Email this article 
Open Access
Subscription or Fee Access
Performance Evaluation of Artificial Neural Network Model using Data Preprocessing in Non-Stationary Hydrologic Time Series
Abstract
For the planning, land use, design of civil projects and
water resources management, the accurate prediction of hydrological
behaviour in the watershed can provide valuable information.
Hydrologic systems include, to a large extent, stochastic components
and are often non-linear and non-stationary. Inspite of high
adaptability of Artificial Neural Network (ANN) in modelling
hydrologic time series, often signals are highly non-stationary and
exhibit seasonal irregularity. In such cases, prediction accuracy of
ANN suffers for want of pre-processing of data. In this study, different
data pre-processing techniques are presented to deal with irregularity
components that exist in hydrologic time series data of the
Brahmaputra basin within India at the Pancharatna gauging station
using daily time unit and their properties are evaluated by performing
one step ahead flow forecasting using ANN. The model results are
evaluated by using Root mean square error (RMSE)and Mean absolute
percentage error(MAPE) and it was found that Logarithm based
pre-processing technique provides better forecasting performance
among various pre-processing techniques. The results indicate that
detecting non-stationary nature and selecting an appropriate
pre-processing technique is highly beneficial in improving the
prediction performance of ANN model.
Keywords
References
Virili, F. And Freisleben,B.(2000) “Nonstationary and data preprocessing
for neural network predictions of an economic time
series”.,IEEE,129-134.
Nourani, V., Alami, T Mohammad, Aminfar, Mohammad.H (2009). “A
combined neural-wavelet model for prediction of Ligvanchai watershed
precipitation.” Elsevier, Engineering Applications of Artificial
Intelligence, 22, 466–472.
Cannas, B., Fanni, A, Sias, G, Tronchi, S, Zedda, M.K. (2005). “River
flow forecasting using Neural Networks and Wavelet Analysis.” EUG
(2005), European Geosciences Union, Vienna, Ausrtia, vol.7, 24-29.
Nguyen, H.H. and Chan C.W.(2004). “A comparison of data
preprocessing strategies for neural network modelling of oil production
prediction.”In: proceedings of the third IEEE International conference
and cognitive informatics (ICCI’04).IEEE Computer Science.
Kajitani,Y.,Hipel,K.W.,McLeod,A.I(2005). “Forecasting non-linear time
series with feedforward neural networks:A case study of Canadian Lynx
Data.”J.of Forecasting,24,105-117.
Granger,C.W.J.(1994). “Forecasting in Economics;Time series
prediction:Forecasting the future and understanding the
past.N.A-Gershenfeld and
A.S.Weigend(Eds.)Reading,MA:Addison-Wesley,pp.529-538.
Nelson, M.Hill, T, Remus, T., O’Connor, M (1999). “Time series
forecasting using neural networks:Should the data be deseasonalised
first?”J. Of Forecasting,18;359-367.
Zhang,B-L,Coggins,R.,Jabri,M.A.Dersch,D,Flower,B.(2001).
“Multiresolution forecasting for futures trading using wavelet
decompositions.”IEEE Transactions on Neural Networks, 12(4),
-775.
Zhang, G.P., Qi,M.(2005). “Neural Network forecasting for seasonal
and trend time series.”European J.of Operation Research,
(2):501-514.
Zhang, G., Patuwo, B.E. and Michael, Y.H. (1998) “Forecasting with
artificial neural networks”.IEEE, 909-912.
Kaastra, I.Boyed,M.(1996). “Designing a neural network for forecasting
financial and economic time series”, NeuroComputing, 10(3), 215-236.
Plummer,E.A(2000). “Time series forecasting with feedforward neural
networks:Guidelines and limitations”.Master Thesis,University of
Wyoming.
Xu,L. And Chen,W.J.(2001). “Short term load forecasting techniques
using ANN.”In: proceedings of the 2001 IEEE International Conference
of control Applications,157-160.
Lam, M.,(2004) “Neural network techniques for fincial performance
prediction:integrating fundamental and technical analysis”.Decision
Support Systems,37,567-581.
Kong,J.H.L. and Martin,G.P.M.D.,(1995) “A backpropagation neural
network for sales forecasting”.IEEE,2121-2124.
Zhang,Y.(1992). “Prediction of traffic fluctuation in telephone networks
with neural networks,”IEEE,909-912.
Maier, H. R. and Dandy, G. C. (2000). ‘‘Neural networks for the
prediction and forecasting of water resources variables:A review of
modelling issues and applications”.Environmental Modelling &
Software,15,101-124.
Lopes,M.L.M,Minussi,C.R. and Lotufo,A.D.P. “A fast electric load
forecasting using neural networks.”In:proc.43rd IEEE Midwest Symp.
On Circuits and Systems,Learning MI.8-11,August,2000,IEEE,1-4.
Crone,S.F.,Kausch,H.,Prebmar,D.(2004). “Prediction of the cats
benchmark using a business forecasting approach to multilayer
perceptron modeling.IN:Wunsch,D.et.al(Eds.):Proc. Of Int. Joint
Conference on Neural Networks, IJCNN’04, Budapest, Hungary, IEEE,
New York.
Deka, P. And Chandramoulli,V.(2005). “Fuzzy neural network modeling
for hydrologic flow routing.”ASCE J.of Hydrologic Engineering, 2005,
July/August, vol.10(4),pp.302-314
Goswami, D.C.(1985).“Brahmaputra river, Assam, India;
Physiographic, basin denudation and channel aggradation”.Water
Resources Research, 21,959-978.
Sharma, J.N.(2005) “Fluvial process and morphology of the
Brahmaputra river in Assam,India”.Geomorphology,70,226-256.
Duch,W. And Jankowski, N.,(1999), “Survey of neural transfer
functions”. Neural computing surveys 2,163-212.
Sreenivasulu,D. and Deka,Paresh Chandra.(2011) “A comparative study
on RBF and NARX based methods for forecasting of groundwater
level”.Int. J. Earth science and Engg.,vol.04(4),August,743-756.
Refbacks
- There are currently no refbacks.