Real Time Suspended Sediment Concentration Forecast by Rainfall
Information: Case Study on Managawa River Basin, Japan
Chadin CHUTACHINDAKATE1 and Tetsuya SUMI2
1Doctoral Student, Department of Civil and Earth Resources Engineering, Kyoto
University, Katsura Campus, Nishikyo-ku, Kyoto 615-8540 Japan; PH +81-75-3833182; FAX +81-75-383-3183; email: chadin@mbox.kudpc.kyoto-u.ac.jp
2D. Eng., Professor, Water Resources Research Center, Disaster Prevention ResearchInstitute, Kyoto University, Goka-sho, Uji 611-0011 Japan; PH +81-774-38-4307;
FAX +81-774-38-4040; email: sumi@mbox.kudpc.kyoto-u.ac.jp
ABSTRACTThe sediment flow into the reservoir is a factor for decision support in real timereservoir operation therefore the serious area of sediment erosion of Managawa river
basin, Japan is monitored by suspended sediment gauge. The hourly suspended
sediment concentration at Okumotani station; the upstream of Managawa reservoir,
was monitored and estimated by the artificial neural network (ANN) model that the
input data were rainfall data and its products. This artificial neural network (ANN)was calibrated and validated by using recently suspended sediment data on heavy
rainfall events from December 2006 to January 2008. Choosing an appropriate neuralnetwork structure and providing field data to that network for training purpose are
address by using a constructive back propagation algorithm. Rainfall and its products;
the computed discharge from rainfall runoff model and rainfall intensity, were applied
as inputs to neural network. It is demonstrated that the artificial neural network(ANN) is capable of modeling the hourly suspended sediment concentration withgood accuracy and the neural network model has efficiency more than the multiple
linear regression (MLR) model and the sediment rating curve (SRC) model.
Keywords: Real time forecast, Artificial neural network, Suspended sedimentconcentration, Managawa river basin
INTRODUCTION
Suspended sediment in river; upstream of reservoir, is an important parameter forreservoir management and it is an index for the status of soil erosion and ecologicalenvironment of a catchment. Mathematical models are widely used in studying soil
erosion and sediment transportation which many empirical models and physicalmodels have been developed to model the suspended sediment flux of a catchment.
Hydrologic applications of artificial neural network (ANN) include the modeling ofdaily rainfall-runoff-sediment yield process, snow-rainfall process, assessment of
stream’s ecological and hydrological responses to climate change, rainfall-runoff
forecasting, ground water quality prediction and ground water remediation. Artificial

33rd IAHR Congress: Water Engineering for a Sustainable Environment
c 2009 by International Association of Hydraulic Engineering & Research (IAHR)
Copyright °ISBN: 978-94-90365-01-1

33rd IAHR Congress: Water Engineering for a Sustainable Environment

neural network (ANN) can be applied to predict the monthly, weekly and dailysuspended sediment in the catchment by relating it to average rainfall, temperature,
rainfall intensity and water discharge (Yun-Mei Zhu, 2007). This study is an attempt
to predict an hourly suspended sediment concentration on the river by using backpropagation artificial neural network with rainfall data and its products as inputs of
network. This artificial neural network was applied to simulate an hourly suspended
sediment concentration from December 2006 to January 2008 the upstream ofMangawa reservoir, Fukui prefecture, Japan. The performances of neural network
were compared with that of multiple linear regression (MLR) models and sediment
rating curve (SRC) model.
STUDY AREAManagawa dam constructed during 1965-1977 in Fukui prefecture, Japan is located atlatitude 35º 55' 50" N and longitude 136º 32' 31" E. Managawa dam is a concrete arch
dam with 127.5 m height, 357 m width and 115 MCM capacity designed for flood
control, irrigation and power. Catchment area above the dam is about 223.7 km2 thatthe mean elevation is 830 m above mean sea level and land slope is about 0.45. The
sediment depositing volume in Managawa dam until year 2004 is about 2.8 MCM.
There are two dams, Kumokawa dam and Sasougawa dam, situated on up stream ofManagawa dam (see Figure 1) located on heavy rainfall area as the average annual
rainfall is about 2,391 mm and the rainy season is during May – August. This area
is covered by forest where accounts for 94% area of total watershed. The major soil
types in the study area are sandstone, mudstone and conglomerate. Sediment yieldand turbidity are the serious problems for reservoir management therefore themonitoring of sediment flow into this reservoir is necessary. The serious area of
sediment erosion of Managawa river basin is monitored by the suspended sedimentgauge, Compact-HTW by JFE Alec Co., Ltd., settled on Okumotani river which is an
upstream branch of Managawa river. Okumotani catchment is about 8.01 km2, theaverage land slope is about 0.49.

Figure 1. Suspended Sediment Gauge located at Okumotani catchment431

33rd IAHR Congress: Water Engineering for a Sustainable Environment

METHODOLOGY(1) Artificial neural network (ANN) with back propagation process
Artificial neural network (ANN) can be categorized into feed forward and recurrent
networks according to the direction of the information flow processing. In the current
study, a multilayer perception with logistic sigmoid function as the transfer function
and back propagation algorithm as the training algorithm is used. It consists of an
input layer, a hidden layer and an output layer which the output layer consists only
one neuron as suspended sediment. Neurons on the input layers represent the casualvariables at different time steps. The neuron on the output layer is suspended
sediment concentration. The number of neurons on the hidden layer is decided by trial
and error method. During the training, the input information is processed in theneurons of the input layer and is passed down to the hidden layer through theconnections. Each neuron calculates its net input as the weighted sum of all inputs
and performs a logistic sigmoid transformation to it before passing to the next layer.
When the information processes reach the last layer, the final output is produced andan error indicating the difference between the predicted and observed outputs iscomputed. This error is back propagated through the network to each neuron, and theconnection weights are adjusted correspondingly. The training process is stopped
until the error is smaller than a specified value or when the error starts to rise after
reaching a minimum. The performances of the models were evaluated with root meansquare error (RMSE), efficiency index (EI) and plotting graph.
(2) Multiple linear regression (MLR)
Multiple linear regression (MLR) models were employed to simulate the relationshipbetween the proper input variables and the hourly suspended sediment. The proper
input variables will be defied from the input variables of good performance neuralnetworks. The multiple linear regression (MLR) model is denoted as Eq. 1.
SS i = b0 + b1 xi1 + b2 xi 2 + ... + b j xij(1)
Where SSi is suspended sediment concentration for the i-th pattern, xij is the j-th
independent variable for the i-th pattern, b0 is the regression intercept and the bj is thecoefficient of the j-th independent variable.
(3) Sediment rating curve (SRC)
A rating curve consists of a graph or equation, relating suspended sedimentconcentration to water discharge, which can be used to estimate sediment loads fromthe water discharge record. The sediment rating curve generally represents afunctional relationship of the form as Eq. 2 and it worked on hourly scale.
SS = aQ c(2)
Where SS is suspended sediment concentration, Q is discharge, a and c are coefficient.
(4) Data collection and data analysis
Although suspended sediment concentration at Okumotani station has been measured
once twenty minutes since December 2006, this study applies an hourly suspended
sediment concentration data for training and testing the network. The climate inputs

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were selected based on physical relationships and correlations between the input andoutput variables. Rainfall is the direct driving force of sediment yield andtransportation. Temperature can influence the sediment generation and transportation
through several indirect ways as through its influence on the evapotranspiration,
runoff and reduce decomposition rate. In the beginning of this study, climate
variables as hourly rainfall (R), temperature (T), wind (W), solar (SL) and snow (SN)were considered as inputs. Neural networks were established with these inputs and it
was found that rainfall and temperature were closely related with suspended sedimentconcentration, while adding wind, solar and snow as inputs did not improve theperformance of the networks. Thus wind, solar and snow were not used for analysis.
About the hydrodynamics data as inputs of network, hourly water discharge (Q) andsuspended sediment concentration (SS) data have been measured and available on
this study area. To improve the network, the prospective processed variables will be
added as inputs that in this study rainfall in two hours (A) and the absolute ofdischarge changing (D) are considered as inputs of network. The rainfall in two hours
is accumulated rainfall with rainfall in previous time step (Rt + Rt-1) and the absoluteof discharge changing in this study is the absolute value of different of discharge inthis time step and previous two time step then divided by two or |Qt-Qt-2|/2. Computed
discharge (Qc) flowing into Managawa dam was calculated from rainfall-runoff
model; NAM model, that the hourly data in year 2004 were used for calibrating
model and it’s efficiency is about 90%. The data in year 2002 and 2003 were used forverifying model and the efficiency index is about 83%. Because of the limitation ofdata, this study applied with the heavy rainfall events to evaluate the network which
the sediment yield is serious during heavy rainfall events. For the calibration step, theheavy rainfall events in February 2007, June 2007 and December 2007 wereconsidered and the heavy rainfall events during December 2006, March 2007 andMay 2007 were used as verifying the network. The correlation coefficients are given
in Table 1. It can be seen that the suspended sediment concentration has relative
higher linear correlations with rainfall, rainfall in two hours, discharge and theabsolute of discharge changing.
Table 1. Correlation coefficients between input variables and suspended sediment
Time
t
t-1
t-2

R
0.594
0.583
0.450

T
0.219
0.226
0.235

Q
0.436
0.354
0.280

Qc
0.471
0.361
0.257

A
0.633
-

D
0.486
-

SS
1.000
0.640
0.464

APPLICATIONS AND RESULTS(1) Artificial neural network (ANN)The efficiency index (EI) and root mean square error (RMSE) values of 22 networks
during calibration and verification periods are given in Table 2. The network in caseof discharge (Q) and temperature (T) are inputs, show relatively poor performance ascase 1 which the error is about 50% in verification step. From linear correlation
efficiency, rainfall (R) has good relationship with suspended sediment (SS), therefore
it is necessary to add rainfall as input. The below discussion is first focusing on the433

33rd IAHR Congress: Water Engineering for a Sustainable Environment

values for verification period because the generalization ability of the networks isinterest for application. Adding discharge (Q) as input variable is improving theperformance of network as shown by the efficiency index (EI) during verification
period of case 3 and case 4. And also when compare the efficiency of case 3, rainfall(R) and temperature (T) are inputs, and case 6, rainfall (R) and discharge (Q) are
inputs, found that the efficiency of case 3 is lower than case 6. To improve the
network which rainfall (R) and discharge (Q) are inputs, it should add rainfall in twohours (A) to one of input then the efficiency will rise to 68 % as shown by case 9.
Therefore the network which rainfall (R), rainfall in two hours (A) and discharge (Q)are inputs is good for forecasting suspended sediment concentration as network case
9. Adding rainfall in two hours (A) into input of network makes the efficiency of
network increasing that it was confirmed by the results of network case 14 and case
11 which each network error are about 33.96 % and 36.32 % respectively. If rainfall(R) is not one of input of network as case of discharge (Q) and previous time step
suspended sediment (SS) are inputs, the performance is fare good as shown in case
16. When the absolute of discharge changing (D) is added to network as case 17, theefficiency is a bit improving from case 16. However adding the absolute of dischargechanging (D) when rainfall (R) is one of input, the efficiency does not improve asshown in case 19 and 22. Because the rainfall (R) is more effect to suspended
sediment concentration than the absolute of discharge changing (D).
Table 2. Performances of neural networks
Calibration
EI
RMSE %
1
t
2,8,1
0.431
45.01
T,Q
R,T
2
t
2,6,1
0.509
43.96
3
t,t-1
4,16,1
0.733
32.39
4
t
3,3,1
0.590
40.17
R,T,Q
R,Q
5
t
2,4,1
0.708
33.91
6
t,t-1
4,8,1
0.591
40.09
7
t,t-1,t-2
6,12,1
0.859
23.48
R,A,Q
8
t
3,3,1
0.655
36.87
9
t,t-1
5,3,1
0.648
37.21
10
t,t-1,t-2
7,5,1
0.730
32.60
R,SS
11
t
2,4,1
0.663
36.43
12
t,t-1
4,20,1
0.835
25.43
R,A,SS13t3,20,1
0.830
25.80
14
t,t-1
5,5,1
0.709
33.84
Q,SS
15
t
2,2,1
0.512
43.83
16
t,t-1
4,4,1
0.579
40.70
Q,D,SS
17
t
3,3,1
0.572
41.04
18
t,t-1
5,5,1
0.607
39.33
R,Q,SS19t3,12,1
0.892
20.58
20
t,t-1
6,6,1
0.789
28.78
R,Q,D,SS
21
t4,16,1
0.868
22.78
22
t,t-1
7,18,1
0.796
28.27
For SS at time t, it means SSt-1 and for SS at time step t-1, it means SSt-2
Input

Case No.

Time Step

Network

434

Verification
EI
RMSE %
0.182
50.09
0.409
44.18
0.387
43.35
0.545
37.33
0.509
38.79
0.554
36.97
0.449
41.12
0.392
43.19
0.676
31.51
0.611
34.52
0.570
36.32
0.562
36.62
0.568
36.37
0.624
33.96
0.497
39.26
0.503
39.05
0.541
37.53
0.328
45.38
0.553
37.02
0.376
43.75
0.487
39.65
0.544
37.37

33rd IAHR Congress: Water Engineering for a Sustainable Environment(2) Multiple linear regression (MLR) and sediment rating curve (SRC)
The multiple linear regression (MLR) models are necessary to define the proper
variables which should relate with suspended sediment therefore this study applied
the set of proper variables in regression model same as inputs of neural networkmodels. Thus there are three cases; case 13, case 19 and case 21, were considered toapply with multiple linear regression (MLR) models as in Eq. 1. The units of eachvariable are followed; rainfall (mm/hr), discharge (cu.m/s), rainfall in two hours(mm/2hrs), the absolute of discharge changing (no unit) and suspended sediment(ppm). Multiple linear regression (MLR) model No.23; rainfall, rainfall in two hours
and suspended sediment are variables, is shown in Eq. 3. Multiple linear regression(MLR) model No.24; rainfall, discharge and suspended sediment are variables, isshown in Eq. 4 and multiple linear regression (MLR) model No.25; rainfall,discharge, the absolute of discharge changing and suspended sediment are variables,
is shown in Eq. 5.
SS t = 16.28 Rt − 1.29 At + 0.52 SS t −1 + 0.93
(3)
SS t = 14.54 Rt + 0.61Qt + 0.42 SS t −1 − 6.73
(4)
SS t = 14.10 Rt + 0.52Qt + 1.66 Dt + 0.40 SS t −1 − 6.76
(5)Where SS is suspended sediment concentration, R is rainfall, A is rainfall in twohours, Q is discharge and D is the absolute of discharge changing.
The sediment rating curve (SRC) or power relation (PR) model is designed to study
the direct relationship between water discharge and sediment flow as Eq. 2. The
sediment rating curve (SRC) model was calibrated by the same data as neuralnetwork model and the relationship is shown in Eq. 6.
SS = 0.4853Q 1.1377(6)
Where SS is suspended sediment concentration and Q is discharge.
DISCUSSION
When the performances of all neural networks in this study are considered both
calibration and verification periods, it found that the best three networks are case 13,
19 and 21. The efficiency as shown in Table 3 of these neural networks is about 75%
Table 3. Performances of models
Model
ANN

MLR
SRC
ANN

Input

Case No.

R, A, SSt-1
R, Q, SSt-1
R, Q, D, SSt-1
R, A, SSt-1
R, Q, SSt-1
R, Q, D, SSt-1
Q
R, Qc, SSt-1
R, Qc, D, SSt-1

13
19
21
23
24
25
26
27
28

435

EI
0.754
0.782
0.746
0.638
0.654
0.657
0.118
0.669
0.613

All data
RMSE %
30.88
29.08
31.43
37.47
36.26
36.48
55.52
35.86
38.75

33rd IAHR Congress: Water Engineering for a Sustainable Environment

1000

1000

AN N case 19 (R ,Q ,SS)

800
600
400
200
0

600
200

1281
1361
1441
1521

881
961
1041
1121
1201

0
1

H ours in serialorder

Observed
Computed

400

641
721
801

200
0

M LR case 25 (R ,Q ,D ,S S )

800

481
561

Observed
Computed

600
400

81
161
241
321
401

AN N case 21 (R ,Q ,D ,SS)

H ours in serialorder

1
81
161
241
321
401
481
561
641
721
801
881
961
1041
1121
1201
1281
1361
1441
1521

Suspended Sedim ent(ppm )

Hours in serialorder

1000
800

Observed
Computed

1
81
161
241
321
401
481
561
641
721
801
881
961
1041
1121
1201
1281
1361
1441
1521

Observed
Computed

Suspended Sedim ent(ppm )AN N case 13 (R ,A,SS )Suspended Sedim ent(ppm )

1000
800
600
400
200
0

1
81
161
241
321
401
481
561
641
721
801
881
961
1041
1121
1201
1281
1361
1441
1521

Suspended Sedeim nt(ppm )

when all data; both calibration and verification periods, are considered and the root
mean square error is about 30%. When measured discharge was replaced bycomputed one as input of network, the efficiency of network reduces. The graphs ofcomparison between observed and computed suspended sediment of each case areshown in Figure 2. The computed time to peak of all three neural networks is almost
equal to the observed one but some computed peak values of network case 19 are
more over than the observed data. For network case 13, it is the most closed to theobserved one. The network case 13 is the best for hourly suspended sedimentprediction on Okumotani catchment. However all three neural networks are good
acceptable for hourly suspended sediment prediction.

H ours in serialorderFigure 2. Comparisons between the observed and computed suspended sedimentconcentration of each model; case 13, 19, 21 and 25 respectively
The multiple linear regression models in this study are also good performance but its
efficiency is a bit less than the neural network models about 10% as shown in Table
3. The multiple linear regression model case 25 has the highest efficiency among
three regression model. Although the computed peak value of the multiple linear
regression model is always less than the observed data but computed time to peak is
fit with observed one, the regression models can be used for hourly suspended
sediment prediction. For the sediment rating curve model, the performance is poor
because the sediment yield and transport process in this study area are hardly
depended on rainfall as shown by correlation coefficient and the coefficient of rainfall
variable in multiple linear regression equations. Therefore sediment rating curvemodel is not suitable for hourly suspended sediment prediction.
CONCLUSIONSThis study is an attempt to predict hourly suspended sediment concentration at
Okumotani station, the upstream of Managawa reservoir, by using the application of
the artificial neural network (ANN), the multiple linear regression (MLR) and thesediment rating curve (SRC). The neural networks were trained and tested by the
recently data at Okumotani catchment that the appropriate variables were rainfall,water discharge, rainfall in two hours, the absolute of discharge changing and436

33rd IAHR Congress: Water Engineering for a Sustainable Environment

previous time step of suspended sediment concentration. It is demonstrated that theartificial neural network (ANN) is capable of modeling the hourly suspended
sediment concentration with good accuracy when the appropriate variables were
inputs of neural network. Rainfall and it’s accumulated with previous time step are
the important factors to relate with suspended sediment prediction model. Only
rainfall and discharge data are used for inputs of neural network, it makes theaccuracy of network is quite good and to improve this network the rainfall in twohours should be added to input of network. These models can be used for real time
prediction when the previous time step of suspended sediment data is known and theprevious time step of suspended sediment concentration makes the efficiency of
network increase. Artificial neural network (ANN) can generate a better fit to theobserved suspended sediment concentration than the multiple linear regression(MLR) model. However this study found that the multiple linear regression models
can be used to predict hourly suspended sediment when appropriate variables wereparameters of models. The sediment rating curve (SRC) model approach gives worse
estimates of suspended sediment concentration than the neural network and multiple
linear regression models. In order to improve and confirm these hourly suspended
sediment concentration prediction models for upstream of Managawa dam, theseasonal effect, climate changing and land surface changing should be considered as
one of parameters of models and the duration of training and testing the model should
be extended.
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