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Inflow modeling of Okhla barrage using
ANN and Fuzzy- based models
Final Presentation29thNovember,2011
Vipin Sharma (08131)Dhruv Chaudhary (08109)
Amit Sharma (08140)
Amit Sehara (08144)
Abhitosh Yadav (08142)
Department of Civil Engineering
National Institute of Technology,
Hamirpur (H.P.)
SupervisorDr. V.S.Dogra
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Outline of Presentation
Introduction
Objectives of the study
Reservoir under study
Methodology
Model Development
Results
Comparisons
Conclusions
References
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Statistical Models
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Data driven models.
Result in better and comparatively easier simulation for non- linear
relationships.
In recent decades, many researchers have shown a great deal of interest in
statistical / mathematical modeling of various hydrological processes.
Statistical models have performed better then the traditional methods in
modeling various hydrological processes like sediment-discharge
relationships. (Sarkar,2010)
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Objectives of Study
To understand the method of modeling a physical process using the
technique of fuzzy-based model and Artificial Neural Network.
Application of these techniques for inflow modeling of Okhla barrage
using previous yearsdata.
Comparison of efficiency and accuracy of ANN model and fuzzy-
based model.
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Okhla Barrage
Constructed on Yamuna river
Situated at the border of U.P. and Delhi.
Constructed in 1986.
Constructed and maintained by Department of Irrigation, U.P.
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Upstream daily inflow data of years 2000 2010 was received from
Department of Irrigation U.P.
The units of data is cubic feet per second.
This data was then processed using Microsoft Excel .
70% of this data was used for the development of models and rest 30% for
validation and testing.
DATA
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Model development
Two types of models have been developed for prediction of inflow.
1. Short term prediction
2. Long term Prediction
Short term prediction
These models have been developed for short term prediction of inflow.The models have been developed to predict the inflow:
One day in advance: (e.g. inflow on May 15th, 2011 can be predicted on May
14th, 2011);
Three days in advance: (e.g. inflow on May 15th, 2011 can be predicted on
May 12th, 2011);
Seven days in advance(e.g. inflow on May 15th, 2011 can be predicted on
May 8th, 2011);
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Model development
Long term Models
These models have been developed to predict the inflow one year in advance.
(e.g. inflow on May 15th, 2011 can be predicted on May 15th, 2010).
A total number of seven models have been developed using ANN for this
purpose. Similarly, 3 models have been developed using Subtractive
clustering cum fuzzy if-then rule base.
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Model Names
Long term Models: Named on the basis of number of inputs and the method
of Development.
e.g. ANN3 is the model made by ANN using 3 inputs.
FSC-3 is the model made by fuzzy and subtractive clustering using 3 inputs.
Short Term Models: Named on the basis of prediction lag and no. of inputs.
Eg. TS12 is short term prediction model made by time series tool
which can predict inflow 1 day in advance and uses 2 inputs.
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Inputs
Past inflow values has been used as input(independent variable) for
prediction of inflow at given time(dependent variable).
These input parameters were selected by studying visible patterns in
the data or on hit and trail basis.
Different models were developed using variable no. of input
parameters and variable no. of hidden layers(ANN).
Campolo et al. (1999) suggested that the capacity of a model to
generalize inflow is more accurate when discharge values are used as
input to the model. 13
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Inputs
ANN1: The input which is used for this model is the 1 year lagged inflow
value. E.g. for prediction of inflow on 15thMay, 2010, input- 15thMay, 2009.
ANN 2: In ANN 2, the inputs used are 1-year and 2- year lagged inflow
values. E.g. to predict the inflow on 15th
May 2010, the inputs -15th
May,
2009 and 15thMay, 2008.
ANN 3: In ANN 3 model, the inputs are 1 year, 2 year and 3 year lagged
inflow values. E.g. To predict the inflow of 15thMay 2010,the inputs 15th
May 2009, 15thMay 2008 and 15thMay 2007.
ANN 4: In ANN4, the inputs are 1-year, 2-year, 3-year and 4-year lagged
inflow values. E.g. To predict the inflow of 15thMay 2010, the inputs - 15th
May 2009, 15thMay 2008 , 15thMay 2007 and 15thMay 2006.
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Inputs
5. ANN 5:In this model, the inputs are 1year, 2year, 3year, 4year lagged inflow
values and monthly average inflow value of corresponding month lagged by 1
year. E.g. the prediction of inflow on 15thMay, 2010 ,inputs-15thMay, 2009,
15thMay, 2008, 15thMay 2007, 15thMay 2006 and average inflow value of
May, 2009.
6. ANN 8:In this model, the inputs are 1year, 2year, 3year, 4year lagged inflow
values and monthly average inflow value of corresponding month lagged by 1
year, 2year, 3year and 4year.e.g. The prediction of inflow on 15 thMay 2010
depends on the inflow values of 15thMay 2009, 15thMay 2008, 15thMay 2007,
15thMay 2006 and average inflow value of May 2009, May 2008, May 2007
and May 2006.15
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Inputs
7. ANN 12:In this model, the inputs are 1-year, 2-year, 3-year and 4-year
lagged inflow values of same day, monthly average inflow value of
corresponding month lagged by 1-year, 2-year, 3-year and 4-year and annual
average inflow value lagged by 1-year, 2-year, 3-year and 4-year.
E.g. the prediction of inflow on 15thMay 2010 depends on the inflow values of
15thMay 2009, 15thMay 2008, 15thMay 2007, 15thMay 2006 plus average
inflow value of May 2009, May 2008, May 2007 and May 2006 and average
inflow value of 2009, 2008, 2007 and 2006.
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Inputs
Fuzzy based Models
The inputs used for the fuzzy models were same as that of corresponding
model of ANN. i.e. the inputs for FSC-3 were same as that of ANN3 and so
on.
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EVALUATION CRITERIA
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EVALUATION CRITERIA
4. Regression coefficient: Directly obtained using Neural- fitting tool box.
5. Pearson Correlation coefficient (R): It gives a measure of correlation
between the Actual and Predicted inflow dataset. Its value lies between -1
to +1. Values near to zero depicts poor model Performance. Its value is
calculated as follows.
Where Xiis the observed inflow, Yiis the predicted in flow.
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ANN12 : Regression Values
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RESULTS- ANN Long Term Models
Model NRMSE % RMSE R CE Rc
ANN1 .204 33.22 0.14 0.05 0.288
ANN2 0.193 31.5 0.30 0.05 0.39
ANN3 0.17 27.8 0.55 0.26 0.56
ANN4 0.17 27.0 0.56 0.22 0.61
ANN5 0.15 24.8 0.66 0.41 0.71
ANN8 0.11 18 .4 0.83 0.68 0.88
ANN12 0.10 19.1 0.84 0.70 0.89
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Table 5.1 Performance criteria for long term prediction ANN models
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ANN12- Predicted Inflow
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RESULTS-ANN short term model
Model NRMSE % RMSE R CE
TS12 0.12 23.5 0.92 0.86
TS13 0.07 13.4 0.93 0.86
TS15 0.07 13.9 0.92 0.85
TS17 0.07 13.2 0.93 0.86
TS32 0.11 22.7 0.80 0.61
TS33 0.11 20.1 0.83 0.69
TS34 0.11 20.7 0.82 0.67
TS37 0.11 21.1 0.83 0.66
TS72 0.20 39.7 0.39 0.21
TS77 0.14 27.2 0.68 0.43
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Table 5.2 Performance criteria for short-term prediction ANN models
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TS17- Time Series Prediction
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Results- Fuzzy models
Model NRMSE % RMSE R CE
FSC-3 0.24 25.3 0.25 0.56
FSC-5 0.17 19.3 0.64 0.40
FSC-8 0.15 15.7 0.84 0.59
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Table 5.3 Performance criteria for fuzzy based models
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FSC-8 FIS- Editor
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FSC-8 Final Model
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Comparisons
1. Hidden Layers: this comparison was done on model ANN4
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Comparisons
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Training Algorithm: Comparison done on Model ANN3
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Comparison: ANN long term prediction model
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ANN vs. Fuzzy
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Model NRMSE % RMSE R CE
FSC-3 0.24 25.3 0.25 -0.56
ANN3 0.17 27.8 0.55 0.26
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ANN vs. Fuzzy
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Performance of ANN 3 and FSC-3 in modelling inflow into barrage
for december,2010
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Conclusion
Long term Prediction model
Model ANN12 was found in terms of performance with regression coefficient
value as high as 0.93 during training.
The Performance of model also increases with increase in number of neurons in
hidden layer, but it tends to increase the processing time for training.
The optimum number of neurons was found to be in range of 100 during this
study.
The fuzzy and ANN models do not show much of difference in the performancefor same set of inputs
once the number of inputs was increased beyond eight, the fuzzy based models
started giving incoherent results.34
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Conclusion
Short term Prediction Models
The accuracy of these models was found out to be relatively higher than
that of long term prediction models
The accuracy of one day lagged models was found to be maximum with
regression value greater than 0.90.
Accuracy of prediction models decreased as the Lag was increased.
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References
Subribabu ,C.R., Ramayya,V.V. and Kumar,R.P. (2005). Application of
Artificial Neural Network Model for Inflow Prediction,Journal of Indian Water
Resources Society,Vol.35, No.4, PP. 9-24.
Sarkar, A., Raju ,M.M. and Kumar,A. (2010). Sediment Runoff Modelling
Using Artificial Neural Networks, Journal of Indian Water Resources SocietyVol.30, No.1, PP. 39-45.
Sarkar, A., Agarwal, A. and Singh, R.D. (2006). Artificial Neural Networks
Models for Rainfall Runoff Forecasting In a Hilly Catchment, Journal of
Indian Water Resources Society , Vol.26, No.2, PP. 5-12.
Adamowski, J. and Karapatki, C. (2010). Comparison of Multivariate
Regression and Artificial Neural Networks for Peak Urban Water Demand
Forecasting: Evaluation of Different ANN Learning Algorithm,ASCE Journal of
Hydrological Engineering , Vol.15, No.10, PP. 729-743.
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EI-Shafie, A., Taha, M.R. and Noureldin ,A. (2007). A neuro-fuzzy model for
inflow forecasting of the Nile river at Aswan high dam,Water Resource Manage,
Vol.21, PP. 533-556.
Deka, P.C. and Chandramouli,V. (2009). Fuzzy Neural Network Modeling of
Reservoir Operation, ASCE Journal of Water Resources Planning and
Management, Vol.135, No.1,PP. 5-12.
Googhari, S.K., Feng, H.Y., Ghazali ,A.H. and Shui, L.T. (2010). Neural
Network for Forecasting Daily Reservoir Inflows, Pertanik J.Science &
Technology, Vol.18,No.1, PP. 33-41.
Othman, F. and Naseri, M. (2011). Reservoir inflow forecasting using artificial
neural network, International Journal of the Physical Science,
Vol.6,No.3,PP.434-440.
References