
2009
ANFIS-based approach to predicting scour location of spillway
.
Proceedings of the ICE-Water Management
162
,
399
407
.
Azamathulla
,
H. M.
,
Cuan
,
Y.
,
Ghani
,
A.
&
Chang
,
C.
2013
Suspended sediment load prediction of river systems: GEP approach
.
Arabian Journal of Geosciences
6
,
3469
3480
.
Azamathulla
,
H. M.
,
Haghiabi
,
A. H.
&
Parsaie
,
A.
2016
Prediction of side weir discharge coefficient by support vector machine technique
.
Water Science and Technology: Water Supply
16
,
1002
1016
.
Baghbanpour
,
S.
&
Kashefipour
,
S. M.
2012
Numerical modeling of suspended sediment transport in rivers (Case study: Karkheh River)
.
JWSS – Isfahan University of Technology
16
,
45
58
.
Cao
,
M.
,
Zhou
,
H.
,
Zhang
,
C.
,
Zhang
,
A.
,
Li
,
H.
&
Yang
,
Y.
2011
Research and application of flood detention modeling for ponds and small reservoirs based on remote sensing data
.
Science China Technological Sciences
54
,
2138
2144
.
Del Giudice
,
G.
,
Rasulo
,
G.
,
Siciliano
,
D.
&
Padulano
,
R.
2014
Combined effects of parallel and series detention basins for flood peak reduction
.
Water Resource Management
28
,
3193
3205
.
Depeweg
,
H.
,
Paudel
,
K. P.
&
Méndez
,
N.
2014
Sediment Transport in Irrigation Canals: A New Approach
.
CRC Press, Balkema
,
The Netherlands
.
Fahmy
,
M. R.
2015
Effect of sediment deposition on the efficiency of Fayoum weir
.
Flow Measurement and Instrumentation
46
(
Part A
),
133
138
.
Fiener
,
P.
,
Auerswald
,
K.
&
Weigand
,
S.
2005
Managing erosion and water quality in agricultural watersheds by small detention ponds
.
Agriculture, Ecosystems & Environment
110
,
132
142
.
Ghani
,
A. A.
&
Azamathulla
,
H. M.
2014
Development of GEP-based functional relationship for sediment transport in tropical rivers
.
Neural Computing and Applications
24
,
271
276
.
Ghani
,
A. A.
,
Azamathulla
,
H. M.
,
Chang
,
C. K.
,
Zakaria
,
N. A.
&
Hasan
,
Z. A.
2011
Prediction of total bed material load for rivers in Malaysia: a case study of Langat, Muda and Kurau Rivers
.
Environmental Fluid Mechanics
11
,
307
318
.
Haghiabi
,
A. H.
,
Azamathulla
,
H. M.
&
Parsaie
,
A.
2017
Prediction of head loss on cascade weir using ANN and SVM ISH
.
Journal of Hydraulic Engineering
23
,
102
110
.
Hassan-Esfahani
,
L.
&
Banihabib
,
M. E.
2016
The impact of slit and detention dams on debris flow control using GSTARS 3.0
.
Environmental Earth Sciences
75
,
1
11
.
Kiat
,
C. C.
,
Ghani
,
A. A.
,
Abdullah
,
R.
&
Zakaria
,
N. A.
2008
Sediment transport modeling for Kulim River – a case study
.
Journal of Hydro-Environment Research
2
,
47
59
.
Liu
,
Q.
,
Qin
,
Y.
,
Zhang
,
Y.
&
Li
,
Z.
2015
A coupled 1D–2D hydrodynamic model for flood simulation in flood detention basin
.
Natural Hazards
75
,
1303
1325
.
Madadi
,
M.
,
Azamathulla
,
H.
&
Yakhkeshi
,
M.
2015
Application of Google earth to investigate the change of flood inundation area due to flood detention dam
.
Earth Science Informatics
8
,
627
638
.
Madadi
,
M. R.
,
Rahimpour
,
M.
&
Qaderi
,
K.
2016
Sediment flushing upstream of large orifices: an experimental study
.
Flow Measurement and Instrumentation
52
,
180
189
.
Moradinejad
,
A.
,
Haghabi
,
A. H.
,
Saneie
,
M.
&
Yonesi
,
H.
2017
Investigating the effect of skimming wall on controlling the sediment entrance at lateral intakes
.
Water Science and Technology: Water Supply
17
(
4
),
1121
1132
.
Nikoo
,
M.
,
Khorramshokouh
,
N.
&
Monghasemi
,
S.
2015
Optimal design of detention rockfill dams using a simulation-based optimization approach with mixed sediment in the flow
.
Water Resources Management
29
(
5
),
5469
5488
.
Noori
,
R.
,
Karbassi
,
A. R.
,
Mehdizadeh
,
H.
,
Vesali-Naseh
,
M.
&
Sabahi
,
M. S.
2011
A framework development for predicting the longitudinal dispersion coefficient in natural streams using an artificial neural network
.
Environmental Progress & Sustainable Energy
30
,
439
449
.
Noori
,
R.
,
Deng
,
Z.
,
Kiaghadi
,
A.
&
Kachoosangi
,
F. T.
2015
How reliable Are ANN, ANFIS, and SVM techniques for predicting longitudinal dispersion coefficient in natural rivers?
Journal of Hydraulic Engineering
142
(
1
).
Parsaie
,
A.
&
Haghiabi
,
A. H.
2016a
Numerical modeling of effect of dead zones on concentration profile of pollution in rivers
.
Water Science and Technology: Water Supply
17
(
3
),
825
834
.
Parsaie
,
A.
&
Haghiabi
,
A. H.
2016b
Prediction of discharge coefficient of side weir using adaptive neuro-fuzzy inference system
.
Sustainable Water Resources Management
2
,
257
264
.
Parsaie
,
A.
,
Dehdar-Behbahani
,
S.
&
Haghiabi
,
A. H.
2016a
Numerical modeling of cavitation on spillway's flip bucket
.
Frontiers of Structural and Civil Engineering
10
(
4
),
438
444
.
Parsaie
,
A.
,
Haghiabi
,
A. H.
,
Saneie
,
M.
&
Torabi
,
H.
2016b
Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS)
.
Frontiers of Structural and Civil Engineering
11
(
1
),
111
122
.
Parsaie
,
A.
,
Yonesi
,
H.
&
Najafian
,
S.
2016c
Prediction of flow discharge in compound open channels using adaptive neuro fuzzy inference system method
.
Flow Measurement and Instrumentation
.
54
,
288
297
.
Parsaie
,
A.
,
Azamathulla
,
H. M.
&
Haghiabi
,
A. H.
2017a
Physical and numerical modeling of performance of detention dams
.
Journal of Hydrology
(in press). doi:10.1016/j.jhydrol.2017.01.018
.
Parsaie
,
A.
,
Najafian
,
S.
,
Omid
,
M. H.
&
Yonesi
,
H.
2017b
Stage discharge prediction in heterogeneous compound open channel roughness
.
ISH Journal of Hydraulic Engineering
23
,
49
56
.
Vorogushyn
,
S.
,
Lindenschmidt
,
K.-E.
,
Kreibich
,
H.
,
Apel
,
H.
&
Merz
,
B.
2012
Analysis of a detention basin impact on dike failure probabilities and flood risk for a channel-dike-floodplain system along the river Elbe, Germany
.
Journal of Hydrology
436–437
,
120
131
.
Yazdi
,
J.
&
Salehi Neyshabouri
,
S. A. A.
2012
Optimal design of flood-control multi-reservoir system on a watershed scale
.
Natural Hazards
63
,
629
646
.
Yazdi
,
J.
&
Salehi Neyshabouri
,
S. A. A.
2014
Adaptive surrogate modeling for optimization of flood control detention dams
.
Environmental Modelling & Software
61
,
106
120
.
Yazdi
,
J.
,
Torshizi
,
A. D.
&
Zahraie
,
B.
2016
Risk based optimal design of detention dams considering uncertain inflows
.
Stochastic Environmental Research and Risk Assessment
30
,
1457
1471
.