Rainfall Prediction Using k-NN Based Similarity Measure

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 266)

Abstract

Time Series Analysis is helpful in understanding past and current pattern of the phenomenon and provides important clue for predicting future trends. Weather forecasting is a matter of great importance in the area of Time Series Analysis. The purpose of this paper is to predict the monthly rainfall based on the historical dataset using pattern similarity based models together with k-NN technique and to compare the estimated values with the actual observations. We have applied a recently proposed approach Approximation and Prediction of Stock Time-series data (APST) for forecasting rainfall. Further we have also suggested two variations of APST. We have also compared the results of similarity based methods with Autoregressive Model. Suggested techniques yield better results than original APST and AR Models.

Keywords

APST Auto regression Forecast Rainfall Pattern similarity k-NN Time series 

References

  1. 1.
    Burlando, P., Rosso, R., Cadavid, L.G., Salas, J.D.: Forecasting of short-term rainfall using ARMA model. J. Hydrol. 144(1–4), 193–211 (1993)CrossRefGoogle Scholar
  2. 2.
    Valipour, M.: Number of required observation data for rainfall forecasting according to climate conditions. Am. J. Sci. Res. 74, 79–86 (2012)Google Scholar
  3. 3.
    KhadarBabu, S.K., Karthikeyan, K., Ramanaiah, M.V., Ramanah, D.: Prediction of rainfall-flow time series using auto-regressive models. Adv. Appl. Sci. Res. 2(2), 128–133 (2011)Google Scholar
  4. 4.
    Henley, B.J., Thyer, M.A., Kuczera, G.: Seasonal stochastic rain fall modelling using climate indices: a Bayesian hierarchical model. In: International Congress on Modelling and Simulation, pp. 1575–1581 (2007)Google Scholar
  5. 5.
    Verbist, K., Robertson, A.W., Cornelis, W.M., Gabriels, D.: Seasonal predictability in daily rainfall characteristics in central northern Chile for dry-land management. J. Appl. Meteorol. Climatol. 49, 1938–1955 (2010)CrossRefGoogle Scholar
  6. 6.
    Sharma, A. Bose, M.: Seasonality and rainfall prediction. In: Seventh International Conference on Data Mining and Warehousing (ICDMW), pp. 145–150, Bangalore (2013)Google Scholar
  7. 7.
    Wu, A.: A novel artificial neural network ensemble model based on K-nearest neighbor nonparametric estimation of regression function and its application for rainfall forecasting. Comput. Sci. Optim. 2, 44–48 (2009)Google Scholar
  8. 8.
    Jan, Z., Abrar, M., Bashir, S., Mirza, A.M.: Seasonal to Inter Annual Prediction Using Data Mining K-NN Technique. CCIs. vol. 20, pp. 40–51, Springer, New York (2008)Google Scholar
  9. 9.
    Olaiya, F., Adeyemo, A.B.: Application of Data Mining Techniques in Weather Prediction and Climate Change Studies. Int. J. Inf. Eng. Electr. Bus. 1, 51–59 (2012)Google Scholar
  10. 10.
    Wu, C.L., Chau, K.W., Fan, C.: Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J. Hydrol. 389(1–2), 146–167 (2010)CrossRefGoogle Scholar
  11. 11.
    Luc, K.C., Ball, J.E., Sharma, A.: An application of artificial neural network for rainfall forecasting. Math. Comput. Modell. 33(6–7), 683–693 (2001)Google Scholar
  12. 12.
    Ramirez, M.C.V., Velho, H.F.D.C., Ferreira, N.J.: Artificial neural network technique for rainfall forecasting applied to Sao Paulo region. J. Hydrol. 301(1–4), 146–162 (2005)Google Scholar
  13. 13.
    Fallah-Ghalhary, G.A., Mousavi-Baygi, M., Habibi-Nokhandan, M.: Seasonal rainfall forecasting using artificial neural network. J. Appl. Sci. 9(6), 1098–1105 (2009)CrossRefGoogle Scholar
  14. 14.
    Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N.K.: An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol. Earth Syst. Sci. Dis. 5(1), 183–218 (2008)CrossRefGoogle Scholar
  15. 15.
    Sivaramakrishnan, T.R., Meganathan, S.: Association rule mining and classifier approach For quantitative spot rainfall prediction. J. Theor. Appl. Inf. Technol. 34(2), 173–177 (2011)Google Scholar
  16. 16.
    Pao-Shan, Y., Shien Tsang, C., Che-Chuan, W., Shu-Chen, L.: Comparison of grey and phase-space rainfall forecasting models using a fuzzy decision method. Hydrol. Sci. 49(4), 655–672 (2004)Google Scholar
  17. 17.
    Suwardi, A., Takenori, K., Shuhei, K.: Neuro-fuzzy approaches for modeling the wet season tropical rainfall. Agric Inf. Res. 15(3), 331–341 (2006)Google Scholar
  18. 18.
    Fall, G.A., Mousavi-Ba, M., HabibiNok, M.: Annual rainfall forecasting by using Mamdani fuzzy inference system. Res. J. Environ. Sci. 3(4), 400–413 (2009)CrossRefGoogle Scholar
  19. 19.
    Wu, C.L., Chau, K.W.: Prediction of rainfall time series using modular soft computing methods. Eng. Appl. Artif. Intell. 26(3), 997–1007 (2013)CrossRefGoogle Scholar
  20. 20.
    Wong, K.W., Wong, P.M., Gedeon, T.D., Fung, C.C.: Rainfall prediction model using soft computing technique. Soft Comput.-SOCO. 7(6), 434–438 (2003)Google Scholar
  21. 21.
    Vishwanath, R.H., Leena, S.V., Srikantaiah, K.C., Shreekrishna Kumar, K. Deepa Shenoy, P., Venugopal, K.R., Patnaik, L.M.: APST: Approximation and prediction of stock time-series data using pattern sequence. In: Venugopal, K.R., Deepa Shenoy, P., Patnaik, L.M (eds.) Seventh International Conference on Data Mining and Warehousing (ICDMW), pp. 151–160. Elsevier Publications, Bangalore (2013). ISBN: 978-93-5107-105-1Google Scholar
  22. 22.
  23. 23.
    Dickey, D.A., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74(366), 427–431 (1979) JSTOR 2286348Google Scholar
  24. 24.
    Martínez-Alvarez, F., Troncoso, A., Riquelme, J.C., Aguilar Ruiz, J.S.: LBF: a label-based forecasting algorithm and its application to electricity price time series. In: IEEE International Conference on Data Mining (2008)Google Scholar
  25. 25.
    Lian, X., Chen, L., Lian, X., Xu Yu, Jeffrey, Han, J., Ma, J.: Multiscale representations for fast pattern matching in stream time series. IEEE Trans. Knowl. Data Eng. 21(4), 568–581 (2009)CrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Delhi UniversityNew DelhiIndia

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