Wireless Networks, Information Processing and Systems

Volume 20 of the series Communications in Computer and Information Science pp 40-51

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

  • Zahoor JanAffiliated withFAST-National University of Computer and Emerging Sciences
  • , Muhammad AbrarAffiliated withNWFP Agricultural University Peshawar
  • , Shariq BashirAffiliated withVienna University of Technology
  • , Anwar M. MirzaAffiliated withFAST-National University of Computer and Emerging Sciences

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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)