CFP last date
22 April 2024
Reseach Article

Design of Neural Network models for Daily Rainfall Prediction

by N. A. Charaniya, S. V. Dudul
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 14
Year of Publication: 2013
Authors: N. A. Charaniya, S. V. Dudul
10.5120/9997-4858

N. A. Charaniya, S. V. Dudul . Design of Neural Network models for Daily Rainfall Prediction. International Journal of Computer Applications. 61, 14 ( January 2013), 23-27. DOI=10.5120/9997-4858

@article{ 10.5120/9997-4858,
author = { N. A. Charaniya, S. V. Dudul },
title = { Design of Neural Network models for Daily Rainfall Prediction },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 14 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number14/9997-4858/ },
doi = { 10.5120/9997-4858 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:09:07.376249+05:30
%A N. A. Charaniya
%A S. V. Dudul
%T Design of Neural Network models for Daily Rainfall Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 14
%P 23-27
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rainfall is a random process and prediction of rainfall requires consistent as well as relevant information of meteorological and environmental data. In this paper, two different artificial neural networks models are proposed for consecutive daily rainfall prediction on the basis of the preceding events of rainfall data. Model designed for rainfall forecast is based on the pattern recognition methodology. In this method relevant spatial and temporal feature of rainfall series in past are extracted. These features are then utilized to predict the rainfall in future. Time lag delay neural network has capability to learn from the past event and predict the next value. Rainfall prediction is done on basis of rainfall on previous day to rainfall for the preceding six days. The proposed network is capable of forecasting daily rainfall one day in advance with accuracy of R2 = 0. 96 and NMSE = 0. 0005.

References
  1. Sulochana Gadgil and Siddhartha Gadgil, The Indian Monsoon, GDP and agriculture, Economic and Political Weekly, XLI, pp. 4887–4895, 2006.
  2. Rajeevan M. , "Prediction of Indian summer monsoon: Status, problems and prospects". Curr. Sci. , 2001, 81, pp. 1451–1457.
  3. Thapliyal, V. and Rajeevan, M. , "Monsoon prediction". Encyclopedia of Atmospheric Sciences (ed. Holton, J. ), Academic Press, New York, 2003, pp. 1391–1400.
  4. Sikka, D. R. , 1980, Some aspects of the large scale fluctuations of summer monsoon rainfall over India in relation to fluctuations in the planetary and regional scale circulation parameters, Proc. Ind. Acad. Sciences (Earth and Planet Sci), 89, 179-195.
  5. F. W. Zwiers, J. V. Storch,"On the role of statistics in climate research," Int. J. Climatology, 2004 Vol. 24, pp. 665-680.
  6. Kin C. Luk, J. E. Ball AND A. Sharma, 2001, An Application of Artificial Neural Networks for Rainfall Forecasting, Mathematical and Computer Modelling 33 pp. 883-699.
  7. Poggio,T. , and F. Girosi, 1990, "Networks for approximation and learning," Proc. IEEE, vol. 78, pp. 1481–1497.
  8. Sven F. Crone: A Business Forecasting Competition Approach to Modeling Artificial Neural Networksfor Time Series Prediction. IC-AI 2004, pp. 207-213.
  9. K. Hornik, M. Stinchcombe and H. White, Multilayer feedforward networks are universal approximators, Neural Networks 1989 2, pp. 359-366.
  10. M. French, W. Krajewski and R. R. Cuykendall, Rainfall forecasting in Space and time using a neural network, Journal of Hydrology 1992 137, 1-31.
  11. K. L. Hsu, V. Gupta and S. Soroshian, Artificial neural network modeling of the rainfall-runoff process, Water Resources Research l995 31 (10), pp. 2517-2530.
  12. Werbos, P. J. , 1990, "Backpropagation through time:What it does and how to do it," Proc. IEEE, vol. 78, pp. 1550–1560.
  13. Rumelhart, D. E. , Hinton G. E. , and Williams R. J. , 1986, "Learning internal representations by error propagation," in D. E. Rumelhart and J. L. McCleland, eds. (Cambridge, MA: MIT Press), vol. 1, Chapter 8.
  14. Jiansheng. Wu, Long. Jin," Forecast Research and Applying of BP Neural Network Based on Genetic Algorithms," Mathematics in Practice and Theory, 2005, vol. 35(1), pp. 83-88.
  15. D. Silverman, J. A. Dracup,"Artificial Neural networks and long-lead precipitation prediction in California," Journal of applied meteorology, 2000 vol. 39, pp. 57-66.
  16. G. J. Bowden, G. C. Dandy and H. R. Maier. "Input determination for neural network models in water resources applications. Part 1-background and methodology,. " Journal of Hydrology, 2005 vol. 301(1-4), pp. 75-92.
  17. Wan, E. A. , 1994, "Time series prediction by using a connectionist network with internal delay lines," in A. S. Weigend and N. A. Gershenfield, eds. , Time Series Prediction: Forecasting the Future and Understanding the Past (Reading, MA: Addison-Wesley), pp. 195–217.
  18. Bogardi I, Matyasovszky I, Bardossy A, Duckstein L 1993 Application of a space-time stochastic model for daily precipitation using atmospheric circulation patterns. J Geophys Res 98(D9), pp. 1653–166
  19. Rajeevan M, Bhate J, Kale JD, Lal B 2005 Development of a high resolution daily gridded rainfall data for the Indian region: analysis of break and active monsoon spells. India Meteorological Department
  20. R. Dean, H. Andrew and B. Fiedler, "Forecasting warm season burn-off low clouds at the San Francisco international airport using linear regression and a neural network ," Apply Meteor, 2002 Vol. 41(6), PP. 629-639.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial neural network Time lag neural network Daily Rainfall Prediction