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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hansen, J.W., Sivakumar, M.V.K.: Advances in applying climate prediction to agriculture. Climate Research 33, 1–2 (2006)
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)
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)
Stern, P.C., Easterling, W.E.: Making Climate Forecasts Matter. National Academy Press (1999)
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)
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)
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)
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)
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)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Elsevier Science and Technology, Amsterdam (2006)
Fix, E., Hodges, J.L.: Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties. USAF school of Aviation Medicine, Randolph Field Texas (1951)
Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining. Wiley, Chichester (2005)
Lettre, J.: Business Planning, Decisionmaking and Ethical Aspects of Seasonal Climate Forecasting (1999), http://members.aol.com/gml1000/busclim.html
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)
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)
Rogel, P., Maisonnave, E.: Using Jason-1 and Topex/Poseidon data for seasonal climate prediction studies. AVISO Altimetry Newsletter 8, 115–116 (2002)
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)
Basak, J., Sudarshan, A., Trivedi, D., Santhanam, M.S.: Weather Data Mining using Component Analysis. Journal of Machine Learning Research 5, 239–253 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jan, Z., Abrar, M., Bashir, S., Mirza, A.M. (2008). Seasonal to Inter-annual Climate Prediction Using Data Mining KNN Technique. In: Hussain, D.M.A., Rajput, A.Q.K., Chowdhry, B.S., Gee, Q. (eds) Wireless Networks, Information Processing and Systems. IMTIC 2008. Communications in Computer and Information Science, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89853-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-89853-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89852-8
Online ISBN: 978-3-540-89853-5
eBook Packages: Computer ScienceComputer Science (R0)