Using Artificial Neural Network to Predict Suspended Sediment Concentration
with Hydrology and Hydrodynamics Data: Case Study on Managawa RiverBasin, JapanChadin 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., Associate Professor, Department of Civil and Earth Resources Engineering,Kyoto University, Katsura Campus Nishikyo-ku Kyoto 615-8540 Japan; PH +81-75383-3182; FAX +81-75-383-3183; email: sumi@mbox.kudpc.kyoto-u.ac.jp
Abstract
Artificial neural network (ANN) is used to estimate the hourly suspended sedimentconcentration in the upstream of Managawa dam, Fukui prefecture, Japan. The
sediment yield from upstream of the river was dropped in to the river then thesediment will be transported to deposit into dam. The serious area of sediment erosionof Managawa river basin is monitored by suspended sediment gauge. The hourly
suspended sediment concentration was related to the rainfall, temperature, water
discharge and the absolute of discharge changing. This artificial neural network(ANN) was calibrated and validated by using the recently suspended sedimentconcentration data from December 2006 to March 2007. Choosing an appropriate
neural network structure and providing field data to that network for training purpose
are address by using a constructive back propagation algorithm. Without the previous
step of suspended sediment data as input the outputs from network were fairly good
agreement with observed data. However, it is demonstrated that the artificial neuralnetwork (ANN) is capable of modeling the hourly suspended sediment concentration
with good accuracy when proper variables, their previous time step on the suspended
sediment concentration and the absolute of discharge changing are used as inputs ofnetwork.
Keywords: Artificial neural network, Suspended sediment concentration, Managawa
river basin
1. Introduction
Suspended sediment in river is an important parameter for reservoir management andit is an index for the status of soil erosion and ecological environment of a catchment.
Mathematical models are widely used in studying soil erosion and sedimenttransportation which many empirical models and physical models have beendeveloped to model the suspended sediment flux of a catchment. Artificial NeuralNetwork (ANN) is a type of empirical model. It is derived from the researches on thenature of the human brain (Muller et al., 1995). Hydrologic applications of Artificial
Neural Network (ANN) include the modeling of daily rainfall-runoff-sediment yieldprocess, snow-rainfall process, assessment of stream’s ecological and hydrologicalresponses to climate change, rainfall-runoff forecasting, ground water quality
prediction and ground water remediation. Artificial Neural Network (ANN) can beapplied to predict the monthly, weekly and daily suspended sediment in thecatchment 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 back propagation artificial
neural network.
2. Study area
Managawa Dam constructed during 1965-1977 in Fukui prefecture, Japan is locatedat latitude 35º 55' 50" N and longitude 136º 32' 31" E. Managawa Dam is a concretearch dam with 127.5 m height, 357 m width and 115 MCM capacity designed forirrigation, water supply and power generation where Managawa river is a tributary ofKuzurui river. Catchment area above the dam is about 223.7 km2 that the meanelevation is 830 m above mean sea level and land slope is about 0.45. The sedimentdepositing volume in Managawa dam until year 2004 is about 2.8 MCM. There are
Kumokawa Dam and Sasougawa Dam, situated on up stream of Managawa Dam
located on heavy rainfall area as the average annual rainfall is 2,391 mm. The area iscovered by forest where accounts for 94% area of total watershed. The major soiltypes in the study area are sandstone, mudstone and conglomerate (Managawa Damoffice, 2005). Sediment yield and turbidity are the serious problems for reservoir
management therefore the monitoring of sediment flow into this reservoir isnecessary. The suspended sediment gauge is settled on Okumotani River which it isan upstream branch of Managawa River. Okumotani catchment as shown in Figure 1
is about 8.01 km2, the average slope is 0.49 and the annual sediment yield calculated
by Modified Universal Soil Loss Equation (MUSLE) is about 3,307 m3 resulted byChadin and Tetsuya (2007).

Figure 1. Okumotani catchment
3. Methodology
3.1 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 aninput layer, a hidden layer and an output layer. Neurons on the input layers represent
the casual variables at different time steps. The neuron on the output layer issuspended sediment concentration. The number of neurons on the hidden layer isdecided by trial and error method. Each neuron on the hidden layer is connected withall neurons in the adjacent layers. During the training, the input information isprocessed in the neurons of the input layer and is passed down to the hidden layer
through the connections. Each neuron calculates its net input as the weighted sum of
all inputs and performs a logistic sigmoid transformation to it before passing to thenext layer. When the information processes reach the last layer, the final output isproduced and an error indicating the difference between the predicted and observed
outputs is computed. This error is back propagated through the network to eachneuron, and the connection 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 wereevaluated with two statistical parameters, root mean square error (RMSE) andcoefficient of determination (R2).
3.2 Data collection and data analysis
However 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 the network. The climate, hydrologic andhydraulic data in this catchment indicate a relationship among rainfall, temperature,
water discharge and sediment load during the considered period that hourly data at
the study area are available. The suspended sediment concentration data for training
the network is during the heavy rainfall events on March 2007 and for testing thisnetwork is during the heavy rainfall events on December 2006 and February 2007.
The coefficients between climatic, hydrologic and hydraulic variables; Rainfall (R),
Temperature (T), Discharge (Q), Absolute of (Qt-Qt-2)/2, Wind Speed, Solar, Snow,and suspended sediment concentration are given in Table 1.
Table 1. Correlation coefficients of the input data with suspended sedimentRTQ| Qt-Qt-2|/2WindSolar Snow
timet0.322 0.250 0.580
0.434-0.002-0.006 0.011
t-1 0.371 0.259 0.558
t-2 0.383 0.257 0.535
-SS
1.000
0.660
0.645

4. Results
The root mean square error (RMSE) and coefficient of determination (R2) values of
all case during calibration and verification periods are given in Table 2. The
performance of network is depended on the inputs and network structure. The
networks, in case of rainfall and temperature are inputs, show relatively poor
performance as case 1, 2 and 3. The coefficient of determination (R2) of case 1, 2 and3 during verification period are 0.261, 0.231 and 0.285 respectively. In case ofapplying weather as inputs, the performance of networks is also poor performance ascase 4 and 5. For improving performance of network, it is necessary to add discharge
data as input to network that in case 7 the inputs are rainfall, temperature anddischarge because this is mainly due to the close relationship between suspended
sediment concentration and water discharge. The coefficient of determination (R2) ofcase 7 is 0.635 and root mean square error (RMSE) is about 24.79 % that this network
is good performance for forecast suspended sediment concentration on this catchment.
Thus the best network for forecasting suspended sediment concentration when theclimate, hydrologic and hydraulic data are available is network case 7 which inputs
are rainfall, temperature, water discharge and their previous time step data. If thesuspended sediment data in previous time step is one of input of network, the resultsfrom this network will be more closely with observed data. The performances ofnetworks from case 11 to 18 are good that the coefficients of determination (R2) are
more than 57 % and root mean square error (RMSE) is less than 22 %. The
correlation coefficient of the absolute of (Qt-Qt-2)/2 and suspended sedimentconcentration is also high therefore this study try to apply the absolute of (Qt-Qt-2)/2
to be one of inputs for increasing the performance of network. The performances ofnetwork case 15 and 16 are more than case 13 and 14 in sequence and theperformances of network case 17 and 18 are also more than case 11 and 12 insequence. If the absolute of (Qt-Qt-2)/2 is added to be one of inputs of network, theperformance of that network will increase.
Table 2. Performances of Artificial Neural Networks
Calibration
Verification
InputsCaseTime Network2R
RMSE %
R2
RMSE %
R,T1
t
2,16,10.350
24.20
0.262
36.12
2t, t-1
4,8,10.440
22.43
0.231
45.25
3t,t-1,t-2 6,6,10.410
23.08
0.285
38.71
R,T,W
4t
5,10,10.368
23.83
0.238
40.01
5t,t-1
7,14,10.518
20.82
0.328
65.24
R,T,Q
6t
3,6,10.477
21.68
0.423
27.85
7
t,t-1
6,6,10.549
21.14
0.635
24.79
R,Q
8
t
2,4,10.435
22.53
0.316
28.83
9
t,t-1
4,4,10.427
22.69
0.767
25.20
10
t,t-1,t-2 6,12,1
0.521
20.75
0.525
30.29
R,Q,SS11t3,6,10.665
17.37
0.570
21.20
12
t,t-1
6,18,10.821
12.68
0.674
21.44
Q,SS
13
t
2,4,10.599
18.97
0.599
19.41
14
t,t-1
4,4,10.631
35.45
0.728
17.58
Q,D,SS15t3,6,10.673
17.15
0.730
16.29
16
t,t-15,5,1
0.641
17.95
0.695
17.08
R,Q,D,SS
17t
4,4,1
0.616
18.57
0.615
19.11
18t,t-1
7,7,1
0.698
16.49
0.683
18.34
W is weathers data: wind, solar and snow.
D is absolute of (Qt-Qt-2)/2.
For SS at time t, it means SSt-1 and for SS at time t-1, it means SSt-2.

According to their performances, network case 14, 15 and 18 could be selected as thenetworks that can be used for suspended sediment prediction. The suspended
sediment concentration of them are plotted in Figure 2 and the scatter plots of theobserved and computed suspended sediment concentration by the three networks forverification and calibration of case 18 are shown in Figure 3. From Figure 2 (c), itshows that peak and time to peak of computed line and observed line by verification
period are almost the same therefore the network case 18 is the best for suspended
sediment prediction.

Suspended Sedim ent(ppm )a250
Observed

200

Computed

150
100
5001

12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287
Hours in serialorder

Suspended Sedim ent(ppm )

b250

Observed

200

Computed

150
100
50011223344556677889100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287
Hours in serialorder

Suspended Sedim ent(ppm )

c250
200

Observed

150
100

Computed

500112 2334 4556677889 100 111 122 133 144 155 166 177 188 199 210 221 232 243 254 265 276 287
Hours in serialorder

Suspended Sedim ent(ppm )

d250

Observed

200

Computed

150
100
500125 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529 553 577 601 625 649 673 697 721
Hours in serialorderFigure 2. Comparisons between the observed and computed suspended sedimentconcentration: (a) Verification case 14, (b) Verification case 15, (c) Verification case
18 and (d) Calibration case 18

250

250
R2 = 0.73

R2 = 0.7276
200
C o m p u te d

C o m p u ted

200
150
100

150
100

50

50

00050

100

150

200

250

050

100

O bserved150200250

O bserved

ab
250

250
R2 = 0.6975

R2 = 0.6826
200
C o m p u ted

C o m p u ted

200150100501501005000050100150

O bserved

200250050100150200250

O bserved

cdFigure 3. Scatter plots of observed and computed suspended sediment: (a)Verification case 14, (b) Verification case 15, (c) Verification case 18 and (d)Calibration case 18
5. Discussion and conclusion
Artificial neural network (ANN) was applied to predict the hourly suspended
sediment concentration in Okumotani catchment, by relating rainfall, temperature,
water discharge, absolute of (Qt-Qt-2)/2 and previous time step of suspended sedimentconcentration. It is demonstrated that the artificial neural network (ANN) is capableof modeling the hourly suspended sediment concentration with good accuracy whenproper inputs variables, the absolute of (Qt-Qt-2)/2 and their lag affect on suspended
sediment concentration. This model can be used for real time prediction when theprevious time step data of suspended sediment concentration are known. In order toimprove and confirm this model for upstream of Managawa dam, the seasonal effectshould be considered and the duration of training and testing the network should beextended.
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