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)


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.


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


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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

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