Seasonal to Inter-annual Climate Prediction Using Data Mining KNN Technique

  • Zahoor Jan
  • Muhammad Abrar
  • Shariq Bashir
  • Anwar M. Mirza
Part of the Communications in Computer and Information Science book series (CCIS, volume 20)

Abstract

The impact of seasonal to inter-annual climate prediction on society, business, agriculture and almost all aspects of human life, force the scientist to give proper attention to the matter. The last few years show tremendous achievements in this field. All systems and techniques developed so far, use the Sea Surface Temperature (SST) as the main factor, among other seasonal climatic attributes. Statistical and mathematical models are then used for further climate predictions. In this paper, we develop a system that uses the historical weather data of a region (rain, wind speed, dew point, temperature, etc.), and apply the data-mining algorithm “K-Nearest Neighbor (KNN)” for classification of these historical data into a specific time span. The k nearest time spans (k nearest neighbors) are then taken to predict the weather. Our experiments show that the system generates accurate results within reasonable time for months in advance.

Keywords

climate prediction weather prediction data mining k-Nearest Neighbor (KNN) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hansen, J.W., Sivakumar, M.V.K.: Advances in applying climate prediction to agriculture. Climate Research 33, 1–2 (2006)CrossRefGoogle Scholar
  2. 2.
    Sayuti, R., Karyadi, W., Yasin, I., Abawi, Y.: Factors affecting the use of climate forecasts in agriculture: a case study of Lombok Island, Indonesia. ACIAR Technical Reports Series, No. 59, pp. 15-21 (2004)Google Scholar
  3. 3.
    Murray-Ruest, H., Lashari, B., Memon, Y.: Water distribution equity in Sindh Province, Pakistan, Pakistan Country Series No. 1, Working Paper 9, International Water Management Institute, Lahore, Pakistan (2000)Google Scholar
  4. 4.
    Stern, P.C., Easterling, W.E.: Making Climate Forecasts Matter. National Academy Press (1999)Google Scholar
  5. 5.
    Landman, W.A., Mason, S.J.: Change in the association between Indian Ocean sea-surface temperatures and summer rainfall over South Africa and Namibia. International Journal of Climatology 19, 1477–1492 (1999)CrossRefGoogle Scholar
  6. 6.
    Landman, W.A.: A canonical correlation analysis model to predict South African summer rainfall. NOAA Experimental Long-Lead Forecast Bulletin 4(4), 23–24 (1995)Google Scholar
  7. 7.
    Hsieh, W.W.: Nonlinear Canonical Correlation Analysis of the Tropical Pacific Climate Variability Using a Neural Network Approach. Journal of Climate 14(12), 2528–2539 (2001)CrossRefGoogle Scholar
  8. 8.
    Hays, S.P., Mangum, L.J., Picaut, J., Sumi, A., Takeuchi, K.: TOGA-TAO: A moored array for real time measurement in the tropical Pacific ocean. Bulletin of the American Meteorological Society 72(3), 339–347 (1991)CrossRefGoogle Scholar
  9. 9.
    Mason, S.E., Goddard, L., Zebiak, S.J., Ropelewski, C.F., Basher, R., Cane, M.A.: Current Approaches to Seasonal to Interannual Climate Predictions. International Journal of Climatology 21, 1111–1152 (2001)CrossRefGoogle Scholar
  10. 10.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques. Elsevier Science and Technology, Amsterdam (2006)Google Scholar
  11. 11.
    Fix, E., Hodges, J.L.: Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties. USAF school of Aviation Medicine, Randolph Field Texas (1951)Google Scholar
  12. 12.
    Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, Chichester (2005)Google Scholar
  13. 13.
    Lettre, J.: Business Planning, Decisionmaking and Ethical Aspects of Seasonal Climate Forecasting (1999), http://members.aol.com/gml1000/busclim.html
  14. 14.
    Mason, S.J., Goddard, L., Graham, N.E., Yulaeva, E., Sun, L., Arkin, P.A.: The IRI seasonal climate prediction system and the 1997/1998 El Niño event. Bulletin of the American Meteorological Society 80, 1853–1873 (1999)CrossRefGoogle Scholar
  15. 15.
    Landman, W.A., Mason, S.J.: Forecasts of Near-Global Sea Surface Temperatures Using Canonical Correlation Analysis. Journal of Climate 14(18), 3819–3833 (2001)CrossRefGoogle Scholar
  16. 16.
    Rogel, P., Maisonnave, E.: Using Jason-1 and Topex/Poseidon data for seasonal climate prediction studies. AVISO Altimetry Newsletter 8, 115–116 (2002)Google Scholar
  17. 17.
    White, A.B., Kumar, P., Tcheng, D.: A data mining approach for understanding control on climate induced inter-annual vegetation variability over the United State. Remote sensing of Environments 98, 1–20 (2005)CrossRefGoogle Scholar
  18. 18.
    Basak, J., Sudarshan, A., Trivedi, D., Santhanam, M.S.: Weather Data Mining using Component Analysis. Journal of Machine Learning Research 5, 239–253 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Zahoor Jan
    • 1
  • Muhammad Abrar
    • 2
  • Shariq Bashir
    • 3
  • Anwar M. Mirza
    • 1
  1. 1.FAST-National University of Computer and Emerging SciencesIslamabadPakistan
  2. 2.NWFP Agricultural University PeshawarPakistan
  3. 3.Vienna University of TechnologyAustria

Personalised recommendations