Call for Paper - January 2022 Edition
IJCA solicits original research papers for the January 2022 Edition. Last date of manuscript submission is December 20, 2021. Read More

Data Mining based Neural Network Model for Rainfall Forecasting

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
P. Arumugam, R. Ezhilarasi
10.5120/ijca2017914831

P Arumugam and R Ezhilarasi. Data Mining based Neural Network Model for Rainfall Forecasting. International Journal of Computer Applications 170(4):30-33, July 2017. BibTeX

@article{10.5120/ijca2017914831,
	author = {P. Arumugam and R. Ezhilarasi},
	title = {Data Mining based Neural Network Model for Rainfall Forecasting},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {4},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {30-33},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume170/number4/28061-2017914831},
	doi = {10.5120/ijca2017914831},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

India is basically an agricultural country and the success or failure of the harvest and water scarcity in any year is always considered with the greatest concern. The average annual or seasonal rainfall at a place does not give sufficient information regarding its capacity to support crop production. Rainfall distribution pattern is the most important. The rainfall forecasting is scientifically and technologically challenging problem around the world in the last century. In this paper Neural Network model was developed for the rainfall forecast performance and the results were compared with Seasonal Auto regressive integrated moving average (SARIMA) model. The performance by (ANN) model and statistical time series model for prediction were examined using visualization technique and statistical test.

References

  1. Bhatnagar S, Lal V, Gupta SD, Gupta OP (2012): Forecasting incidence of dengue in Rajasthan, using time series analyses. Indian J Public Health Vol.2, No.56, pp. 281-285.
  2. Box, G.E.P., and G.M. Jenkins (1976). Time Series Analysis: Forecasting and Control, Second Edition, Holden Day.
  3. Cadenas. E., and W. Rivera. (2010). Wind Speed Forecasting in Three Different Regions of Mexico, Using a Hybrid ARIMA-ANN Model, Renewable Energy, 35, 2732-2738.
  4. Chen, C., Lai, M., and Yeh, C. (2012). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge Based Systems, Vol 26, pp. 281-287.
  5. Esling, P., and C. Agon (2012): Time-Series Data Mining. ACM Computing Surveys, Vol. 45, No.1, pp.1-34.
  6. Fausett, L (1994): Fundamentals of Neural Networks, Prentice Hall, USA.
  7. Freeman, J. A., and D.M. Skapura (1992): Neural Networks Algorithms, Applications and Programming Techniques. Addison-Wesley Publishing Company.
  8. Hansen, J.V., and R.D. Nelson (2002): Data Mining of Time Series Using Stacked Generalizers. Neurocomputing, Vol. 43, pp.173-184.
  9. Lee, T. S., and C. C. Chiu (2002). Neural Network Forecasting of an Opening Cash Price Index. International Journal of Systems Science, 33(3), 229-237.
  10. Narvekar M. and Fargose P. (2015): Daily weather forecasting using artificial neural network International Journal of Computer Applications 121 9-13.
  11. Senthamarai Kannan, K., Deneshkumar, V, and S Arumugam (2013): A Comparative Study on FFNN and ARIMA Model in the Presence of Outliers. International Journal of Computer Applications, Vol 76, No. (17), pp.12-18.
  12. Zhang. G., Patuwo B.E., and Hu. M.Y (1998): Forecasting with artificial neural network: the state of the art, international journal of foresting, Vol 14, PP.35 – 62.

Keywords

Data mining, Neural networks, Time Series, SARIMA, BIC and RMSE.