11.rainfall runoff modelling of a watershed

Civil and Environmental Research www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol 2, No.2, 2012

Rainfall-runoff modelling of a watershed

Pankaj Kumar Devendra Kumar
GBPUA & T Pantnagar (US Nagar) ,India

Abstract
In this study an adaptive neuro-fuzzy inference system was used for rainfall-runoff modelling for the
Nagwan watershed in the Hazaribagh District of Jharkhand, India. Different combinations of rainfall and
runoff were considered as the inputs to the model, and runoff of the current day was considered as the
output. Input space partitioning for model structure identification was done by grid partitioning. A hybrid
learning algorithm consisting of back-propagation and least-squares estimation was used to train the model
for runoff estimation. The optimal learning parameters were determined by trial and error using gaussian
membership functions. Root mean square error and correlation coefficient were used for selecting the best
performing model. Model with one input and 91 gauss membership function outperformed and used for
runoff prediction.

Keywords: Rainfall, runoff, modelling, ANFIS

Introduction

The hydrologic behavior of rainfall-runoff process is very complicated phenomenon which is
controlled by large number of climatic and physiographic factors that vary with both the time and space.
The relationship between rainfall and resulting runoff is quite complex and is influenced by factors relating
the topography and climate.

In recent years, artificial neural network (ANN), fuzzy logic, genetic algorithm and chaos theory have been
widely applied in the sphere of hydrology and water resource. ANN have been recently accepted as an
efficient alternative tool for modeling of complex hydrologic systems and widely used for prediction. Some
specific applications of ANN to hydrology include modeling rainfall-runoff process (Sajikumar et. al.,
1999). Fuzzy logic method was first developed to explain the human thinking and decision system by
Zadeh (1965). Several studies have been carried out using fuzzy logic in hydrology and water resources
planning (Mahabir et al. 2003; Chang et al., 2002).
Adaptive neuro-fuzzy inference system (ANFIS) which is integration of neural networks and fuzzy
logic has the potential to capture the benefits of both these fields in a single framework. ANFIS utilizes
linguistic information from the fuzzy logic as well learning capability of an ANN. Adaptive neuro fuzzy
inference system (ANFIS) is a fuzzy mapping algorithm that is based on Tagaki-Sugeno-Kang (TSK) fuzzy
inference system (Jang et al., 1997; Loukas, 2001). ANFIS used for many application such as, database
management, system design and planning/forecasting of the water resources (Nayak et. al., 2004; Firat et.
al., 2009 and Wang et. al 2009).

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Vol 2, No.2, 2012

Study area
The Nagwan watershed is located at Upper Siwane river of Damodar-Barakar basin in the Hazaribagh
District of Jharkhand, India, and lies between 85.250 to 85.430 E longitudes and 23.990 to 24.120 N latitude.
The location and topographic map of Nagwan watershed is shown in Figure 3.1. The catchment is
rectangular in shape with an area of 92.46 sq km and length-width (L/W) ratio as 2.7. The maximum and
minimum elevation in the catchment is 640 m and 550 m respectively above mean sea level. The catchment
has very undulating and irregular slope varying from 1 to 25%. The climate of watershed is sub-tropical
with three distinct seasons viz. winter (October to February), summer (March to May) and monsoon (June
to September). The average annual

Fig 1. Location and topographic map of Nagwan watershed

rainfall in the watershed is about 1137 mm. About 90% of the rainfall occurs due to southeast monsoon
during the period of 1st June to 30th September. The daily mean temperature of the watershed ranges from
30C to 400C. The mean monthly relative humidity varies from a minimum of 40% in the month of April to a
maximum of 85% in the month of July.

Materials and methods

Adaptive neuro-fuzzy inference systems (ANFIS)

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Vol 2, No.2, 2012

Adaptive Neuro Fuzzy Inference System (ANFIS) is a fuzzy mapping algorithm that is based on
Tagaki-Sugeno-Kang (TSK) fuzzy inference system (Jang et al., 1997 and Loukas, 2001).ANFIS is
integration of neural networks and fuzzy logic and have the potential to capture the benefits of both these
fields in a single framework. ANFIS utilizes linguistic information from the fuzzy logic as well learning
capability of an ANN for automatic fuzzy if-then rule generation and parameter optimization.
A conceptual ANFIS consists of five components: inputs and output database, a Fuzzy system
generator, a Fuzzy Inference System (FIS), and an Adaptive Neural Network. The Sugeno- type Fuzzy
Inference System, (Takagi and Sugeno, 1985) which is the combination of a FIS and an Adaptive Neural
Network, was used in this study for rainfall-runoff modeling. The optimization method used is hybrid
learning algorithms.
For a first-order Sugeno model, a common rule set with two fuzzy if-then rules is as follows:
Rule 1: If x1 is A1 and x2 is B1, then f1 = a1 x1+b1 x2 + c1.
Rule 2: If x1 is A2 and x2 is B2, then f2 = a2 x1+b2 x2 + c2.
where, x1 and x2 are the crisp inputs to the node and A1, B1, A2, B2 are fuzzy sets, ai, bi and ci (i = 1, 2) are
the coefficients of the first-order polynomial linear functions. Structure of a two-input first-order Sugeno
fuzzy model with two rules is shown in Figure 1 It is possible to assign a different weight to each rule based
on the structure of the system, where, weights w1 and w2 are assigned to rules 1 and 2 respectively.
and f = weighted average
The ANFIS consists of five layers (Jang, 1993), shown in Figure 3.
The five layers of model are as follows:
Layer1: Each node output in this layer is fuzzified by membership grade of a
fuzzy set corresponding to each input.
O1,i = µAi (x1) i = 1, 2
or
O1,i = µBi-2 (x2) i = 3, 4 ... (1.1)

Where, x1 and x2 are the inputs to node i (i = 1, 2 for x1 and i = 3, 4 for x2) and
x1 (or x2) is the input to the ith node and Ai (or Bi-2) is a fuzzy label.

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Vol 2, No.2, 2012

Fig 2. ANFIS architecture

Layer 2: Each node output in this layer represents the firing strength of a rule, which performs fuzzy, AND
operation. Each node in this layer, labeled Π, is a stable node which multiplies incoming signals and sends
the product out.
O2,i = Wi = µAi (x1) µBi (x2) i = 1, 2

... (1.2)

Layer 3: Each node output in this layer is the normalized value of layer 2, i.e., the normalized firing
strengths.
wi
O3,i = Wi = i =1, 2
w1 + w2
… (1.3)
Layer 4: Each node output in this layer is the normalized value of each fuzzy rule. The nodes in this layer

are adaptive .Here Wi is the output of layer 3, and {ai,bi,ci} are the parameter set. Parameters of this

layer are referred to as consequence or output parameters.
O4i = Wi f i = Wi (ai x1 + bi x2 + ci ) i = 1, 2
…(1.4)
Layer 5: The node output in this layer is the overall output of the system, which is the summation of all
coming signals.

2

2
∑W f i i
Y = ∑1Wi f i = 1
2

∑W 1
i 38

Vol 2, No.2, 2012

… (1.5)

In this way the input vector was fed through the network layer by layer. The two major phases for
implementing the ANFIS for applications are the structure identification phase and the parameter
identification phase. The structure identification phase involves finding a suitable number of fuzzy rules
and fuzzy sets and a proper partition feature space. The parameter identification phase involves the
adjustment of the premise and consequence parameters of the system.
Optimizing the values of the adaptive parameters is of vital importance for the performance of
the adaptive system. Jang et al. (1997) developed a hybrid learning algorithm for ANFIS to approximate
the precise value of the model parameters. The hybrid algorithm, which is a combination of gradient
descent and the least-squares method, consists of two alternating phases: (1) in the backward pass, the error
signals recursively propagated backwards and the premise parameters are updated by gradient descent
, and (2) least squares method finds a proper set of consequent parameters (Jang et al., 1997). In premise
parameters set for a given fixed values, the overall output can be expressed as a linear combination of the
consequent parameters.
AX = B … (1.6)
Where, X is an unknown vector whose elements are the consequent parameters. A least squares estimator of X,

namely X*, is chosen to minimize the squared error . Sequential formulas are employed to

compute the least squares estimator of X. For given fixed values of premise parameters, the estimated
consequent parameters are known to be globally optimal.

Material and methods
The daily rainfall and runoff data of monsoon period (June to September) for the period 1993-1999 were
used to describe daily time series and development of models. Daily rainfall and runoff data for the year of
1993 to 1999 were used for the training (calibration) of the developed model whereas daily data for the year
of 2000 to 2002 were used for verification (testing) of the models.
Different combinations of rainfall and runoff were considered as the inputs to the model, and
runoff of the current day was considered as the output. Input space partitioning for model structure
identification was done by grid partition. Hybrid learning algorithm was used to train the models for runoff
prediction. The optimal learning parameters were determined by trial and error (Kim et al., 2002) for
gaussian membership function. In order to choose better model among developed models root mean square
error and correlation coefficient was computed (Nayak et al., 2005). Different combinations of the runoff
and rainfall were used to construct the appropriate input structure in the runoff forecasting model.

Result and Discussions

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Vol 2, No.2, 2012

The study revealed that the highest value of correlation coefficient and least value of root mean square error
were obtained for model with one input as current day rainfall and output as current day runoff. There were
vague results for increasing number of inputs (Previous day’s rainfall as well as previous day’s runoff). It
implies that runoff mainly depends upon rainfall of current day. It is due to small area of watershed and
varying slope condition. Among triangular, bell shaped, trapezoidal and gaussian membership function,
the gaussian membership function was found most suitable for this study. By increasing membership
function number, best fit model was found for 91 gauss membership functions. Input space partitioning for
model structure identification was done by grid partition because of only one input. Quantitative
performance indices such as root mean square error and correlation coefficient for model are 0.964 and
1.087. In case of testing period root mean square error and correlation coefficient are 0.867 and 1.390
respectively.

Fig 3. Observed and estimated runoff during training period

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Vol 2, No.2, 2012

Fig 4. Observed and estimated runoff during testing period

Conclusions
The present study discusses the application and usefulness of adaptive neuro fuzzy inference system based
modelling approach for estimation of runoff. The visual observation based on the graphical comparison
between observed and predicted values and the qualitative performance assessment of the model indicates
that ANFIS can be used effectively for hydrological rainfall runoff modelling. The ANFIS model is flexible
and has options of incorporating the fuzzy nature of the real-world system.

REFERENCES

[1] Chang ,F.J.; Chang, L.C., Huang, H.L.(2002).‟ Real time recurrent neural network for streamflow
forecasting”. Hydrol Process , 16:2577–88
[2] Firat, M.,Turan, M. and Yurdusev, M.A. (2009). ‟Comparative analysis of fuzzy inference systems
for water consumption time series prediction”. Journal of Hydrology ,374 :235–241.
[3] Jang, J.-S. R. (1993) ANFIS: “adaptive network-based fuzzy inference systems”. IEEE Trans.
Syst., Man Cybern. 23: 665–685.
[4] Jang, J.S.R.; Sun, C.T. and Mizutani, E.(1997). “Neuro-Fuzzy and Soft Computing, A Computational
Approach to Learning and Machine Intelligence”. Prentice Hall, NJ,USA ISBN 0-13-261066-3.
[5] Kim, B.; Park, J.H.; Kim, B.S.(2002). “Fuzzy logic model of Langmuir probe discharge data”.
Comput Chem,26(6):573–581.
[6] Loukas, Y.L .(2001). “Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor
for improved QSAR studies”. J Med Chem 44(17):2772–2783.
[7] Mahabir, C.; Hicks, F.E. and Robinson F. A.(2003). “Application of fuzzy logic to forecast seasonal
runoff”. Hydrological Process, 17:3749-3762.

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[8] Nayak, P.C., Sudheer, K.P., Rangan, D.M. and Ramasastri, K.S. (2004). “A neuro-fuzzy
computing technique for modelling hydrological time series”. J. Hydrology, 291 : 52-66.
[9] Sajikumar, N. and Thandaveswara, B.S. (1999). “A non-linear rainfall-runoff model using
artificial neural networks”. J. Hydrol., 216: 32-55.
[10] Takagi, T., and M. Sugeno.(1985). “Fuzzy identification of systems and its application to modeling
and control”. IEEE Transactions on Systems, Man, and Cybernetics,15: 116–132.
[11] Wang,W.C.; Chau, K.W.; Cheng,C.T. and Lin,Q.(2009). “A comparison of performance of several
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[12] Zadeh, L.A. (1965). “Fuzzy Sets.” Information and Control, (8): 338-353.

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