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Reseach Article

On the Prediction of Average Monsoon Rainfall in Bangladesh with Artificial Neural Network

by Md. Habibur Rahman, M.A. Matin
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 5
Year of Publication: 2015
Authors: Md. Habibur Rahman, M.A. Matin
10.5120/ijca2015906392

Md. Habibur Rahman, M.A. Matin . On the Prediction of Average Monsoon Rainfall in Bangladesh with Artificial Neural Network. International Journal of Computer Applications. 127, 5 ( October 2015), 45-52. DOI=10.5120/ijca2015906392

@article{ 10.5120/ijca2015906392,
author = { Md. Habibur Rahman, M.A. Matin },
title = { On the Prediction of Average Monsoon Rainfall in Bangladesh with Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 5 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 45-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number5/22729-2015906392/ },
doi = { 10.5120/ijca2015906392 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:19:08.073372+05:30
%A Md. Habibur Rahman
%A M.A. Matin
%T On the Prediction of Average Monsoon Rainfall in Bangladesh with Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 5
%P 45-52
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The monsoon rainfall has very important affect on the agricultural production, livestock as well as human ecology. In this study, try to fit Artificial Neural Network (ANN) model to predict average monsoon rainfall. For the ANN models monthly average rainfall, sea surface temperature, wind speed, monsoon rainfall, temperature is used as inputs to predict the monsoon rainfall. The feed forward network was trained using a variety of algorithms. For the different networks use sigmoid transfer function, tan sigmoid transfer function and linear transfer function. The total sample was divided into a training set (first 75 percent) and a testing set (last 25 percent). The data pertaining to the years 1961 to 2005 have been explored to develop the predictive models. The model performance is measured by prediction error, mean square error, root mean square error, correlation, similarity, mean percentage error and mean absolute percentage error. Finally, the prediction performance of artificial neural network has compared with polynomial curve fitting, Fourier series, auto regressive moving average model (ARMA), and multiple linear regressions. The average monsoon rainfall prediction based on Artificial Neural Network was found to be superior to that based on polynomial curve fitting, multiple linear regression, ARMA model and Fourier series. Finally, made cluster analysis between actual average monsoon rainfall and predicted average monsoon rainfall by different ANN and other statistical models. From the dendrogram, it is evident that the actual monsoon rainfall and predicted rainfall by ANN fall in one cluster. The ANN model gives more accurate prediction compared to other models.

References
  1. Anil K. Jain, Jianchang Mao and Mohiuddin .K.M, “Artificial neural networks: a tutorial” Michigon State University March (1999)
  2. Arora S. and Khot S (2003)., “Fitting algebraic curves to noisy data,” Journal of Computer and System Sciences 67, 325–340.
  3. Brown, B.G., and A.H. Murphy, (1988), “The economic value of weather forecasts in wildfire suppression mobilization decisions”, Canadian J. Forest Res. 18, 1641-1649.
  4. Cartalis, C., and C. Varotsos, (1994), Surface ozone in Athens, Greece, at the beginning and at the end of the 20th-century, Atmos. Environ. 28, 3-8.
  5. Chapman & Hall/CRCChernov N. and Lesort C, (2004), “Statistical efficiency of curve fitting algorithms” Computational Statistics & Data Analysis 47, 713 – 728.
  6. Chatfield, C., (2004), “The analysis of time series, an introduction”, sixth edition: New York,
  7. Chernov N. and Lesort C, (2004), “Statistical efficiency of curve fitting algorithms” Computational Statistics & Data Analysis 47, 713 – 728.
  8. Elsner, J.B., and A.A. Tsonis, (1992), Non-linear prediction, chaos, and noise, Bull. Am. Me-teor. Soc. 73, 49-60.
  9. Gardner, M.W., and S.R. Dorling, (1998), “Artificial Neural Network (Multilayer Perceptron) – A review of applications in atmospheric sciences”, Atmos. Environ. 32, 2627-2636.
  10. Helmut Lutkepohl and Markus Kratzig, (2004) “Applied Time Series Econometrics” Cambridge University Press 2004 ISBN 052183919X
  11. Hsieh, W.W., and T. Tang, (1998), Applying neural network models to prediction and data analysis in meteorology and oceanography, Bull. Am. Meteor. Soc. 79, 1855-1869.
  12. Hu, M.J.C., (1964), Application of ADALINE system to weather forecasting, Technical Report, Stanford Electron, Stanford, CA.
  13. Jacovides, C.P., C. Varotsos, N.A. Kaltsounides, M. Petrakis and D.P. Lalas, (1994), Atmospheric turbidity parameters in the highly polluted site of Athens basin, Renewable Energy 4, 5, 465-470.
  14. Kalogirou, S.A., C.N. Constantinos, S.C. Michaelides and C.N. Schizas, (1997), A time series construction of precipitation records using Artificial Neural Networks, EUFIT ’97, September 8-11, 2409-2413.
  15. Kondratyev, K.Y., and C.A. Varotsos,(2001a), Global tropospheric ozone dynamics – Part I: Tropospheric ozone precursors, Environ. Sci. Pollution Res. 8, 1, 57-62.
  16. L. Ljung, (1987) “System Identification: Theory for the User”, Prentice Hall, Engle wood Cliis, NJ).
  17. Men, B., Z. Xiejing and C. Liang,(2004), Chaotic analysis on monthly precipitation on Hills Region in Middle Sichuan of China, Nature and Science 2, 45-51.
  18. Michaelides, S.C., C.C. Neocleous and C.N. Schizas, (1995), Artificial Neural Networks and multiple linear regression in estimating missing rainfall data, Proc. DSP95 Intern. Confer. “Digital Signal Processing”, Limassol, Cyprus. 668-673.
  19. Pappas S. SP., Ekonomou, L. Karampelas P., Katsikas S.K and Liatsis. P., ”modelling of round water resistance variation using ARMA models,” Simulation Modelling Practice and Theory 16 (2008) 560–570.
  20. Sivakumar, B., S.Y. Liong, C.Y. Liaw and K.K. Phoon, (1999), Singapore rainfall behavior: Chaotic, J. Hydrol. Eng., ASCE 4, 38-48.
  21. Sivakumar, B., (2000), Chaos theory in hydrology: important issues and interpretations, J. Hy-drology 227, 1-20.
  22. Surajit C. and Manojit C. (2007), “A Soft Computing technique in rainfall forecasting”, International Conference on IT, HIT, March 19-21, 2007
  23. Varotsos, C., (2005), Power-law correlations in column ozone over Antarctica, Intern. J. Remote Sensing 26, 3333-3342.
  24. Varotsos, C., and D. Krik-Davidoff, (2006), Long-memory processes in ozone and temperature variations at the region 60 degrees S-60 degrees N, Atmos. Chem. Phys. 6, 4093-4100.
  25. Varotsos, C., K.Y. Kondratyev and M. Efstathiou, (2001), On the seasonal variation of the surface ozone in Athens, Greece, Atmos. Environ. 35, 2, 315-320.
  26. Wilks, D.S., (1991), Representing serial correlation of meteorological events and forecasts in dynamic decision-analytic models, Month. Weather Rev. 119, 1640-1662.
  27. Wong, K.W., P.M. Wong, T.D. Gedeon and C.C. Fung, (1999), Rainfall prediction using neural fuzzy technique, URL:www.it.murdoch.edu.au/~wong/publications/SIC97.pdf, 213-221.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks Similarity Cluster analysis Activation function Prediction.