Journal of Water Resource and Protection, 2012, 4, 528-539 Published Online July 2012 (
Multistep-Ahead River Flow Prediction Using
LS-SVR at Daily Scale
Parag P. Bhagwat, Rajib Maity
Department of Civil Engineering, Indian Institute of Technology, Kharagpur, India
Received April 5, 2012; revised May 5, 2012; accepted June 1, 2012
In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily
variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment
response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river
flow magnitudes. The pr oblem complexity may ge t enhanced with the influence of upstream dam releases. These issues
are investigated by exploiting the capability of LS-SVR—an approach that considers Structural Risk Minimization
(SRM) against the Empirical Risk Minimization (ERM)—used by other learning approaches, such as, Artificial Neural
Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment,
constructed in 1989. The river gauging station—Sandia is located few hundred kilometer downstream of Bargi dam.
The model development is carried out with pre-construction flow regime and its performance is checked for both pre-
and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both
the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In
order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead
prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions
though the p erformance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and
it is found that the former performs better than the latter for all the lead-times in general, and shor ter lead times in par-
Keywords: Multistep-Ahead Prediction; Kernel-Based Learning; Least Square-Support Vector Regression (LS-SVR);
Daily River Flow; Narmada River
1. Introduction
River flow is an important component in hydrological
cycle, which is directly available to the community. In
hydrology, river flow plays an important role in estab-
lishing some of the critical interactions that occur be-
tween physical, ecological, social or economic processes.
Accurate or at least reasonably reliable prediction of
river flow is an important foundation for preventing
flood, reducing natural disasters, and the optimum man-
agement of water resource. Constructions of major and
minor dams are essential in order to effective use of avai-
lable water resources. This is more crucial for the mon-
soon dominated countries, where most of the annu al rain-
fall occurs during couple of months, and rest of the
months are mostly no-rainfall months. Existence of dams
adds to the complexity in th e river flow modelling, which
is influenced by the releases from upstream reservoirs as
well as the influence of the inputs from the catchment
area between immediate downstream of the dam and the
gauging site.
Depending on the natural variability and complexity,
traditional statistical methods, such as, transfer function
model, Box-Jenkins approach etc. may be inadequate due
to their underlying assumptions. As a consequence many
new methodologies have been introduced to understand
the variations of hydrologic variables and to predict for
the future time steps.
Development of different techniques to predict river
flow is having a long history. Among the parametric linear
approaches, Auto-Regressive Integrated Moving Average
(ARIMA) model is one of the most popular approaches.
Since last decade, machine learning techniques are being
applied in this field. For instance, Artificial Neural Net-
works (ANN), fuzzy logic, genetic programming, etc.,
have been widely used in the modelling and prediction of
hydrologic variables. More recently, kernel-based learn-
ing approaches have gained wide popularity.
The statistical learning framework proposed by Vap-
nik [1] led to the introduction of the Support Vector Ma-
chine (SVM), which has been successfully applied for
opyright © 2012 SciRes. JWARP
nonlinear classification and regression in learning prob-
lems. Kernel based approaches are expected to perform
better due its nonlinear, even smooth enough, feature
space development based on the available historical re-
cord. One such kernel based machines learning approach
is the Least Squares-Support Vector Machine (LS-SVM)
1.1. Support Vector Machines (SVMs) and
Kernel Based Learning
SVMs are a kind of supervised machine learning system
that use a linear high dimensional hypothesis space
called feature space. These systems are trained using a
learning algorithm, which is based on optimization theory.
SVMs belong to a family of generalized linear classifier
[3]. The SVMs can be applied to both classification and
regression problems. Application of SVMs to regression
problem was made in late nineties [1,4]. Popularity of
SVM increases very rapidly since then in fields of text
classification, pattern recognition, remote sensing and so
The basic idea of working principle of SVMs is pro-
vided by the use of kernel functions that implicitly map
the data to a higher dimensional space. According to
Cover’s theorem, linear solution in the higher dimen-
sional feature space corresponds to a non-linear solution
in the original lower dimensional input space. This makes
SVM a powerful tool for solving a variety of hydrologic
problems, which are non-linear in nature. There are me-
thods available which use nonlinear kernels in their app-
roach towards regression problems while applying
1.2. Application of SVMs in Hydrologic
Application of SVMs in the field of hydrology is gaining
wide popularity and the results are found to be encoura-
ging. Such applications range from remotely sensed im-
age classification [5], statistical downscaling [6], soil
water forecasting [7], stream flow forecasting [8] and so
on. Liong and Sivapragasam [9] compared SVM per-
formance with other machine learning model, such as,
ANN in forecasting flood stage and reported a superior
performance of SVM. Bray and Han [10] used SVM for
rainfall runoff modelling and that model was compared
with a transfer function model. The study outlin ed a pro-
mising area of research for further application of SVMs
in unexplored areas. Samui [11] used LS-SVM to deter-
mine evaporation loss of reservoir and it is established to
be a powerful approach for the determination of evapo-
ration loss. She and Basketfield [12] forecasted spring
and fall season stream flows in Pacific Northwest region
of US using SVM and reported superior results in fore-
casting. Zhang et al. [13] studied two machine learning
approaches—ANN and SVM and compared for appro-
ximating the Soil and Water Assessment Tool (SWAT)
model. The results showed that SVM in general exhibited
better generalization ability than ANN. Khadam and
Kaluarachchi [14] discussed the impact of accuracy and
reliability of hydrolog ical data o n model calib ration. Th is,
coupled with application of SVMs, was used to identify
the faulty model calibration, which would have been
undetected otherwise. Applicability of SVMs was also
demonstrated in downscaling Global Circulation Models
(GCMs), which are among the most advanced tools for
estimating future climate change scenarios. The results
presented SVMs as a compelli ng alternative to tradition al
Artificial Neural Networks (ANN) to conduct climate
impact studies [10,11] downscaled monthly precipitation
to basin scale using SVMs and reported the results to be
encouraging in their accuracy while showing large pro-
mise for further applications.
1.3. Advancement of SVM
Apart from the general benefit of SVM pointed out in the
aforementioned studies, SVMs are sometimes criticized
by its large number of parameters and high level of com-
putational effort, particularly in case of large dataset.
Chunking is one of the proposed remedies to the latter
problem. However, according to Suyken et al. [15], it is
worthwhile to investigate the possibility of simplifying
the approach to the extent possible without losing the any
of its advantages. Thus, they proposed a modification
over the SVM approach, which essentially leads to Least
Square-Support Vector Machines (LS-SVM).
The main advantage of LS-SVM is in its higher com-
putational efficiency than that of standard SVM method,
since the training of LS-SVM requires only the solution
of a set of linear equations instead of the long and com-
putationally demanding quadratic programming problem
involved in the standard SVM [2]. Qin et al. [16] in-
vestigated the application of LS-SVM for the modelling
of water vapor and carbon diox ide fluxes and they fou nd
the excellent generalization property of LS-SVM and its
potential for further applications in area of general hy-
drology. Maity et al. [13] investigated the potential of
support vector regression, which is also based on LS-
SVM principle, for prediction of streamflow using en-
dogenous property of the monthly time series. In this
study, potential of LS-SVM for Regression (LS-SVR) is
investigated for the obj ective outlined as follows.
1.4. Objective of This Study
Potential of LS-SVM for Regression (LS-SVR) is ex-
ploited for multistep-ahead river flow prediction at daily
scale, to assess its performance with the increasing time
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horizon. Most of the major river basins are being po-
pulated with major and minor dams. River flow mode-
lling is supposed to be influenced by the effect of the
release from these dams if the site location is on the
downstream side of the dam. However, the effect of its
existence will gradually reduce with increase of the dis-
tance from the dam location. The study is carried out
with observed daily river flow in the upper Narmada
River basin with Sandia gauging station at the outlet.
Bargi dam exists few hundred km upstream from the out-
let of the watershed. Details of this dam are provided in
th e “Study Area” section later. Investigation is carried out
to assess the necessity of consideration of daily releases
from upstream dam on the daily river flow variation at
the outlet of the study area. Also, a multistep-ahead pre-
diction is carried out to assess maximum temporal hori-
zon over which the prediction results may be relied upon.
The results are compared with the performance of neural
networks approach that uses Empirical Risk Minimi-
zation (ERM).
2. Methodology
2.1. Data Normalization
The observed river flow data, as commonly used in data-
driven models, is normalized to prevent the model from
being dominated by the large values. The performance of
LS-SVM with normalized input data has shown to out-
perform the same with non-normalized input data [16].
Therefore, the data is normalized and finally the model
outputs are back transformed to their original form by
denormalisation. The normalization (also back transfor-
mation) is carried out using
where i is the normalized data for day and is
the observed value for day. i
2.2. Least Square-Support Vector Regression
Let us consider a given training set
, where
represents a
in in
input vector and i is a scalar measured output,
which represents system output. Subscript i indicates
the training pattern. In the context of multistep-
ahead river flow prediction, i is the vector comprising
n-previous days normalized river flow values, i is
the target river flow with certain lead-time in day(s) and
subscript indicates the reference time from which n-
previous days and the lead-time are counted. The goal is
to construct a function
, which represents the
dependence of the output on the input . The form
of this function is as follows:
where is known as weight vector and as bias.
This regression model can be constructed using a non-
linear mapping function
. The function
is a mostly nonlinear function, which
maps the data into a higher, possibly infinite, dimen-
sional feature space. The main difference from the stan-
dard SVM is that the LS-SVR involves equality con-
straints instead of inequality constraints, and works with
a least square cost function. The optimization problem
and the equality constraints are defined by the following
Minimize ,22
such that1,,
Je e
be iN
where i is the random error and is a regu-
larization parameter in optimizing the trade-off between
minimizing the training errors and minimizing the mo-
del’s complexity. The objective is now to find the opti-
mal parameters that minimize the prediction error of the
regression model. The optimal model will be chosen by
minimizing the cost function where the errors, i, are
minimized. This formulation corresponds to the regre-
ssion in the feature space and, since the dimension of the
feature space is high, possibly infinite, this problem is
difficult to solve. Therefore, to solve this optimization
problem, the following Lagrange function is given,
 
LwbeJ we
be y
The solution of above can be obtained by partially dif-
ferentiating with respect to , , and
, i.e.
 
wx (5)
Lei N
 
Lbe yiN
From the set of Equations (5)-(8), and e can be
eliminated and finally, the estimated values of b and
, i.e. and i
, can be obtained by solving the linear
system. Replacing in Equation (2 ) from Equatio n (5 ),
Copyright © 2012 SciRes. JWARP
the kernel trick may be applied as follows:
x x
Here, the kernel trick means a way to map the obser-
vations to an inner product space, without actually com-
puting the mapping and it is expected that the obser-
vations will have meaningful linear structure in that inner
product space.
Thus, the resulting LS-SVR model can be expressed as
where is a kernel function.
In comparison with some other feasible kernel fun-
ctions, the RBF is a more compact and able to shorten
the computational training process and improve the gene-
ralization performance of LS-SVR (LS-SVM, in general),
a feature of great importance in designing a model [13].
Aksornsingchai and Srinilta [17] studied support vector
machine with polynomial kernel (SVM-POL), and su-
pport vector machine with Radial Basis Function kernel
(SVM-RBF) and found SVM-RBF is the accurate model
for statistical downscaling methods. Also, many works
have demonstrated the favorable performance of the ra-
dial basis function [9,15]. Therefore, the radial basis fun-
ction is adopted in this study. The nonlinear radial basis
function (RBF) kernel is defined as:
x x,e
Kxx (11)
is the kernel function parameter of the RBF
kernel. The symbol is the norm of the vector
thus, 2
xx xx
i is basically the Euclidean distance be-
tween the vectors and i. In the context of river
flow prediction, i is the new vector of previous river
flow, based on which multi-step ahead prediction (i) is
made with certain lead-time. i (observed) and are
compared to assess the model performances.
2.3. Model Calibration and Parameter
The regularization parameter
determines the trade-
off between the fitting error minimization and smooth-
ness of the estimated function. It is not known before-
hand which
and 2
are the best for a particular
problem to achieve maximum performance with LS-SVR
models. Thus, the regularization parameter
and the
RBF kernel parameter 2
have to be calibrated during
model development period. These parameters are inter-
dependent, and their (near) optimal values are often ob-
tained by a trial-and-error method. Interrelationship is
also coupled with the number of previous river flow
values to be considered, which is denoted as . In order
to find all these parameters (
and ) grid search
method is employed in parameter space. Once the para-
meters are estimated from the training dataset, the ob-
tained LS-SVR model is complete and ready to use for
modelling new river flow data period. Performance of the
developed model is then assessed with the data set during
testing period. Different models (parameter sets) are
developed for different prediction lead-times in case of
multi-step ahead prediction.
2.4. Comparison with Artificial Neural Networks
The flexible computing based ANN models have been
extensively studied and used for time series forecasting
in many hydrologic applications since late 1990s. This
model has the capability to execute complex mapping
between input and output and to form a network that
approximates non-linear functions. A single hidden layer
feed forward network is the most widely used model
form for time series modeling and forecasting [18]. This
model usually consists of three layers: the first layer is
the input layer where the data are introduced to the net-
work followed by the hidden layer where data are pro
cessed and the final or output layer is where the results of
the given input are produced.
The number of input nodes and output nodes in an
ANN are dependent on the problem to which the network
is being applied. However, there is no fixed method to
find out the number of hidden layer nodes. If there are
too few nodes in the hidden layer, the network may have
difficulty in generalizing the problems. On the other hand,
if there are too many nodes in the hidden layer, the net-
work may take an unacceptably long time to learn any
thing from the training set [19]. Increase in the number of
parameters may slow the calibration process [20]. In a
study by Zealand et al. [21], networks were initially con
figured with both one and two hidden layers. However,
the improvement in forecasting results was only marginal
for the two hidden layer cases. Therefore, it is decided to
use a single hidden layer in this study. In most of the
cases, suitable number of neurons in the hidden layer is
obtained based on the trial-and-error method [22]. Maity
and Nagesh Kumar, [23] proposed a GA based evolution-
ary approach to decide the complete network structure.
The output t
of an ANN, assuming a linear output
neuron having a single hidden layer with h sigmoid
hidden nodes, is given by:
and k are the linear transfer function and
bias respectively of the output neuron k,
is the
connection weights between neuron of hidden la-
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yers and output units, is the transfer function of
the hidden layer [24]. The transfer functions can take
several forms and the most widely used transfer func-
tions are Log-sigmoid, Linear, Hyperbolic tangent sig-
moid etc. In this study, hyperbolic tangent sigmoid is
in study area is from 289 to 1134 m. The basin lies
between east longitudes 78˚30' and 81˚45', and north
latitudes 21˚20' and 23˚45'. Bargi dam (later renamed as
Rani Avanti Bai Sagar Project) is a major structure in the
basin up to Sandia, which is located few hundred km up-
stream of Sandia. The latitude and longitude of the dam
are 22˚56'30''N and 79˚55'30''E, respectively. It was
constructed in late eighties and being operated from early
nineties. Thus, pre-construction period (1978-1986) is
considered for training. For testing, two sets of data are
used—pre-construction data set (1986-1990) and post-
construction period (1990-2000). However, out of this
entire range four year s data is missing (1, 1981 - 31 May
1982; June 1, 1987 - May 31, 1988; June 1, 1993 - May
31, 1994; June 1, 1998 - May 31, 1999). These periods
are ignored from the analysis. River flow data from
Sandia station, operated by the Water Resources Agency,
is obtained from Central Water Commission, Govt. of
India. Among these records, a daily data (June 1, 1978 to
May 31, 1986) is used for training and (June 1, 1986 to
May 31, 2000) is used to test the model performance.
fs (13)
where is the input signal referred to as the
weighted sum of incoming information. Several optimi-
zation algorithms can be used to train the ANN. Among
the various training algorithms available, the back-
propagation is most popular and widely used algorithm
[25]. Details of this techniques is well established in the
literature and can be found elsewhere (ASCE 2000 and
references therein) [26].
3. Study Area and Data Sets
Narmada River is the largest west flowing river of Indian
peninsula. It is the fifth largest river in India. The study
area is up to Sandia gauging station, which is in the
upstream part of Narmada river basin as shown in Figure
1. The upstream part Narmada river basin is in the state
of Madhya Pradesh, India. The river originates from
Maikala ranges at Amarkantak and flows westwards over
a length of 334 km up to Sandia. The elevation difference
4. Results and Discussions: Performance of
LS-SVR for River Flow Prediction
4.1. Data Pre-Processing and Parameter
Observed river flow data at Sandia is normalized as ex-
plained in the methodology before proceeding to parameter
Figure 1. Narmada river basin with study area up to Sandia station.
meter estimation. Optimum number of previous river
flow values to be considered is denoted as n. This para-
meter along with the regularization parameter
the RBF kernel parameter 2
is calibrated during mo
del development period (training period). To select best
combination of n,
and 2
, grid search method is
used. Model performances for different combinations of
these parameters are assessed based on statistical mea-
sures, such as, Correlation Coefficient (CC), Root Mean
Square Error (RMSE) and Nash-Sutcliffe Efficiency
(NSE). Ten different values of n (1 through 10) are tested
to decide the optimum number of previous daily river
flow to be considered for the best possible results. Range
is considered to be 25 to 1000 with a resolution of
25 and 2
in the range of 0.01 to 1 with a resolution of
0.01. Approximately (because of different lag and lead-
times considered) 2556 data points are used for training
purpose. Performance of each model is assessed with the
remaining testing data points. Model performances stati-
stic is obtained between observed and modelled river
flow values during training and testing period. The com-
bination that yields comparable performance during
training and testing period is selected, which ensures the
best parameter values without the fear of overfitting.
Results are shown in Table 1 for prediction lead-time of
1 day. This is achieved in the following way: for each
value of n, two (training period and testing period) 2-D
surfaces are obtained for each of the performance mea-
sures (CC, RMSE and NSE). The values of
and 2
are identified for which the training and testing period
performances are “closest”. Priority is given to CC and
corresponding RMSE and NSE are reported for same
values of
and 2
. These values are named as “best
” and “best 2
” in Table 1 for a particular n. It is
found from that the best combination of n,
and 2
is 5 days, 175 and 0.21 respectively. However, these
parameters are for prediction lead-time of 1 day. Opti-
mum combinations of parameters are computed separa-
tely for different prediction lead-times and reported later.
4.2. Model Performance during Pre- and
Post-Construction Testing Period
Model performances are tested during both pre- and
post-construction period. For this purpose, the model is
trained with river flow data during pre-construction pe-
riod and the developed model is used to assess the per-
formance during both pre-construction and post con-
struction period. It is found that the performance remains
almost similar in both pre- and post-construction period
(Table 2). Flow regimes are supposed to be modified on
the immediate downstream of a newly constructed dam
due to the effect of human controlled releases from the
dam. However, such effect is supposed to get diminished
with increase in distance from the dam location towards
downstream. This is being reflected in case of river flow
at Sandia gauging station indicating that the station is
sufficiently away towards downstream. Model perfor-
mance during training period is shown in Figure 2 and
the model performances during testing period—both
pre-construction and post-construction period are shown
in Figures 3 and 4 respectively. The immediate next
question would be to find the temporal horizon up to
which the prediction would be reliable.
4.3. Performance of River Flow Prediction for
Different Lead-Times
The multistep-ahead river flow prediction is carried out
for time steps T, T + 1, T + 2, T + 3 and T + 4. In other
words, five different prediction lead-times are tested for
prediction performance, i.e., 1 day, 2 days, 3 days, 4 days
and 5 days in advance. As stated earlier, optimum combi-
nations of parameters are computed separately for diffe-
rent prediction lead-times. Results are shown in Table 3 .
Table 1. Model performances during training (testing) period for different numbers of previous daily river flows considered
as inputs (Lead-time = 1 day) wi th corre sponding “be st
” and “best ” values.
Number of previous river
flow values input (n) Best
Best 2
1 265 0.01 0.810 (0.810) 0.025 (0.030) 0.657 (0.655)
2 960 0.03 0.829 (0.829) 0.024 (0.029) 0.687 (0.679)
3 865 0.11 0.827 (0.827) 0.024 (0.029) 0.683 (0.675)
4 845 0.31 0.825 (0.825) 0.024 (0.030) 0.681 (0.662)
5 175 0.21 0.827 (0.827) 0.0243 (0.029) 0.684 (0.665)
6 795 0.34 0.829 (0.829) 0.024 (0.029) 0.688 (0.674)
7 710 0.32 0.831 (0.831) 0.024 (0.029) 0.690 (0.681)
8 235 0.22 0.833 (0.833) 0.024 (0.029) 0.694 (0.681)
9 530 0.28 0.838 (0.838) 0.024 (0.028) 0.702 (0.688)
10 750 0.45 0.839 (0.839) 0.024 (0.028) 0.703 (0.696)
Copyright © 2012 SciRes. JWARP
Table 2. Comparison of performance during pre- and post-c onstr uc tion of Bar g i dam.
Testing performance
Statistical Measures Training Period
(5-Jun-78 to 31-May-86) Before dam const ruc ti on
(5-Jun-86 to 31-May-90) After dam construction
(5-Jun-90 to 31-May-97)
Correlation Coefficient 0.83 0.84 0.83
RMSE 0.024 0.017 0.033
NSE 0.68 0.71 0.66
Table 3. Optimum river flow lags and parameter estimation for different lead-times.
Lead-time (in days) Number of previous river flow value s input (n) Gamma (γ) Sigma (σ)
1 5 175 0.21
2 2 75 0.14
3 1 150 0.40
4 1 50 0.38
5 1 75 0.37
Figure 2. Comparison between observed and predicted river flow for training period (1-day-ahead).
Figure 3. A plot between observed and predicted river flow (1-day-ahead) during testing period before construction of dam.
Copyright © 2012 SciRes. JWARP
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Figure 4. A plot between observed and predicted river flow (1-day-ahead) during testing period after construction of dam.
Table 4. Performance of LS-SVR for multistep-ahead daily
river flow prediction for different lead-times.
From these results, it is observed that for different pre-
diction lead-times the best combination of model pa-
rameters varies. The optimum numbers of previous river
values to be considered differ for different lead-times.
This is decreasing with the increase of lead-time. For
instance, five previous days input is best for 1-day ahead
prediction; similarly for 2-day lead-time best is two pre-
vious days inputs and for further lead-times one previous
day input is resulting the best river flow prediction.
(in days) CC RMSE NSE
1 0.83 0.029 0.67
2 0.71 0.036 0.49
3 0.64 0.040 0.39
4 0.60 0.041 0.35
5 0.58 0.042 0.32
Model performance is investigated to determine ability
of model to predict multistep-ahead river flow through
CC, RMSE and NSE. Results are shown in Table 4. It is
found that the prediction performance decreases with the
increase in lead-time. For instance, 69% (square of
correlation coefficient) of daily variation is explained in
case of 1-day-ahead prediction, whereas 50%, 41%, 36%
and 34% of daily variation is explained in case of 2-day,
3-day, 4-day and 5-day ahead predictions respectively.
RMSE and NSE values are also increasing and de-
creasing respectively with the increase in lead-times, in-
dicating that the performance is better for the shorter
prediction lead-times.
4.4. Comparison of LS-SVR and ANN Model
The relative skill of LS-SVR is compared with the per-
formance of ANN. Basics of ANN approaches are dis-
cussed in methodology section. The architecture of ANN
consists of different layers and combinations of neurons
as discussed earlier. Architecture of ANN networks
adopted in this study is having n neurons in the input
layer as river flow values from n previous days are used
as input. As explained earlier, n is the optimum number
of previous river flow values to be considered. Output
layer consists of one neuron as the target is to predict the
daily river flow with a particular lead-time. The adopted
networks for different lead-times also differ from each
other with respect to the number of neuron s in the hi dden
layers. The trial-and-error method is used to find out best
combination of neur on for h idden layer. In trial-and-error
method, different combinations of hidden layer neurons
are tried and performance is checked. Based upon the
performance, optimum number of hidden layer neurons
is selected. It is found that 5, 3, 3, 3 and 3 neurons in
hidden layer provid e the best performance for lead-times
of 1, 2, 3, 4 and 5 days respectively. It might be noted
Plots between observed and predicted river flow for
Training and Testing periods for 1-day lead-times are
shown in Figures 2-4 sequentially.
It is observed that predicted river flow values well
correspond to the observed one at low and medium range
of river flows. However, the highest peak is not captured
properly. As these types of peaks are very short-lived (at
daily scale), the reason could be the effect of some other
factor, such as, sudden flash rain, which is shorter than
the daily temporal resolution and thus, not available from
the information contained in the previous days river
here that back propagation algorithm (as explained ear-
lier) is used to train these networks using 2556 to 2552
(from lead-time 1-day through 5-day) training data set.
The largest network (5-5-1) is having 25 + 5 + 5 + 1 = 31
parameters, which is much less than number of training
Different networks for different lead-times are trained
separately. Statistical measures showing the prediction
performances are obtained during training and testing
period for different lead-time cases and the results are
shown in Table 5. After comparison between LS-SVR
and ANN, it is found that, in general, LS-SVR performs
better than ANN for all the lead-times. For instance, at
1-day lead-time, 69% (CC = 0.83) and 53.2% (CC = 0.73)
of daily river flow variation are captured by LS-SVR and
ANN respectively dur ing the entire testing period (Table
5). Also, for this case, the NSE values are found to be
0.67 and 0.52 in case of LS-SVR and ANN respectively.
The performance of ANN also decreases with the in-
crease in lead-time. It is also found that the difference in
prediction performance obtained from LS-SVR and ANN
is more prominent for shorter lead times. As the lead
time increases, the performance of LS-SVR and ANN
becomes comparable. Different error measures, i.e.,
Mean Absolute Error (MAE), Mean Absolute Relative
Error (MARE) and Root Mean Square Error (RMSE) are
also determined for LS-SVR and ANN based predictions.
Results are shown in Table 6. Based on these measures
also, it is found that error measures for LS-SVR are bet-
ter compared to that of ANN. These observations indi-
cate the higher capability of LS-SVR approach to capture
the river flow variation using the previous inf ormation.
For a visual comparison of prediction performances by
LS-SVR and ANN simultaneously, both LS-SVR and
ANN model predicted river flow values are plotted with
the observed river flow during the model testing period
(Figure 5). This is shown in case of 1-day-ahead
prediction—best for both LS-SVR and ANN. It is ob-
served that predicted river flow values are very well
corresponds to the low and medium range of river flow
in case of LS-SVR and ANN. Relatively better perfor-
mance of LS-SVR is discussed with respect to the sta-
tistical measures as shown in Table 5. However, as
discussed in case of LS-SVR earlier, the peak river flow
values are not captured with reasonable accuracy in case
of ANN as well. Apart from the fact that these peaks are
very short-lived (at daily scale), this is a shortcoming for
both LS-SVR and ANN based approaches. The reason
could be the same as discussed before, i.e., these peaks
might be the effect of some factor, such as, sudden flash
rain, which is shorter than the daily temporal resolution
and thus, not available from the information contained in
the previous days river flows alone. Consideration and
incorporation of such additional inputs could be future
scope of study. However, the better performance of LS-
SVR, particularly for shorter prediction lead-times, as
compared to that of ANN, is noticed in this study.
The better performance of LS-SVR over ANN may be
Table 5. Performance measures for LS-SVR and ANN models for training and testing period (1978-2000). Testing period
values are shown within parentheses.
(in days) Optimum number of previous
daily flow(s) to be considered CC NSE CC NSE
1 5 0.83 (0.83) 0.68 (0.67) 0.76 (0.73) 0.58 (0.52)
2 2 0.71 (0.71) 0.51 (0.49) 0.71 (0.71) 0.50 (0.49)
3 1 0.64 (0.64) 0.40 (0.39) 0.63 (0.59) 0.39 (0.33)
4 1 0.61 (0.60) 0.36 (0.35) 0.66 (0.55) 0.43 (0.30)
5 1 0.58 (0.58) 0.33 (0.32) 0.62 (0.56) 0.39 (0.30)
CC: Correlation Coefficient; NSE: Nash-Sutcliffe Efficiency (NSE).
Table 6. Error measures for LS-SVR and ANN models for training and testing period. Testing period values are shown
within parentheses.
1 0.006 (0.008) 0.031 (0.041) 0.024 (0.029) 0.008 (0.009) 0.055 (0.049) 0.028 (0.035)
2 0.008 (0.010) 0.044 (0.059) 0.030 (0.036) 0.008 (0.010) 0.047 (0.056) 0.031 (0.036)
3 0.009 (0.012) 0.056 (0.065) 0.033 (0.040) 0.009 (0.011) 0.053 (0.058) 0.034 (0.042)
4 0.011 (0.012) 0.066 (0.070) 0.034 (0.041) 0.010 (0.012) 0.061 (0.061) 0.033 (0.043)
5 0.011 (0.013) 0.069 (0.073) 0.035 (0.042) 0.010 (0.012) 0.060 (0.067) 0.034 (0.042)
MAE: Mean Absolute Error; MARE: Mean Absolute Relative Error; RMSE : Root Mean Square Error.
Copyright © 2012 SciRes. JWARP
Figure 5. Comparison between observed and predicted river flow from LS-SVR and ANN (1-day-ahead).
attributed to its fundamental approach towards error mi-
nimization. Fundamental difference in the working prin-
ciples of ANN and LS-SVR (SVM, in general) lies in
their approaches of risk minimization. ANN follows an
Empirical Risk Minimization (ERM) approach, whereas
Structural Risk Minimization (SRM) principle is fol-
lowed in LS-SVR. The SRM minimizes an upper bound
on the generalization error, as opposed to ERM which
minimizes the error on the training data. Thus, the solu-
tions may fall in to local optima in case of ANN [27]. It
is this difference which equips LS-SVR with a greater
potential to generalize the regression surface without the
danger of overfitting the training data set and global opti-
mum solution is guaranteed in LS-SVR [28].
With respect to the number of parameters, LS-SVR is
less complex than ANN. As discussed earlier, there are
only three parameters in LS-SVR based approach,
namely—regularization parameter (
) RBF kernel pa-
rameter (
) and number of previous daily river flow
values to be considered (n). However, in ANN, the num-
ber of hidden layers, number of hidden nodes, transfer
functions and so on must be determined, which are com-
paratively more complex. On the other hand, LS-SVR is
able to provide better prediction with small sample size.
This is because the decision function of LS-SVR is only
determined by supporting vectors. In general, the su-
pporting vectors are only a part of training pattern (from
available river flow data) and the remaining pattern are
not used in constructing the LS-SVR model. Therefore
the performance of LS-SVR may still be acceptable, even
if the sample size is small. In contrast, the decision fun-
ction in ANN is determined by all training data sets [29].
Thus, generalization of relationship between past and
future river flow values is more likely in case of LS-SVR
as found in this study and thus, more suitable for mul-
tistep-ahead prediction.
5. Conclusions
In this study, daily variation of river flow is modelled
and potential Least Square-Support Vector Regression
(LS-SVR) is used for multistep-ahead river flow predic-
tion. Daily river flow values from Sandia station at upper
Narmada river basin in India are used for illustration.
Bargi dam is located few hundred km upstream of Sandia.
It is investigated whether it is required to consider the
releases from the upstream dam to model the daily varia-
tion at Sandia. Parameters of LS-SVR –
tion parameter) and
(RBF kernel parameter) and
optimum numbers of previous river flow values to be
considered (n) are estimated based on the model per-
formance during model development period (June 1,
1978 to May 31, 1986 excluding the missing data). The
model is tested for the period June 1, 1986 to May 31,
2000 and different statistical performance measures (CC,
NSE, RMSE, MAE and MARE) are obtained. These sta-
tistical measures confirm the well correspondence be-
tween observed and predicted river flow values. This
correspondence is found to be better for low and medium
range of flow values. However, the peak river flow va-
lues are not captured with reasonable accuracy in case of
both LS-SVR and ANN.
While comparing the performance during pre- and
post-construction of dam, it is found that the prediction
performances are similar for both the flow regimes,
which indicates that we may ignore the releases from the
dam at daily scale for this gauging site. In order to inves-
tigate the temporal horizon over which this may be relied
upon, a multistep-ahead prediction is carried out and the
model performance is investigated up to 5-day-ahead
Copyright © 2012 SciRes. JWARP
predictions. The performance is found to be decrease
with the increase in lead-time. In other words, the per-
formance is better for shorter lead-times.
General comparison between LS-SVR and ANN mo-
del reveals that the performance of LS-SVR is better that
that of ANN for all the lead-times—1-d ay-ahead through
5-day-ahead prediction. Better performance of LS-SVR,
in comparison with that of ANN, becomes more pro-
minent for shorter prediction lead-times. Thus, the better
performance of LS-SVR, as compared to that of ANN, is
noticed for multistep-ahead prediction. The superior per-
formance of LS-SVR over ANN may be attributed to its
fundamental approach towards error minimization, en-
sured global optimum solution and capability to gene-
ralize the relationship between past and future river flow
values, even with short length of available data. Thus, it
may be inferred that Structural Risk Minimization (SRM)
is better approach as compared to Empirical Risk Mini-
mization (ERM) and LS-SVR, being a SRM based app-
roach, may be used for multistep-ahead prediction to
obtain better performance. Use of exogenous inputs may
be of further research interests.
[1] V. N. Vapnik, “Statistical Learning Theory,” John Wiley
and Sons, New York, 1998.
[2] J. A. K. Suykens and J. Vandewalle, “Least Squares
Support Vector Machine Classifiers,” Neural Processing
Letters, Vol. 9, No. 3, 1999, pp. 293-300.
[3] B. E. Boser, I. Guyon and V. Vapnik, “A Training Algo-
rithm for Optimal Margin Classifiers,” Proceedings Fifth
Annual Workshop on Computational Learning Theory,
Pittsburgh, 1992, pp. 144-152.
[4] H. D. Drucker, C. J. C. Burges, L. Kaufman, A. Smola
and V. Vapnik, “Support Vector Regression Machines,”
In: M. C. Mozer, M. I. Jordan and T. Petsche, Eds., Ad-
vances in Neural Information Processing Systems, Vol. 9,
Morgan Kaufmann, San Mateo, 1997, pp. 155-161.
[5] Y. B. Dibike, S. Velickov, D. Slomatine and M. B. Ab-
bott, “Model Induction with Support Vector Machines:
Introduction and Applications,” Journal of Computing in
Civil Engineering, Vol. 15, No. 3, 2001, pp. 208-216.
doi: 10.1061/(ASCE)0887-3801(2001)15:3(208)
[6] S. Tripathi, V. V. Srinivas and R. S. Nanjundian, “Down-
scaling of Precipitation for Climate Change Scenarios: A
Support Vector Machine Approach,” Journal of Hydrol-
ogy, Vol. 330 No. 3-4, 2006, pp. 621-640.
doi: 10.1016/j.jhydrol.2006.04.030
[7] W. Wu, X. Wang, D. Xie and H. Liu, “Soil Water Con-
tent Forecasting by Support Vector Machine in Purple
Hilly Region,” International Federation for Information
Processing, Vol. 258, 2008, pp. 223-230.
[8] R. Maity, P. P. Bhagwat and A. Bhatnagar, “Potential of
Support Vector Regression for Prediction of Monthly
Streamflow Using Endogenous Property,” Hydrological
Processes, Vol. 24, No. 7, 2010, pp. 917-923.
doi: 10.1002/hyp.7535
[9] S.-Y. Liong and C. Sivapragasam, “Flood Stage Forecas-
ting with Support Vector Machines,” Journal of the Ame-
rican Water Resources Association, Vol. 38, No. 1, 2002,
pp. 173-196. doi:10.1111/j.1752-1688.2002.tb01544.x
[10] M. Bray and D. Han, “Identification of Support Vector
Machines for Runoff Modelling,” Journal of Hydroin-
formatics, Vol. 6, No. 4, 2004, pp. 265-280.
[11] P. Samui, “Application of Least Square Support Vector
Machine (LSSVM) for Determination of Evaporation
Losses in Reservoirs,” Engineering, Vol. 3, No. 4, 2011,
pp. 431-434. doi:10.4236/eng.2011.34049
[12] N. She and D. Basketfield, “Long Range Forecast of
Stream Flow Using Support Vector Machine,” Proceed-
ings of the World Water and Environment Resources
Congress, ASCE, Anchorage, 2005.
[13] X. Zhang, R. Srinivasan and M. V. Liew, “Approxi- mat-
ing SWAT Model Using Artificial Neural Network and
Support Vector Machine,” Journal of the American Water
Resources Association, Vol. 45, No. 2, 2009, pp. 460-474.
[14] I. M. Khadam and J. J. Kaluarachchi, “Use of Soft Infor-
mation to Describe the Relative Uncertainty of Calibra-
tion Data in Hydrologic Models,” Water Resources Re-
search, Vol. 40, No. W11505, 2004, p. 15.
doi: 10.1029/2003WR002939
[15] J. A. K. Suykens, J. De Brabanter, L. Lukas and J.
Vandewalle, “Weighted Least Squares Support Vector
Machines: Robustness and Sparse Approximation,” Neu-
rocomputing, Vol. 48, No. 1-4, 2002, pp. 85-105.
[16] Z. Qin, Q. Yu, J. Li, Z. Wu and B. Hu, “Application of
Least Squares Vector Machines in Modelling Water Va-
por and Carbon Dioxide Fluxes over a Cropland,” Jour-
nal of Zhejiang University Science, Vol. B6, No. 6, 2005,
pp. 491-495, doi: 10.1631/jzus.2005.B0491
[17] P. Aksornsingchai and C. Srinilta, “Statistical Down-
scaling for Rainfall and Temperature Prediction in Thai-
land,” Proceeding of the International MultiConference
of Engineers and Computer Scientists, Hong Kong, Vol. 1,
16-18 March 2011.
[18] G. Zhang, B. E. Patuwo and M. Y. Hu, “Forecasting with
Artificial Neural Networks: The State of the Art,” Inter-
national Journal of Forecasting, Vol. 14, No. 1, 1998, pp.
35-62. doi:10.1016/S0169-2070(97)00044-7
[19] T. Naes, K. Kvaal, T. Isaksson and C. Miller, “Artificial
Neural Networks in Multivariate Calibration,” Journal of
Near-Infrared Spectroscopy, Vol. 1, 1993, pp. 1-11.
[20] A. Y. Shamseldin, “Application of a Neural Network
Technique to Rainfall-Runoff Modeling,” Journal of Hy-
drology, Vol. 199, No. 3, 1997, pp. 272-294.
[21] C. M. Zealand, D. H. Burn and S. P. Simonovic, “Short
Term Stream Flow Forecasting Using Artificial Neural
Copyright © 2012 SciRes. JWARP
Copyright © 2012 SciRes. JWARP
Networks,” Journal of Hydrology, Vol. 214, No. 1-4,
1999, pp. 32-48. doi:10.1016/S0022-1694(98)00242-X
[22] A. S. Weigend, D. E. Rumelhart and B. A. Huberman,
“Predicting the Future: A Connectionist Approach,” In-
ternational Journal of Neural Systems, Vol. 1, No. 3,
1992, pp. 193-209.
[23] R. Maity and D. Nagesh Kumar, “Basin-Scale Stream
Flow Forecasting Using the Information of Large-Scale
Atmospheric Circulation Phenomena,” Hydrological Pro-
cesses, Vol. 22, No. 5, 2008, pp. 643-650.
[24] P. Coulibaly and N. D. Evora, “Comparison of Neural
Network Methods for Infilling Missing Daily Weather
Records,” Journal of Hydroogy, Vol. 341, No. 1-2, 2007,
pp. 27-41. doi:10.1016/j.jhydrol.2007.04.020
[25] H. F. Zou, G. P. Xia, F. T. Yang and H. Y. Wang, “An
Investigation and Comparison of Artificial Neural Net-
work and Time Series Models for Chinese Food Grain
Price Forecasting,” Neurocomputing, Vol. 70, No. 16-18,
2007, pp. 2913-2923. doi:10.1016/j.neucom.2007.01.009
[26] ASCE, “Artificial Neural Networks in Hydrology. II:
Hydrologic Applications,” ASCE Task Committee on
Application of Artificial Neural Networks in Hydrology,
Journal of Hydrologic Engineering, Vol. 5, No. 2, 2000,
pp. 124-137.
[27] T. Van Gestel, J. A. K. Suykens, B. Baesens, S. Viaene, J.
Vanthienen, G. Dedene, B. De Moor and J. Vandewalle,
“Benchmarking Least Squares Support Vector Machine
Classifiers,” Machine Learning, Vol. 54, No. 1, 2004, pp.
5-32. doi:10.1023/B:MACH.0000008082.80494.e0
[28] W. H. Chen, J. Y. Shih and S. Wu, “Comparison of Sup-
port-Vector Machines and Back Propagation Neural
Networks in Forecasting the Six Major Asian Stock Mar-
kets,” International Journal of Electronic Finance, Vol. 1,
No. 1, 2006 , pp. 49-67.
[29] R. M. Balabin and E. I. Lomakina, “Support Vector Ma-
chine Regression (LS-SVM)—An Alternative to Artifi-
cial Neural Networks (ANNs) for the Analysis of Quan-
tum Chemistry Data?” Physical Chemistry Chemical Phy-
sics, Vol. 13, No. 24, 2011, pp. 11710-11718.