Advances in River Sediment Research – Fukuoka et al. (eds)© 2013 Taylor & Francis Group, London, ISBN 978-1-138-00062-9

Real-time sediment inflow prediction for sediment bypass operation
at Miwa Dam in Japan
D. Nohara & T. Sumi
Disaster Prevention Research Institute, Kyoto University, Uji, Japan

S.A. Kantoush
Civil Engineering Department, German University in Cairo, New Cairo City, Egypt

ABSTRACT: Reservoir sedimentation has been an important issue at dams in the world. Evacuating
sediments with sediment bypass tunnels, which allow sediments from upstream of reservoirs to passthrough the reservoirs without being captured by the reservoirs, is expected to be an effective countermeasures against the issue. In this paper, real-time prediction models of sediment inflow are developedfor efficient operation of a sediment bypass tunnel at Miwa Dam in the Mibu River basin in Japan.
Two kinds of prediction models, namely a Multivariable Linear Regression (MLR) model and Artificial
Neural Network (ANN) models, are respectively developed in order to predict sediment inflow for the
coming three hours with hourly time resolution. Through application of developed prediction models, it
was demonstrated that the ANN model showed better performance than MLR model, especially in termsof time series predictability.

1

INTRODUCTION

Reservoir sedimentation has been an important
issue at dams in the world since it decreases theavailable capacity of the reservoirs and consequently brings decrease in the efficiency of the reservoirs. Effective sediment management is therefore
needed for sustainable use of reservoirs.
As one of the countermeasures against reservoir
sedimentation, sediment bypasses, which allow
sediments from the upstream to pass through thedownstream of reservoirs by diverting them withbypass tunnels during the flooding situation, havebeen installed at some reservoirs. This measure isconsidered as one of the effective measures to overcome the reservoir sedimentation issues, because
it can pass through without letting them enter
the reservoir and therefore has a potential to be apermanent solution for sediment management atreservoirs. It is, however, needed for reservoir managers to have foresight into the future sedimentinflow in order to operate the sediment bypass
tunnels effectively and adequately. Real-time prediction of the time series of sediment inflow to thereservoir is therefore important to decide when andhow long the bypass tunnels to be operated.
Studies on estimation of sediment amount orconcentration in the river water have been increasing in this decade. Nagy et al. (2002) developed
Artificial Neural Network (ANN) models for

estimation of sediment load concentration in rivers
with physical parameters related to river flows orriver channels. Cigizoglu & Alp (2006) developed
two kinds of ANNs, namely, feed forward backpropagation and generalized regression neural networks in order to model daily river sediment yield.
Cobaner et al. (2009) developed estimating modelsof daily Suspended Sediment Concentration (SSC)by employing neural network techniques as wellas conventional Sediment Rating Curves (SRC),
and demonstrated advantages of neural networkmodels for the estimation of SSC. Rajee et al.
(2009) developed ANN and neuro-fuzzy models
for simulation of daily SSC, and concluded thatthese models could be suitable substitutes for theconventional linear models such as Multi-Linear
Regression (MLR) or SRC methods. These studies
mainly focused on estimating sediment concentration or amount at daily basis.
On the other hand, reservoir operations areoften conducted with shorter time step such ashourly basis during flooding situation especiallyat reservoirs which have small mountainous catchment areas where flood water runoffs quickly tothe reservoirs. Sediment amount transported byflood waters of the rivers also changes quickly as
flood situation changes. Thus prediction of sediment discharge from the upstream with shortertime resolution is needed for effective operation of
sediment bypass tunnels so as to pass the sediments

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through the dams when more amount of sedimentis discharged. A few studies have, however, beenreported that focused on estimation of sedimentyield with short time resolution (Rai & Mathur,
2008; Chutachindakate & Sumi, 2009) as it is morechallenging to develop such models in terms ofdata availability and complexity in variability of
sediment yield.
From the point of view described above, shortterm prediction models of sediment inflow to areservoir during flood events are developed in thisstudy for effective decision making in the operation of a bypass tunnel installed into the reservoir.
Two types of prediction models, namely, MLR
and ANN models, are developed here in order topredict sediment inflow for the coming three hours
with one hour resolution considering hydrologicalstates such as rainfall or water inflow. The models
are developed for Miwa Dam in the Mibu River,Central Japan, which has a precipitous catchment area and equips a sediment bypass tunnel
to discharge a huge amount of sediment from the
upstream during flood events.
2

STUDY AREA

Miwa Dam is a multi-purpose dam for flood
control, power generation and irrigation with
29,952,000 m3 of total storage capacity, and islocated in the Mibu River, a tributary of the Tenryu
River in Central Japan. The catchment area of
Miwa Dam lies in the mountainous area around
Itoigawa-Shizuoka Tectonic Line and receives
approximately 2,000 mm of annual precipitation,
which causes intensive sediment yield to the riverand subsequently to the dam reservoir. In orderto mitigate sediment deposition in the reservoir, asediment bypass tunnel has been installed to Miwa
Dam since 2005 to divert inflow water containing
a large quantity of sediment and to discharge them
directly to the downstream of the dam without
letting the sediment come into the reservoir during major flooding events. The bypass tunnel isdesigned to be operated when inflow water amountis expected to exceed 300 m3/s so that it can divert
flood water with a large amount of sediment
effectively while it can store river water with lessamount of sediment for the purposes other thanflood control.
On the other hand, Miwa Dam has the small
catchment which surface area is 311.1 km2 withsteep geography in the mountainous region. Waterand sediment are therefore discharged quickly andcause rapid increase in water and sediment inflows
at the reservoir within several hours after heavy
rainfall has started. Due to these characteristics,
short-term prediction of sediment inflow at thereservoir with short time resolution like one hour
unit is crucially important for the effective operation of the sediment bypass tunnel at Miwa Dam
during flood events.
3

METHODOLOGY

3.1 Outline of methodology
Prediction models of hourly sediment inflow
for the coming three hours are developed in thisstudy in order to support real-time decision makings for operation of the sediment bypass tunnel.
Hourly SSC of the inflowing water to the reservoir
is employed as the predicted variable for sedimentinflow.
Candidates for predictors are selected fromamong variables observed at Miwa Dam including
rainfall in the upstream, inflow and SSC at earlier
hours according to results of correlation analysis
respectively conducted between them and SSC attime to be predicted. Two prediction models, which
are based on MLR and ANN techniques respectively, are then developed employing variables
strongly correlated with SSC to be predicted as
result of the correlation analysis.
Although Miwa dam has observed a number offlood events after the bypass tunnel for sedimentpassing was installed, hourly SSC had been continuously monitored through one entire flood event
since before increase in inflow water only in several
events, as inflow water normally increases rapidly
after rainfall and therefore makes preparation formonitoring difficult. The correlation analysis anddevelopment of MLR and ANN models must be
conducted paying attention to the situation thatthe number of data does not seem to be fully
enough much for statistics and model developmentin this study.
3.2 Correlation analysis
Correlation analysis between SSC and hydrological observation at the reservoir basin is conducted
to find potential candidates for predictors of SSC.
Correlation with time lags more than three hours
was analyzed here so as to develop a prediction
model of SSC which can estimate SSC before threehours according to the equation described below:l ( k, l ) m xy ( k , l )m x m y (k )(1)where l(k, l)  correlation between SSC and hydrological variable k with time lag of l; mx  standard
deviation of SSC; my  standard deviation ofhydrological variable k; and mxy(k, l) is covariance1212

between SSC and hydrological variable k describedas follows:m xy ( k , l )£ x ( k , i l )  y( i )(2)iwhere x(k, i l)  ith sample of hydrological variable k with consideration of time lag l and y(i)  ithsample of SSC in inflow water.
Four hydrological variables, namely hourly
water inflow at the current time t (q(t)), hourly
rainfall (r(t)), and accumulated rainfall amounts
over the past two and three hours (r2(t) and r3(t)respectively), which can be respectively considered
to have a relationship with SSC in inflowing waterat the reservoir, are considered in the correlation
analysis with SSC in inflowing water. Hydrologicalvariables strongly correlated with SSC of inflow
water at the reservoir are considered as candidates
of predictors when the prediction models for SSC
are developed.
3.3

Multi-variable Linear Regression model(MLR)

A MLR model for prediction of SSC in inflowing
water is described as follows.
y(t )  a0

£ ak  x( k, t )(t )Figure 1.

A feedforward ANN model with three layers.

(called as hidden layer hereafter), and the set of
output values are obtained from the last layer(called as the output layer hereafter). Input andoutput values of the units in layer h are respectively
calculated as following equations from the output
values of the precedent layer (layer h 1):umh£ w hjm ,hohj 1(4)f (umh

(5)

j

(3)

k

where a0  constant term; ak  coefficient for variable k which are selected as predictors from result ofcorrelation analysis with SSC of water inflow; anda  error term for estimation of prediction modelof SSC in the inflow water.
3.4

Artificial Neural Networks (ANN)An ANN is a network model which is inspired by
the functioning of the brain and biological nervous systems (Tokar & Markus, 2000). Becauseof its capability for modeling non-linear relationships, ANNs have been employed for modelinghydrological processes (e.g. French et al., 1992;
Dawson & Wilby, 1998; Nagy et al., 2002; Nohara
et al., 2006). Although various types of ANNs havebeen proposed including layer typed networks andmutually connected ones, a standard multi-layer
feedforward model (see Fig. 1) with backpropagation training is employed as an ANN model in thisstudy.
In this multi-layer ANN model, input values tothe first layer (called as the input layer hereafter)
are transformed according to the weight value ofthe connection between units of the two adjacentlayers and conversion function of units (called as
response function of units) in each middle layer

omh

h
m)

where umh  input value to unit i in layer h;w hjm ,h  weight parameter of the connection
between unit j in layer h 1 and unit m in layer h;h 1hm and oi are respectively output of unit j in layer
h 1 and one of unit m in layer h; k mh  offset ofunit m in layer h. f() is response function of theunit in the hidden layers, which is defined as sigmoid function described as below in this study:f x) 

1
1 exp(( 2 x uo )(6)

where uo  coefficient. Adjustment of weightparameters of connection between units, whichis called as training of the network, is iteratively
conducted so as to minimize the value described asfollows according to backpropagation algorithm:n£ E p  £ (Tpn Opn )2/2
p

p

(7)

n

where Opn  output of unit n in the output layer
for training pattern p; Tpn  desired output signal
which is also called as supervisory signal and normally derived from observed data to be estimated1213

for unit n for training pattern p; Ep  integrated
error of estimation for desired output values fortraining pattern p; and En  total training error for
all training patterns. The network is adjusted so as
to be capable to output good estimation from data
used for training through the processes describedabove.
4
4.1

CASE STUDY
Correlation analysis and estimationof candidates for predictors

Correlation analysis between SSC of inflow waterto Miwa Reservoir and hydrological variables
was conducted. Three flood events from 2006 to
2007shown in Table 1 in which SSC was successfully
monitored were considered in correlation analysis.
As mentioned in Chapter 3, four hydrological variables including hourly water inflow at the current
time t (q(t)), hourly rainfall (r(t)), and accumulated
rainfall amounts over the past two and three hours(r2(t) and r3(t) respectively) were considered as mentioned inChapter 3, and three time lags from three
to five hours were also considered in the correlation analysis. Results of the correlation analysis are
summarized in Table 2. As we can see in Table 2,

Table 1. Three flood events which observational data
was used for correlation analysis.
EventnumberDate

Peak inflow(m3/s)

Peak SSC(mg/l)

1
2
3

July 19th, 2006
July 15th, 2007
Sep. 6th, 2007

366.2
165.1
565.3

16,9003610
25,000

Table 2.

Results of correlation analysis.

Hydrological variable

Correlation with SSC (t)

q(t
q(t
q(t
r(t
r(t
r(t
r2(t
r2(t
r2(t
r3(t
r3(t
r3(t

0.80*
0.75
0.72
0.49
0.55
0.54
0.57
0.60
0.55
0.61*
0.60
0.56

3)
4)
5)
3)
4)
5)
3)
4)
5)
3)
4)
5)

* Employed for predictors in the prediction models.

water inflow before three hours (q(t 3)) showed
the strongest correlation (0.80) with the current
SSC SSC(t) in all variables related to water inflow.
On the other hand, accumulated rainfall for threehours from before three hours (r3(t 3)) showed thestrongest correlation (0.61) with SSC(t) in all variables related to averaged rainfall in the catchment
area of the reservoir. These results are broadly consistent with the physical characteristics of the studied catchment area where rainfall water empiricallyrunoffs to the reservoir within several hours after
the soils are saturated with continuous rainfall.
Taking it into consideration that we are developing prediction models of SSC with not much data,
more simple combination of variables is considered
to be better for stability of the prediction for new
data. Moreover, variables which strongly correlate
each other should not be employed at once for astatistical prediction model like a regression model
because it will cause multicollinearity and degrade
predictability for new data. From these points ofview, q(t 3), which showed the strongest correlation with SSC(t), as well as r3(t 3) which also
showed the strongest correlation with SSC(t) inthe variables related to catchment rainfall and had
weak correlation of 0.28 with q(t 3) were considered for a candidate of the predictors of SSC indevelopment of MLR and ANN models.
4.2 Development of MLR model
A MLR model for prediction of SSC after threehours was developed with the predictors of inflow
water at the current hour t and catchment rainfall
accumulated for three hours from before three hours
up to the current hour t based on the results of correlation analysis conducted in the previous section.
Data from three flood events shown in Table 1 wasalso used for the development of the MLR model.
The estimated MLR model for the prediction of
SSC (t 3) (g/l) is described as the following equation with two predictors q(t) (m3/s) and r3(t) (mm).
SSC (t

) 11.

q(t )

.0 r3 (t )(8)

The developed regression model was applied to
predict SSC for three hours ahead for a flood
event observed in July in 2010 (see also Event 7 inTable 4). The time series of prediction is shown in
Figure 2. It can be seen in Figure 2 that two peaksof SSC were predicted comparatively well while
the MLR model overestimated SSC in the otherparts of the time series. This is considered because
the MLR model was developed using data mainly
around peaks of SSC in the inflowing water of thethree flood events used for the development of themodel, and the model was not good at predicting
comparatively low SSC condition.

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Table 3. Input variables (predictors) of the developed
ANN models.

Figure 2. Time series of the prediction result for
the flood event in July in 2010 by the developed MLR
model.

4.3

Development of ANN models

ANN models were also developed based on the
results of correlation analysis between SSC andhydrological variables of the reservoir. Three layer
feedforward ANN model was employed here, andhourly SSC for the coming three hours (i.e. SSC(t 1), SSC (t 2) and SSC (t 3)) was employed
as output variables (i.e. predictands) by the ANN
models. Three ANN models which respectively
have different combinations of input variables (i.e.
predictors) were developed here with consideration of the results of the correlation analysis also
employing SS(t) for consistence in time series ofthe prediction. The combinations of the input variables for ANNs are shown in Table 3.
The ANN models were respectively trained withdata from six flood events from Event 1 through6 shown in Table 4. Although data from only threeflood events was used for development of MLR
model in the previous section, six flood events
including those of comparatively small flood
events were considered for development of the
ANN models, as ANN models have more parameters to be estimated and therefore seem to needmore data for the estimation. The number of units
in the hidden layer was set to five for each ANN
model so as to minimize the number of units without decreasing in capability to model nonlinear
input/output relationships with trial and error. The
numbers of training of the networks were respectively set to 3,000. Figure 3 illustrates time series
estimated by an ANN model for training data
produced from the six flood events’ data after the
network was trained. The result for ANN model 3
is shown in Figure 3 as an example. It can be seenthat estimated values by the ANN model generally
agree with observed values.
After the training of the ANN models, prediction was also conducted for flood event data in
July in 2010 (see also Event 7 in Table 4) by each

ANN
model

Number of
input variables

Input variables(predictors)

1

5

2
3

3
3

r(t 2), r(t 1), r(t),q(t), SSC(t)r3(t 1), q(t), SSC(t)r3(t), q(t), SSC(t)Table 4. Six flood events which observational data
was used for development of ANN models (Events 1
through 6) and one flood event for prediction as validation (Event 7).
Event
number Date

Peak inflow Peak SSC(m3/s)(mg/l)

1*
2*
3*
4
5
6
7

366.2
165.1
565.3
132.8
78.1
102.8
228.2

July 19th, 2006
July 15th, 2007
Sep. 6th, 2007
Oct. 8th, 2004
July 5th, 2005
May 25th, 2007
July 11th–16th, 2010

16,9003610
25,000
1990
2610
3990
5160

* These events were also used for development of the
MLR model.

Figure 3. Estimation results of time series produced
from data of six flood events for training by ANN model
3 after the model was trained.

developed ANN model. The results of predictions
by three ANN models are summarized in Tables 5through 7.
Root Means Square Errors (RMSEs) of predictions by the ANN models are shown in Table 5.
ANN model 1 (described as ANN1 hereafter),which employed hourly rainfalls as input variables, predicted SSC for the coming hour with less
RMSE value compared with the other two models,
while ANN model 2 (described as ANN2 hereafter) and ANN model 3 (also described as ANN3
hereafter), both of which employed accumulated

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rainfall as input variables, performed less RMSE
values compared with ANN1 in the predictions of
SSCs two and three hours ahead.
On the other hand, correlations between observations and prediction by the ANN models forthe flood event are shown in Table 6. It can also
be seen that ANN1 showed better correlationin the prediction of SSC for one hour and twohours ahead, although ANN2 and ANN3 showedstronger correlation in the prediction for SSC threehours ahead.
From the point of view that prediction accuracy
in the peak concentration of suspended sedimentin the inflow water is also important for the operation of the sediment bypass tunnel, percentages oferrors of the predictions by ANN models to observations for the peak values of SSC are also summarized in Table 7. It can be seen in Table 7 that theabsolute value of prediction error of ANN1 wassmaller than those of ANN2 and ANN3 in theprediction of SSC for the next hour, while ANN3
respectively showed the smallest absolute errors in
Table 5. RMSEs of predictions for Flood Event 7 by
ANN models.
Root mean square error (g/l)
ANN
model

SSC (t

1
2
3

629.4
663.5
690.8

1)

SSC (t

2)

805.5
740.0
721.1

SSC (t

3)

960.4
860.7
743.8

4.4

Table 6. Correlations between observations and predictions by ANN models for Flood Event 7.
Correlation
ANN
model

SSC (t

1
2
3

0.947
0.918
0.928

1)

SSC (t

2)

0.902
0.898
0.882

SSC (tthe three models in the prediction of SSC for two
and three hours ahead.
These results suggest that accumulated rainfall
for three hours in the near past can be good predictor for the prediction of future SSC like those twoor three hours ahead as it was seen in the results ofthe correlation analysis, while recent hourly rainfall is encouraged to be considered in the prediction model for SSC in the coming hour at Miwa
Dam. From the viewpoint that predictability ofthe peak of SSC from the early stage is considered
important for successful operation of bypass tunnels, ANN3 with input of rainfall accumulated forthe past three hours is considered effective as theprediction model in the three models studied inthis paper.
Time series of predicted SSC by ANN3 areshown in Figures 4 through 6. Large errors were
illustrated in the low SSC periods with any timelag. This can be considered because the prediction
models were arranged so as to minimize sum ofsquare error described as Equation (7) which considered important to give small errors during high
SSC period in which the square error can easily
become greater, and period when small values wereobserved were not considered well in the model fitting. On the other hand, large peaks of SSC werepredicted with comparatively good accuracy while
the accuracy of the prediction degraded as the leadtime became longer.

3)

0.851
0.892
0.888

Comparison of MLR and ANN models

Comparing results of the prediction of SSC forthree hours ahead by the MLR and ANN3 which
are respectively illustrated in Figure 2 and Figure 6,
it can be seen that ANN3 showed better performance in the low SSC period. ANN3 was especiallyless sensitive against small peak of SSC which canbe seen in the earlier period of the flood event(Flood Event 7), and closer to the observed values.
Similar characteristics can be seen after the second
large peak of SSC, where ANN3 predicted a small
peak while MLR predicted the larger peak. On the

Table 7. Percentage of errors of predictions by ANN
models to observations for the peak values of SSC for
Flood Event 7.
Percentage of errors for the peak values (%)
ANN
model

SSC (t

1
2
3

5.5
10.2
16.4

1)

SSC (t
4.8
16.2
1.0

2)

SSC (t
12.8
11.1
1.7

3)

Figure 4. Time series of predicted SSC for the coming
hour (SSC(t 1)) by ANN3 for Flood Event 7.

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Figure 5. Time series of predicted SSC two hours ahead(SSC(t 2)) by ANN3 for Flood Event 7.

three hours were respectively selected for the candidates of the predictors of SSC in inflow water
three hours ahead. These results of the correlation
analysis were effectively considered in the development of the prediction models to determine
variables for predictors. Through application ofdeveloped prediction models, it was demonstrated
that accumulated rainfall for the recent several
hours can be considered as an important predictor
for the prediction of SSC in the near future. It wasalso illustrated that ANN3 model, which employedcatchment rainfall accumulated for the past threehours as a predictor, can be considered to show the
better performance in terms of predictability intime series tendency, while more study with more
observational data is considered necessary formore reliable conclusion.
REFERENCES

Figure 6. Time series of predicted SSC three hours
ahead (SSC(t 3)) by ANN3 for Flood Event 7.

other hand, there was no significant difference inthe predictability for the large peak of SSC in thetwo models. Synthesizing the results mentioned
above, ANN3 model can be considered to show
the better performance in terms of predictability intime series tendency, while there are still problems
to be tackled in the performance of the prediction
models especially in low SSC periods and for thesmall peak of SSC. Further monitoring of SSC or
turbidity in the inflowing water will improve themodels’ performance by adding data for development of the models.
5

CONCLUSION

Real-time prediction models of sediment inflow
were developed for efficient operation of the sediment bypass tunnel at Miwa Dam the Mibu Riverbasin in Japan. Two kinds of prediction models,
namely a MLR model and three ANN models, were developed in order to predict sedimentinflow for the coming three hours with hourly
time resolution. As a result of correlation analysis between SSC in inflow water and hydrologicalvariables observed in the reservoir basin, amountof inflow water to the reservoir at the current timeand catchment rainfall accumulated for the past

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