Application of Artificial Neural Networks in Short-Term Rainfall Forecasting

Chapter

Abstract

Short-term rainfall is important in agriculture, industry, the energy sector, and any other water-dependent activities where profitability depends on climatic conditions. The scarcity of reliable prediction models encouraged the authors of the present study to develop a modeling platform using a neurogenetic model to estimate rainfall occurrence within a short-term duration. The data on both the quantity and the probability of occurrence of rainfall based on the previous 1–5 days were used to predict the quantity and occurrence of rainfall 1–4 days hence. The potential of neurogenetic models to predict short-term rainfall on the basis of such a small-scale data set was analyzed with the aim of developing a software platform for laypeople and to help related professionals maintain the profitability of their organization by reducing the likelihood of wastage resulting from large-scale prediction errors, which are common with the available linear models. The results indicate that neurogenetic models can reliably predict rainfall 1, 3, and 4 days in advance, but not 2 and 5 days, if the models are trained with a suitable algorithm. The subpar performance of the 2- and 5-day rainfall prediction models was attributed to the choice of training algorithms and length of time, although the reliable prediction of rainfall even 1 day in advance warrants pursuing further development of the present investigation.

Keywords

Rainfall Event Neural Network Model Kappa Index Stochastic Neural Network Quick Propagation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Alvisi S, Franchini M (2012) Grey neural networks for river stage forecasting with uncertainty. Phys Chem Earth Pt A/B/C 42–44:108–118CrossRefGoogle Scholar
  2. Bodri L, Čermák V (2000) Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia. Adv Eng Softw 31(5):311–321CrossRefGoogle Scholar
  3. Burlando P, Rosso R, Cadavid LG, Salas JD (1993) Forecasting of short-term rainfall using ARMA models. J Hydrol 144(1–4):193–211CrossRefGoogle Scholar
  4. French MN, Bras RL, Krajewski WF (1992) A Monte Carlo study of rainfall forecasting with a stochastic model. Stoch Hydrol Hydraul 6(1):27–45CrossRefGoogle Scholar
  5. Gautam MR, Watanabe K, Ohno H (2004) Effect of bridge construction on floodplain hydrology—assessment by using monitored data and artificial neural network models. J Hydrol 292(1–4):182–197CrossRefGoogle Scholar
  6. Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput 7(2):585–592CrossRefGoogle Scholar
  7. Khashei M, Hamadani AZ, Bijari M (2012) A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Syst Appl 39(3):2606–2620CrossRefGoogle Scholar
  8. Kim J-W, Pachepsky YA (2010) Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. J Hydrol 394(3–4):305–314CrossRefGoogle Scholar
  9. Kisi O, Cimen M (2012) Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng Appl Artif Intel 25(4):783–792CrossRefGoogle Scholar
  10. Kisi O, Ozkan C, Akay B (2012) Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. J Hydrol 428–429:94–103CrossRefGoogle Scholar
  11. Kottegoda NT, Natale L, Raiteri E (2003) A parsimonious approach to stochastic multisite modelling and disaggregation of daily rainfall. J Hydrol 274(1–4):47–61CrossRefGoogle Scholar
  12. Lekouch I, Lekouch K, Muselli M, Mongruel A, Kabbachi B, Beysens D (2012) Rooftop dew, fog and rain collection in southwest Morocco and predictive dew modeling using neural networks. J Hydrol (in press), Accepted manuscript, Available online 13 Apr 2012Google Scholar
  13. Manzato A (2007) Sounding-derived indices for neural network based short-term thunderstorm and rainfall forecasts. Atmos Res 83(2–4):349–365CrossRefGoogle Scholar
  14. Olsson J, Uvo CB, Jinno K (2001) Statistical atmospheric downscaling of short-term extreme rainfall by neural networks. Phys Chem Earth Pt B Hydrol Ocean Atmos 26(9):695–700CrossRefGoogle Scholar
  15. Pan T-y, Wang R-y (2004) State space neural networks for short term rainfall-runoff forecasting. J Hydrol 297(1–4):34–50CrossRefGoogle Scholar
  16. Papalexiou S-M, Koutsoyiannis D, Montanari A (2011) Can a simple stochastic model generate rich patterns of rainfall events? J Hydrol 411(3–4):279–289CrossRefGoogle Scholar
  17. Piotrowski AP, Rowinski PM, Napiorkowski JJ (2012) Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers. Expert Syst Appl 39(1):1354–1361CrossRefGoogle Scholar
  18. Sugimoto S, Nakakita E, Ikebuchi S (2001) A stochastic approach to short-term rainfall prediction using a physically based conceptual rainfall model. J Hydrol 242(1–2):137–155CrossRefGoogle Scholar
  19. Thielen J, Boudevillain B, Andrieu H (2000) A radar data based short-term rainfall prediction model for urban areas — a simulation using meso-scale meteorological modeling. J Hydrol 239(1–4):97–114CrossRefGoogle Scholar
  20. Zhao L, Hicks FE, Robinson Fayek A (2012) Applicability of multilayer feed-forward neural networks to model the onset of river breakup. Cold Reg Sci Technol 70:32–42CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of Hydro-Informatics EngineeringNational Institute of Technology Agartala, BarjalaJiraniaIndia
  2. 2.Department of Production EngineeringNational Institute of Technology Agartala, BarjalaJiraniaIndia

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