Short term flood forecasting using RBF static neural network modeling a comparative study

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 6, 2010

Short term flood forecasting using RBF static neural
network modeling a comparative study

Rahul P. Deshmukh A. A. Ghatol
Indian Institute of Technology, Bombay Former Vice-Chancellor
Powai, Mumbai Dr. Babasaheb Ambedkar Technological University,
India Lonere, Raigad, India
deshmukh.rahul@iitb.ac.in vc2005@rediffmail.com

Abstract—The artificial neural networks (ANNs) have been essential and plays a vital role in planning for flood regulation
applied to various hydrologic problems recently. This research and protection measures.
demonstrates static neural approach by applying Radial basis The total runoff from catchment area depends upon
function neural network to rainfall-runoff modeling for the various unknown parameters like Rainfall intensity, Duration
upper area of Wardha River in India. The model is developed of rainfall, Frequency of intense rainfall, Evaporation,
by processing online data over time using static modeling. Interception, Infiltration, Surface storage, Surface detention,
Methodologies and techniques by applying different learning Channel detention, Geological characteristics of drainage
rule and activation function are presented in this paper and a basin, Meteorological characteristics of basin, Geographical
comparison for the short term runoff prediction results features of basin etc. Thus it is very difficult to predict runoff
between them is also conducted. The prediction results of the at the dam due to the nonlinear and unknown parameters.
Radial basis function neural network with Levenberg In this context, the power of ANNs arises from the
Marquardt learning rule and Tanh activation function indicate capability for constructing complicated indicators (non-linear
a satisfactory performance in the three hours ahead of time models). Among several artificial intelligence methods
prediction. The conclusions also indicate that Radial basis artificial neural networks (ANN) holds a vital role and even
function neural network with Levenberg Marquardt learning ASCE Task Committee Reports have accepted ANNs as an
rule and Tanh activation function is more versatile than other efficient forecasting and modeling tool of complex hydrologic
combinations for RBF neural network and can be considered systems[22].
as an alternate and practical tool for predicting short term Neural networks are widely regarded as a potentially
flood flow. effective approach for handling large amounts of dynamic,
non-linear and noisy data, especially in situations where the
underlying physical relationships are not fully understood.
Keywords-component; Artificial neural network; Forecasting; Neural networks are also particularly well suited to modeling
Rainfall; Runoff; systems on a real-time basis, and this could greatly benefit
operational flood forecasting systems which aim to predict the
I. INTRODUCTION flood hydrograph for purposes of flood warning and
The main focus of this research is development of control[16].
Artificial Neural Network (ANN) models for short term flood A subset of historical rainfall data from the Wardha
forecasting, determining the characteristics of different neural River catchment in India was used to build neural network
network models. Comparisons are made between the models for real time prediction. Telematic automatic rain
performances of different parameters for Radial basis function gauging stations are deployed at eight identified strategic
artificial neural network models. locations which transmit the real time rainfall data on hourly
The field engineers face the danger of very heavy flow basis. At the dam site the ANN model is developed to predict
of water through the gates to control the reservoir level by the runoff three hours ahead of time.
proper operation of gates to achieve the amount of water In this paper, we demonstrate the use of Radial basis
flowing over the spillway. This can be limited to maximum function neural network (RBF) model for real time prediction
allowable flood and control flood downstream restricting river of runoff at the dam and compare the effectiveness of different
channel capacity so as to have safe florid levels in the river learning rules and activation function. Radial basis function
within the city limits on the downstream. neural network is having a feed-forward structure consisting of
By keeping the water level in the dam at the optimum hidden layer for a given number of locally tuned units which
level in the monsoon the post monsoon replenishment can be are fully interconnected to an output layer of linear units.
conveniently stored between the full reservoir level and the At a time when global climatic change would seem to
permissible maximum water level. Flood estimation is very be increasing the risk of historically unprecedented changes in

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ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 6, 2010
river regimes, it would appear to be appropriate that MSE (Mean Square Error):
alternative representations for flood forecasting should be
considered. The formula for the mean square error is:

  d  yij 
P N
2
II. METHODOLOGY ij
j 0 i  0
In this study different parameters like learning rule and MSE 
NP
activation function are employed for rainfall-runoff modeling
… (1)
using Radial basis function neural network model of artificial
Where
neural network.
P = number of output PEs,
Radial basis functions networks have a very strong N = number of exemplars in the data set,
mathematical foundation rooted in regularization theory for yij
solving ill-conditioned problems. = network output for exemplar i at PE j,
The mapping function of a radial basis function dij
= desired output for exemplar i at PE j.
network, is built up of Gaussians rather than sigmoids as in
MLP networks. Learning in RBF network is carried out in two
phases: first for the hidden layer, and then for the output layer. NMSE (Normalized Mean Square Error):
The hidden layer is self-organising; its parameters depend on
the distribution of the inputs, not on the mapping from the input The normalized mean squared error is defined by
to the output. The output layer, on the other hand, uses the following formula:
supervised learning (gradient or linear regression) to set its P N MSE
parameters. NMSE  2
N
 N 
N  dij 2    dij 
 i 0 N i 0 
P

j 0
… (2)
Where
P = number of output processing elements,
N = number of exemplars in the data set,
MSE = mean square error,
dij
= desired output for exemplar i at processing
element j.

r (correlation coefficient):

The size of the mean square error (MSE) can be used
to determine how well the network output fits the desired
output, but it doesn't necessarily reflect whether the two sets of
data move in the same direction. For instance, by simply
Figure 1. The Radial basis function neural network scaling the network output, the MSE can be changed without
changing the directionality of the data. The correlation
In this study we applied different learning rules to the coefficient (r) solves this problem. By definition, the
RBF neural network and studied the optimum performance correlation coefficient between a network output x and a
with different activation function. We applied Momentum, desired output d is:
Deltabar Delta, Levenberg Marquardt , Conjugate Gradient,
Quick prop learning rule with activation function Tanh, Linear   _

  x  x  d
_
Tanh, Sigmoid and Linear Sigmoid. i i d
i  
r N
2 2
  _
 _
Performance Measures:   di  d 
i  
  xi  x 
i  
The learning and generalization ability of the estimated N N … (3)
NN model is assessed on the basis of important performance
measures such as MSE (Mean Square Error), NMSE
(Normalized Mean Square Error) and r (Correlation 1 N _
1 N

x d
_
coefficient) x i d i
where N i 1 and N i 1

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The correlation coefficient is confined to the range [-1, 1]. Data: Rainfall runoff data for this study is taken from the
When r = 1 there is a perfect positive linear correlation between Wardha river catchment area which contains a mix of urban
x and d, that is, they co-vary, which means that they vary by and rural land. The catchments is evenly distributed in eight
the same amount. zones based on the amount of rainfall and geographical survey.
The model is developed using historical rainfall runoff data ,
III. STUDY AREA AND DATA SET provided by Upper Wardha Dam Division Amravati,
The Upper Wardha catchment area lies directly in the path department of irrigation Govt. of Maharashtra. Network is
of depression movements which originates in the Bay of trained by rainfall information gathered from eight telemetric
Bengal. When the low pressure area is formed in the Bay of rain-gauge stations distributed evenly throughout the catchment
Bengal and cyclone moves in North West directions, many area and runoff at the dam site.
times this catchment receives very heavy intense cyclonic The data is received at the central control room online through
precipitation for a day or two. Occurrence of such events have this system on hourly basis. The Upper Wardha dam reservoir
been observed in the months of August and September. operations are also fully automated. The amount of inflow,
Rainfall is so intense that immediately flash runoff, causing amount of discharge is also recorded on hourly basis. From the
heavy flood has been very common feature in this catchment. inflow and discharge data the cumulative inflow is calculated.
For such flashy type of catchment and wide variety in The following features are identified for the modeling the
topography, runoff at dam is still complicated to predict. The neural network .
conventional methods also display chaotic result. Thus ANN
TABLE I - THE PARAMETERS USED FOR TRAINING THE NETWORK
based model is built to predict the total runoff from rainfall in
Upper Wardha catchment area for controlling water level of the
Month RG1 RG2 RG3 RG4 RG5 RG6 RG7 RG8 CIF
dam.
In the initial reaches, near its origin catchment area is hilly • Month – The month of rainfall
and covered with forest. The latter portion of the river lies • Rain1 to Rain8 – Eight rain gauging stations.
almost in plain with wide valleys. • Cum Inflow – Cumulative inflow in dam
The catchment area up to dam site is 4302 sq. km. At
Seven years of data on hourly basis from 2001 to 2007 is
dam site the river has wide fan shaped catchment area which
used. It has been found that major rain fall (90%) occurs in the
has large variation with respect to slope, soil and vegetation
month of June to October Mostly all other months are dry
cover.
hence data from five months. June to October is used to train
the network

IV. RESULT
The different structures of neural network are
employed to learn the unknown characterization of the system
from the dataset presented to it. The dataset is partitioned into
three categories, namely training, cross validation and test. The
idea behind this is that the estimated NN model should be
tested against the dataset that was never presented to it before.
This is necessary to ensure the generalization. An experiment is
performed at least twenty five times with different random
initializations of the connection weights in order to improve
generalization.
Figure 2- Location of Upper Wardha dam on Indian map
The data set is divided in to training , testing
and cross validation data and the network is trained for all
models of Radial basis function neural network for 5000
epochs.
The performance results obtain on parameters by
applying learning rules Momentum, Deltabar Delta, Levenberg
Marquardt , Conjugate Gradient, Quick prop with activation
function Tanh, Linear Tanh, Sigmoid, Linear Sigmoid are
listed in Table II through Table VI.

Figure 3- The Wardha river catchment

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TABLE II - RBF NETWORK PERFORMANCE WITH MOMENTUM LEARNING RULE TABLE V - RBF NETWORK PERFORMANCE WITH CONJUGATE GRADIENT
LEARNING RULE

Param MSE N Min Max
eter MSE Abs Abs r Param MSE N Min Max
1 error error eter MSE Abs Abs r
4 error error
Tanh 0.106 0.124 0.034 0.465 0.534
Tanh 0.094 0.165 0.051 0.312 0.646
Linear 0.097 0.105 0.024 0.212 0.639
Tanh Linear 0.089 0.094 0.059 0.215 0.633
Sigmo 0.089 0.093 0.047 0.421 0.678 Tanh
id Sigmo 0.092 0.134 0.041 0.474 0.701
Linear 0.094 0.132 0.041 0.381 0.689 id
Sigmo Linear 0.094 0.124 0.064 0.541 0.732
id Sigmo
id

TABLE VI - RBF NETWORK PERFORMANCE WITH QUICK PROP. LEARNING
TABLE III - RBF NETWORK PERFORMANCE WITH DELTABAR DELTA RULE
LEARNING RULE

Param MSE N Min Max
Param MSE N Min Max MSE Abs Abs r
MSE Abs Abs r eter
eter 5 error error
2 error error
Tanh 0.133 0.245 0.042 0.465 0.584
Tanh 0.093 0.141 0.051 0.564 0.651
Linear 0.169 0.212 0.054 0.514 0.601
Linear 0.190 0.241 0.041 0.412 0.591 Tanh
Tanh Sigmo 0.106 0.256 0.059 0.329 0.563
Sigmo 0.143 0.215 0.032 0.495 0.543 id
id Linear 0.098 0.112 0.046 0.311 0.609
Linear 0.086 0.095 0.067 0.315 0.603 Sigmo
Sigmo id
id

The parameters and performance for RBF model with
different learning rule and activation function are compared on
TABLE IV - RBF NETWORK PERFORMANCE WITH L. M. LEARNING RULE
the performance scale and are listed in the Table VII shown
below. The comparative analysis of the MSE and r (the
Param MSE N Min Max correlation coefficient) is done.
eter MSE Abs Abs r
3 error error
TABLE VII – COMPARISON OF PERFORMANCE PARAMETERS
Tanh 0.076 0.064 0.018 0.143 0.854
Linear 0.086 0.094 0.028 0.298 0.732
Tanh
Sigmo 0.083 0.094 0.020 0.228 0.634
id
Linear 0.089 0.095 0.034 0.469 0.758
Sigmo
id

After training the network the optimum performance is studied
and it is found that Levenberg Marquardt learning rule and
Tanh activation function produce optimal result. In the Table-
VIII the parameters and the best performances for Radial basis
function neural network are listed.

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TABLE VIII- RBF NETWORK PARAMETERS have fewer weights, these networks train extremely fast and
require fewer training samples.

Parameter Performance
MSE 0.07629 V. CONCLUSION
NMSE 0.06431 An ANN-based short-term runoff forecasting system is
Min Abs Error 0.01943 developed in this work. A comparison between five different
Max Abs Error 0.14387 learning rules with four activation function for optimal
r 0.85437 performance for Radial basis function neural network model is
made. We find that Radial basis function neural network with
Levenberg Marquardt learning rule and Tanh activation
Fig 4 shows the plot of actual Vs predicted optimum values for function is more versatile than other approaches studied. Radial
Radial basis function neural network found with Levenberg basis function neural network with Levenberg Marquardt
Marquardt learning rule and Tanh activation function. learning rule and Tanh activation function is performing better
as compare to other approaches studied as far as the overall
performance is concerned for forecasting runoff for 3 hrs lead
time. Other approaches studied are also performing optimally.
Which means that static model of Radial basis function neural
Actual Vs Predicted Runoff by RBF NN Model
network with Levenberg Marquardt learning rule and Tanh
10 activation function is powerful tool for short term runoff
8 forecasting for Wardha River basin.
Runoff

6
4
ACKNOWLEDGMENT
2
0
This study is supported by Upper Wardha Dam Division
1 3 5 7 9 11 13 15 17 19 21 23 25 Amravati, department of irrigation Govt. of Maharashtra, India
Exemplar

Actual Runoff Predicted Runoff
REFERENCES
Figure 4.– Actual Vs. Predicted runoff by RBF for L.M. and Tanh

The error found in the actual and predicted runoff at the [1] P. Srivastava, J. N. McVair, and T. E. Johnson, "Comparison of process-
based and artificial neural network approaches for streamflow modeling
dam site is plotted for RBF network as shown in the Figure 5. in an agricultural watershed," Journal of the American Water Resources
Association, vol. 42, pp. 545563, Jun 2006.
Error in prediction for RBF NN Model [2] K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward
networks are universal approximators," Neural Netw., vol. 2, pp. 359-
366,1989.
1
0.2000
26 2 [3] M. C. Demirel, A. Venancio, and E. Kahya, "Flow forecast by SWAT
25 3 model and ANN in Pracana basin, Portugal," Advances in Engineering
24 0.1000 4
Software, vol. 40, pp. 467-473, Jul 2009.
23 5
0.0000 [4] A. S. Tokar and M. Markus, "Precipitation-Runoff Modeling Using
22 6
-0.1000 Artificial Neural Networks and Conceptual Models," Journal of
21 7 Hydrologic Engineering, vol. 5, pp. 156-161,2000.
-0.2000 error
20 8 [5] S. Q. Zhou, X. Liang, J. Chen, and P. Gong, "An assessment of the VIC-
19 9 3L hydrological model for the Yangtze River basin based on remote
18 10 sensing: a case study of the Baohe River basin," Canadian Journal of
17 11 Remote Sensing, vol. 30, pp. 840-853, Oct 2004.
16 12
15 13 [6] R. J. Zhao, "The Xinanjiang Model," in Hydrological Forecasting
14 Proceedings Oxford Symposium, lASH, Oxford, 1980 pp. 351-356.
[7] R. J. Zhao, "The Xinanjiang Model Applied in China," Journal of
Hydrology, vol. 135, pp. 371-381, Ju11992.
Fig 5 – Error graph of RBF Model for L.M. and Tanh [8] D. Zhang and Z. Wanchang, "Distributed hydrological modeling study
with the dynamic water yielding mechanism and RS/GIS techniques," in
Proc. of SPIE, 2006, pp. 63591Ml-12.
[9] J. E. Nash and I. V. Sutcliffe, "River flow forecasting through
conceptual models," Journal ofHydrology, vol. 273, pp. 282290,1970.
The main advantage of RBF is that it finds the input to output [10] D. Zhang, "Study of Distributed Hydrological Model with the Dynamic
map using local approximators. Each one of these local pieces Integration of Infiltration Excess and Saturated Excess Water Yielding
is weighted linearly at the output of the network. Since they Mechanism." vol. Doctor Nanjing: Nanjing University, 2006, p. 190.529

97 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 6, 2010
[11] E. Kahya and J. A. Dracup, "U.S. Streamflow Patterns in Relation to the
EI Nit'lo/Southern Oscillation," Water Resour. Res., vol. 29, pp. 2491-
2503 ,1993.
[12] K. J. Beven and M. J. Kirkby, "A physically based variable contributing
area model of basin hydrology," Hydrologi cal Science Bulletin, vol. 43,
pp. 43-69,1979.
[13] N. J. de Vos, T. H. M. Rientjes, “Constraints of artificial neural
networks for rainfall-runoff modelling: trade-offs in hydrological state
representation and model evaluation”, Hydrology and Earth System
Sciences, European Geosciences Union, 2005, 9, pp. 111-126.
[14] Holger R. Maier, Graeme C. Dandy, “Neural networks for the perdiction
and forecasting of water resources variables: a review of modeling issues
and applications”, Environmental Modelling & Software, ELSEVIER,
2000, 15, pp. 101-124.
[15] T. Hu, P. Yuan, etc. “Applications of artificial neural network to
hydrology and water resources”, Advances in Water Science, NHRI,
1995, 1, pp. 76-82.
[16] Q. Ju, Z. Hao, etc. “Hydrologic simulations with artificial neural
networks”, Proceedings-Third International Conference on Natural
Computation, ICNC, 2007, pp. 22-27.
[17] G. WANG, M. ZHOU, etc. “Improved version of BTOPMC model and
its application in event-based hydrologic simulations”, Journal of
Geographical Sciences, Springer, 2007, 2, pp. 73-84.
[18] K. Beven, M. Kirkby, “A physically based, variable contributing area
model of basin hydrology”, Hydrological Sciences Bulletin, Springer,
1979, 1, pp.43-69.
[19] K. Thirumalaiah, and C.D. Makarand, Hydrological Forecasting Using
Neural Networks Journal of Hydrologic Engineering. Vol. 5, pp. 180-
189, 2000.
[20] G. WANG, M. ZHOU, etc. “Improved version of BTOPMC model and
its application in event-based hydrologic simulations”, Journal of
Geographical Sciences, Springer, 2007, 2, pp. 73-84.
[21] H. Goto, Y. Hasegawa, and M. Tanaka, “Efficient Scheduling Focusing
on the Duality of MPL Representatives,” Proc. IEEE Symp.
Computational Intelligence in Scheduling (SCIS 07), IEEE Press, Dec.
2007, pp. 57-64.
[22] ASCE Task Committee on Application of Artificial Neural Networks in
Hydrology, ”Artificial neural networks in hydrology I: preliminary
concepts”, Journal of Hydrologic Engineering, 5(2), pp.115-123, 2000

Rahul Deshmukh received the B.E. and
M.E. degrees in Electronics Engineering from
Amravati University. During 1996-2007, he
stayed in Government College of Engineering,
Amravati in department of Electronics and
telecommunication teaching undergraduate
and postgraduate students. From 2007 till now
he is with Indian Institute of Technology (IIT)
Bombay, Mumbai. His area of reserch are
artificial intelligence and neural networks.

A. A. Ghatol received the B.E. from
Nagpur university foallowed by M. Tech
and P.hd. from IIT Bombay. He is best
teacher award recipient of government of
Maharastra state. He has worked as
director of College of Engineering Poona
and Vice-Chancellor, Dr. Babasaheb
Ambedkar Technological University,
Lonere, Raigad, India. His area of
research is artificial intelligence, neural
networks and semiconductors.

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