INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.5, No.12, Nov. 2013

Prediction of Rainfall in India using Artificial Neural Network (ANN) Models

Full Text (PDF, 1285KB), PP.1-22


Views:462   Downloads:68

Author(s)

Santosh Kumar Nanda, Debi Prasad Tripathy, Simanta Kumar Nayak, Subhasis Mohapatra

Index Terms

Autoregressive Integrated Moving Average Model, ARIMA, Autocorrelation Function, FLANN, MLP, Legendre neural Network (LeNN)

Abstract

In this paper, ARIMA(1,1,1) model and Artificial Neural Network (ANN) models like Multi Layer Perceptron (MLP), Functional-link Artificial Neural Network (FLANN) and Legendre Polynomial Equation ( LPE) were used to predict the time series data. MLP, FLANN and LPE gave very accurate results for complex time series model. All the Artificial Neural Network model results matched closely with the ARIMA(1,1,1) model with minimum Absolute Average Percentage Error(AAPE). Comparing the different ANN models for time series analysis, it was found that FLANN gives better prediction results as compared to ARIMA model with less Absolute Average Percentage Error (AAPE) for the measured rainfall data.

Cite This Paper

Santosh Kumar Nanda, Debi Prasad Tripathy, Simanta Kumar Nayak, Subhasis Mohapatra,"Prediction of Rainfall in India using Artificial Neural Network (ANN) Models", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.12, pp.1-22, 2013. DOI: 10.5815/ijisa.2013.12.01

Reference

[1]Ming Zhang and Jessica Crane. Rainfall Estimation Using SPHONN Model. May 15-17, Pittsburgh, USA. pp. 1-5. 2000.

[2]Ozlem Terzi. Monthly Rainfall Estimation Using Data-Mining Process. July 2012 Academic Editor: Tzung P. Hong.

[3]D.I.F. Grimes, E. Pardo-Igu´zquiza and R. Bonifacio TAMSAT.Optimal areal rainfall estimation using raingauges and satellite data. July 1999.

[4]Saleh Zakaria, Nadhir Al-Ansari, Sven Knutsson and Thafer Al-Badrany ARIMA Models for weekly rainfall in the semi-arid Sinjar District at Iraq vol. I, no. 4, 2012 25-ISSN: 1792-9040(print), 1792-9660 (online) International Scientific Press, 2011.

[5]Dayhoff E.J. Neural Network Architecture – An Introduction Van Norstand Reilold, New York, 1990. 

[6]M. T.Hagan, H.B.Demuth and M.Beale, Neural Network Design Thomson Asia Pte. Ltd , Singapore,2002

[7]http://www.imd.gov.in/section/hydro/Monsoon.jpg 

[8]http://www.imdpune.gov.in/mons_monitor/all-India.gif

[9]Girish Kumar Jha, Artificial Neural Networks, Indian Agriultural Research Institute, PUSA, New Delhi.

[10]Bose N.K. and Liang P., Neural Network Fundamentals with Graphs, Algorithms, Applications, TMH Publishing Company Ltd, 1998.

[11]Montgomery, D.C and L.A. Johnson, Forecasting and Time Series Analysis. McGraw-Hill Book Company, http://www.abebooks.com/Forecasting- Time Series-Analysis Montgomerouglas /1323032148/bd, McGraw-Hill, (1967).

[12]Pankratz, A., Forecasting With Univariate Box-Jenkins Models Concepts and Cases.John Wiley & Sons, Inc. New York, ISBN0-471-09023-9, pp: 414.

[13]B. Widrow, S.D. Sterns, Adaptive Signal Processing, Prentice-Hall, Inc. Engle-wood Cliffs, New Jersey, 1985. 

[14]S. Haykin, Adaptive Filter Theory, Pearson Education Asia, 4th edition, 2002. 

[15]S. Haykin, Neural Networks: A comprehensive foundation, Pearson Education Asia, 2nd Edition, 2002.

[16]J. C. Patra, W. C. Chin, P. K. Meher and G. Chakraborty, Legendre-FLANN-based nonlinear channel equalization in wireless communication system, in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1826–1831, Singapore, October, 2008.

[17]S.K. Nanda, S.Panda, P Raj Sekhar Subudhi and R. K. Das A Novel Application of Artificial Neural Network for the Solution of Inverse Kinematics Controls of Robotic Manipulators, International Journal of Intelligent Systems and Applications, (IJISA), Hongkong, Vol.4, Issue 9, pp-81-91, 2012.

[18]S.K.Nanda and D.P.Tripathy, Application of Functional Link Artificial Neural Network for Noise Prediction in Mining Industry, International Journal of Advances of Fuzzy Logic System, USA, 2011, http://dx.doi.org/10.1155/2011/831261.

[19]Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, 2nd edition, JohnWiley & Sons, INC.2001. 

[20]Y.H. Pao, Adaptive Pattern Recognition and Neural Networks, Addison Wesley, Reading, Massachusetts, 1989.

[21] Y.H. Pao, S. M. Phillips,D. J. Sobajic, Neural-net computing and intelligent control systems,Int. J. Conr. , vol. 56, no.2, pp.263-289, 1992 

[22]Y.H. Pao, G.H. Park and D.J. Sobjic, Learning and Generalization Characteristics of the Random Vector function, Neuro Computation, vol.6, pp.163-180, 1994.

[23] Fu L.M., Hsu H.H. and Principe J.C., Incremental Back Propagation Learning Networks, IEEE Trans. on Neural Network, vol.7, no.3, pp.757-762, 1996. 

[24]Zhao Q and Higuchi T., Evolutionary Learning of Nearest-Neighbor MLP, IEEE Trans. on Neural Network, vol.7, no.3, pp.762-768, 1996. 

[25]J.-S.R. Jang, C.-T. Sun and E. Mizutan, Neuro-Fuzzy and Soft Computing, Prentice Hall of IndiaPrivate Limited, New Delhi, 2005.

[26]S. Haykin, Adaptive Filter Theory, Pearson Education, Inc., New Delhi, 2002.

[27]S.Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, Reading, MA, 1994.

[28]M. Hagan, H. Demuth and M. Beale, Neural Network Design, Thomson Learning, New Delhi, 2003.

[29]K. S. Narendra and K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Network., 1, 4–27, 1990.

[30]Z. Xiang, G. Bi and T.-L. Ngoc, Polynomial perceptrons and their applications to fading channel equalization and co-channel interference suppression, IEEE Trans. Signal Processing, 42, Pp 470–2479, 1994.

[31]J.C. Patra and R.N. Pal, Functional link neural network-based adaptive equalization of nonlinear channels with QAM signal, In: Proceedings of IEEE International Conference on Systems, Man, Cybernetics (SMC1995), Vancouver, BC, Canada, October, pp. 2081–2086, 1995.

[32]J.-S. R. Jang, C.-T. Sun and E. Mizutan Neuro-Fuzzy and Soft Computing, Prentice Hall of India Private Limited, New Delhi, India, 2005.

[33]S. Haykin, Adaptive Filter Theory, Pearson Education, Inc., New Delhi, India, 2002.

[34]S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, USA, 1994.

[35]M. Hagan, H. Demuth and M. Beale, Neural Network Design, Thomson Learning, New Delhi, India, 2003.

[36]J. C. Patra and R. N. Pal, Functional link artificial neural network-based adaptive channel equalization of nonlinear channels with QAM signal, In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2081–2086, 1995.

[37]J. C. Patra and R. N. Pal, A functional link artificial neural network for adaptive channel equalization, Signal Processing, vol. 43, no. 2, pp. 181–195, 1995.

[38]J. C. Patra, R. N. Pal, R. Baliarsingh and G. Panda, Nonlinear channel equalization for QAM signal constellation using artificial neural networks, IEEE Transactions on Systems,Man, and Cybernetics, Part B, vol. 29, no. 2, pp. 262–271, 1999.