Research Journal of Applied Sciences, Engineering and Technology 4(21): 4284-4292, 2012
ISSN: 2040-7467
© Maxwell Scientific Organization, 2012
Submitted: February 02, 2012
Accepted: March 02, 2012
Published: November 01, 2012
Development of an ANN-Based Model for Forecasting River Kaduna Discharge
J.O. Folorunsho, E.O. Iguisi, M.B. Mu’azu and S. Garba
Department of Geography, Ahmadu Bello University, Zaria, Nigeria
Abstract: Artificial Neural Networks (ANNs) provides a quick and flexible means of creating models for river
discharge forecasting and has been shown to perform well in comparison with conventional methods. This
paper presents a method of discharge prediction for River Kaduna by developing an ANN-based model. Given
the major triple problems of unavailability, inconsistency and paucity of data, the water resources planning and
development in any drainage basin always suffer a setback. Rainfall, temperature, relative humidity and the
stage height (input variables) and discharge (target output) data were obtained for River Kaduna drainage basin
for April-October 1975 to 2004. In order to develop the ANN model, the data set was partitioned into two parts
of 24 months sets. 70% of the entire data was used as training data and 30% of the entire data used as the
validation data. From the results obtained, the developed Artificial Neural Network (ANN) model developed
in the PredictDemo NeuralWare Environment using the Neural Statistics shows a correlation value of 82%.
Keywords: Artificial neural networks, discharge, forecasting, river Kaduna, water resources
INTRODUCTION
The application of Artificial Neural Network (ANN)
forecasting tool for river discharge is the main focus of
this research as a supposed tool for water resources
management in the River Kaduna drainage basin. Thus,
effective management of the highly variable water
resources of any river in any environment requires an
understanding of its flow characteristics such that,
suitable quality water can be provided for the
municipalities under various conditions within
institutional constraints (Texas Water Resources Institute
Report-165, 1994). Therefore, the study and
understanding of the amount of water that would be
discharged by a stream in the future is of crucial
importance to the water resources development, planning
and management of any area. This is so because, it affects
directly the design and operations of many water
resources structures (Kisi, 2004, 2005).
Three major problems resulted in a great setback for
water resources harnessing for effective planning and
development in Nigeria inspite of its vast water resources
endowment. First among these problems is the nonavailability of consistent streamflow data of Nigerian
rivers with less than 30% of its rivers gauged
(Oguntoyinbo, 1979). Secondly, is the vandalization of
available manual (though obsolete) gauging equipment in
few rivers in Nigeria and finally, is the prohibitive cost of
gauging equipment. Apart from these ones, general
knowledge about variation of river flows are often
invalidated by the short time or unavailability of
consistent and unreliable discharge data (Ward, 1968;
Jagtap, 1995). It has also been established that these
contributed noticeably to the inability to plan and manage
the water resources assiduously (Nemec and Schaake,
1982; Cohen, 1987; Arnell et al., 1990; Lettenmaier and
Gan, 1990; Saelthun et al., 1990; Vehvilainen and
Lohvansuu, 1991; Krasovskaia and Gottenschalk, 1993;
Krasovskaia et al., 1993; Wateren-de-Hoog, 1993;
Samuel, 1993).
In hydrological studies various methods or techniques
have been used for stream flow forecasting as found in the
literature, but they can all be categorized, following Ward
(1975), as empirical methods, statistical methods,
analytical methods and modeling methods. Similarly,
whichever of these methods is chosen will depend on a
number of factors including the purpose of the prediction
or forecasting, the available data as well as the size and
other characteristics of the basin (Ward, 1975). Most of
these methods are based on the conventional statistical
analysis of flow data which were measured in the past.
Many of them offer very complex or too demanding tools
for practical cases. Among them all, the most well
employed method is regression analysis of the observed
data. Regression of the observed data in a nearby river can
give important information on the behavior of the targeted
river. Unfortunately, the hydrological and topographical
conditions of the two basins should be similar to each
other (Kilinc et al., 2000). Recently, flow forecasting
using ANN model has been accepted as a good alternative
to forecasting with the erstwhile known hydrological and
hydrodynamics models (ASCE, 2000a,b). According to
Hsu et al. (1995), ANN approaches to rainfall-runoff
modeling are more efficient than the conventional flow
Corresponding Author: J.O. Folorunsho, Department of Geography, Ahmadu Bello University, Zaria, Nigeria
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Res. J. Appl. Sci. Eng. Technol., 4(21): 4284-4292, 2012
Fig. 1: The drainage basin of river kaduna source: Modified form drainage map of kaduns state
forecasting models whenever explicit knowledge of the
hydrological balance is not required and when the system
may be treated as a black-box.
However, in the hydrological forecasting context,
recent experiments have reported that ANNs (particularly
the MLP and the RBF network) may offer a promising
alternative for rainfall-runoff modeling (Smith and Eli,
1995; Tokar and Johnson, 1999), streamflow prediction
(Sivakumar et al., 2002; Kisi, 2004, 2005; Antar et al.,
2006) and reservoir inflow forecasting (Jain et al., 1999)
to mention just a few. The application ANNs to various
aspects of hydrological modeling has undergone much
investigation in recent years. This interest has been
motivated by the complex nature of hydrological systems
and the ability of ANNs to model non-linear relationships.
Although deterministic models strive to account for all
physical and chemical processes, their successful
employment may be restricted by a need for catchmentspecific data and the simplifications involved in solving
the governing equations. The use of time-series methods
may therefore be complicated by non-stationarity and
non-linearity in the data, requiring experience and
expertise from the modeler and which are not always
available especially in the developing environment but
ANNs offer a relatively quick and flexible means of
modeling and as such applications of ANN modeling are
widely reported in hydrological literature (Raman and
Sunilkumar, 1995; Maier and Dandy, 1996; Loke et al.,
1997; Shamseldin et al., 1997; Zhang and Stanley, 1997;
Brion and Lingireddy, 1999; Imrie et al., 2000). This
study is is therefore aimed at developing an artificial
neural network model in forecasting the discharge of river
Kaduna.
Study area: River Kaduna drainage basin lies between
latitude 9º30!N and latitude 11º45!N; longitude 7º03!E and
longitude 8º30!E with a total basin area of approximately
21,065 km2 (Fig. 1). The basin enclosed major rivers such
as Kubanni, Galma, Tubo which are tributaries to the
main River Kaduna and a greater part of the Kaduna
metropolis. The basin lies on the High Plains of Northern
Nigeria at altitude of about 670 m above sea level and
situated within the Northern Guinea Savanna. Typical of
the savanna climate, River Kaduna drainage basin
experience distinct wet and dry seasons. The wet season
(May-October) is characterized by conventional rainfall
followed by intense lightning and thunderstorms. The
mean annual rainfall can be as high as 2000 mm in wet
years and as low as 500 mm in drought years, but with a
long term average of 1000 mm (Folorunsho, 2004). The
dry season (November-April) is characterized by a period
of low temperature with harmattan season around
December-February; and the hot dry season between
March-April with temperatures as high as 32ºC. Relative
Humidity is high only during the raining season, but drops
during the dry season (Sawa and Buhari, 2011).
MATERIALS AND METHODS
Monthly averages of rainfall for Zaria, Kaduna and
Jos (the drainage basin under study), temperature, relative
humidity for Kaduna metropolis and stage height of River
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Res. J. Appl. Sci. Eng. Technol., 4(21): 4284-4292, 2012
Fig. 2: Showing the initial launch of the ANN model
Fig. 3: Showing the ANN model building wizard dialog box
Fig. 4: Showing the initial step of the ANN model building wizard
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Res. J. Appl. Sci. Eng. Technol., 4(21): 4284-4292, 2012
Fig. 5: Showing building ANN model (step 2 of 6) -location of input data
Fig. 6: Showing building ANN model (step 3 of 6) -location of target output
Fig. 7: Showing building ANN model (step 4 of 6) - data properties
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Res. J. Appl. Sci. Eng. Technol., 4(21): 4284-4292, 2012
Fig. 8: Showing building ANN model (step 5 of 6) - variable selection and network search
Fig. 9: Showing data analysis and transformation dialog box
Fig. 10: Showing the data partitioning dialog box
4288
35
30
25
20
15
10
5
0
1975 Apr
Apr
1977 Apr
Apr
1980 Apr
Apr
1982 Apr
Apr
1984 Apr
Apr
1986 Apr
Apr
1988 Apr
Apr
1990 Apr
Apr
1992 Apr
Apr
1994 May
1996 Jun
1997 Jun
Jun
1998 Jun
Jun
2001 Jun
Aug
Kaduna (input variables) and discharge data (output) for
River Kaduna was used for the forecast. The data
covering a period of 30 years (1974-2004) and for the
rainy months in the basin (Apr-Oct) were sourced from
the Hydrology Department, Kaduna State Water Board,
Kaduna and Nigerian Meteorology Agency, Oshodi,
Lagos, Nigeria.
The ANN-based model of forecasting for this study
was developed in the NeuralWare PredictDemo
environment using the 6 basic procedures such as
launching the model and loading the data set partitioned
into 70% of the data sets for training and 30% for the
testing and validation; configuration of the model;
training of the model using 4 sets of variables for input;
model performance testing; model validation for
reliability for forecasting the discharge and graphical plots
of actual and forecasted discharge output in order to
ability of the model to learn the trend, cycles and pattern
Rainfall (mm)
Res. J. Appl. Sci. Eng. Technol., 4(21): 4284-4292, 2012
(Apr-oct, 1975-2004)
Fig. 13: Showing the rainfall plot for April-October, 1975-2004
of the discharge. In this research, the ANN-based model
development using the NeuralWare PredictDemo
interface to achieve the desired operations from the initial
launch to the completed training era are enumerated
below from Fig. 2 to 12.
Fig. 11: Showing data transformation and input variable dialog bo
Fig. 12: Showing the training completed dialog box
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0.30
0.25
0.20
0.15
0.10
0.05
1975 Apr
Apr
1977 Apr
Apr
1980 Apr
Apr
1982 Apr
Apr
1984 Apr
Apr
1986 Apr
Apr
1988 Apr
Apr
1990 Apr
Apr
1992 Apr
Apr
1994 May
1996 Jun
1997 Jun
Jun
1998 Jun
Jun
2001 Jun
Aug
0
1975 Apr
May
1977 Jun
July
1980 Aug
Sept
1982 Oct
Apr
1984 May
Jun
1986 July
Aug
1988 Sept
Oct
1990 Apr
May
1992 Jun
July
1994 Aug
Sept
1996
1997 Oct
Apr
1998 May
Jun
2001 July
Sept
o
90
80
70
60
50
40
30
20
10
0
Stage height (m)
Temperature ( C)
Res. J. Appl. Sci. Eng. Technol., 4(21): 4284-4292, 2012
(Apr-oct, 1975-2004)
(Apr-oct, 1975-2004)
Fig. 16: Showing the stage height plot for April-October, 19752004
1200
100
1000
Discharge (m3/S)
120
80
60
40
20
800
600
400
200
0
1975 Apr
May
1977 Jun
July
1980 Aug
Sept
1982 Oct
Apr
1984 May
Jun
1986 July
Aug
1988 Sept
Oct
1990 Apr
May
1992 Jun
July
1994 Aug
Sept
1996
1997 Oct
Apr
1998 May
Jun
2001 July
Sept
0
1975 Apr
May
1977 Jun
July
1980 Aug
Sept
1982 Oct
Apr
1984 May
Jun
1986 July
Aug
1988 Sept
Oct
1990 Apr
May
1992 Jun
July
1994 Aug
Sept
1996
1997 Oct
Apr
1998 May
Jun
2001 July
Sept
Relative humidity (%)
Fig. 14: Showing the temperature plot for April-October, 19752004
(Apr-oct, 1975-2004)
(Apr-oct, 1975-2004)
Fig. 15: Showing the relative humidity plot for April-October,
1975-2004
Fig. 17: Showing the discharge plot for April-October, 19752004
Fig. 18: Showing the ‘Test’ command dialog box for the validation data
RESULTS AND DISCUSSION
The plots of the monthly average of all the input
variables and the target output used for this study are
shown in Fig. 13-17. These variables were loaded for the
ANN model development which was achieved using the
procedures outlined in the NeuralWare PredictDemo
interface. These processes were used in order to achieve
the training of the model with the results displayed
in Fig. 18.
However, one of the major tests of reliability of a
good ANN- based model is that the training set
performance and that of the test set are fairly similar. The
key indicator of such model is the performance of the
model when subjected to the standard procedural and
statistical tests such as outlined earlier. For this study,
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Res. J. Appl. Sci. Eng. Technol., 4(21): 4284-4292, 2012
Discharge (m3/S)
1200
the ANN-based model is a veritable tool in overcoming
the discharge record paucity, inconsistency and
unreliability hampering water resources development in
the study area.
Series 1
Series 2
1000
800
600
400
ACKNOWLEDGMENT
200
141
151
161
171
1
11
21
31
41
51
61
71
81
91
101
111
121
131
0
Month
Fig. 19: Showing the actual discharge and the ANN predicted
discharge (1975-2004)
these values were found to be similar such that while the
R for the trained data was 80%, the tested data was 82%,
RMSE is 145, Accuracy is 82% and the Confidence
Interval is 290 (Fig. 2).
Considering the test result generated by the ANNbased model as displayed in Fig. 2, the model has learned
both the trend and the cycle of the actual data. This has
further confirmed the reliability of the model for
purposeful water resources planning and development for
the basin. However, in order to validate the model, the
performance of the actual discharge and the ANN-based
model in its ability to predict the River Kaduna discharge
was tested. This was carried out by comparing the actual
discharge output and the ANN-based model output and
the result is shown in Fig. 19.
From Fig. 19, it becomes clearer that the ANN-based
model developed has been able to learn both the trend and
the cycle of the River Kaduna discharge with a relatively
high figure of merit as indicated by the 82% correlation
value. This present a good effects on the reliability of the
ANN model for forecasting discharge series that can
enhance formidable policy that can stand the test of time
for sustainable water resources planning for the study
area.
CONCLUSION
The sustainable development of any region is of
paramount importance to the planning, development and
management of its water resource. As such, developing
models to effectively predict the river discharge is quite
apt. In this research work, ANN-based model was
developed to forecast the discharge of River Kaduna
depending on available variables (rainfall, temperature,
relative humidity and stage height). The ANN-Based
Model was developed in the NeuralWare PredictDemo
environment. Hence based on the results obtained from
this research, it can be concluded that ANN is a reliable
modeling tool for predicting river discharge considering
the strong, high and positive Correlation Coefficient the
model displayed. From the foregoing, it can be concluded
The author grateful to Dr. M.B. Muazu and Engr.
Bashira both of Department of Electrical Engineering,
Ahmadu Bello University, Zaria, Kaduna State for their
effort to painstakingly taking me through several classes
on how to use the ANN model toolbox.
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