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A Meteorological Conceptual Modeling Approach Based on Spatial Data Mining and Knowledge Discovery

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3533))

Abstract

Conceptual models play an important part in a variety of domains, especially in meteorological applications. This paper proposes a novel conceptual modeling approach based on a two-phase spatial data mining and knowledge discovery method, aiming to model the concepts of the evolvement trends of Mesoscale Convective Clouds (MCCs) over the Tibetan Plateau with derivation rules and environmental physical models. Experimental results show that the proposed conceptual model to much extent simplifies and improves the weather forecasting techniques on heavy rainfalls and floods in South China.

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References

  1. Giancarlo, G., Heinrich, H., Gerd, W.: On the general ontological foundations of conceptual modeling. In: Spaccapietra, S., March, S.T., Kambayashi, Y. (eds.) ER 2002. LNCS, vol. 2503, pp. 65–78. Springer, Heidelberg (2002)

    Google Scholar 

  2. Chen, P., Thalheim, B., Wong, L.Y.: Future directions of conceptual modeling. In: Chen, P., Akoka, J., Kangassalo, H., Thalheim, B. (eds.) Conceptual Modeling. LNCS, vol. 1565, pp. 287–301. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  3. Jiang, J., Fan, M.: Convective Clouds and Mesoscale Convective Systems over the Tibetan Plateau in Summer. Atmosphere Science (1), 262–269 (2002) (in Chinese)

    Google Scholar 

  4. Souto, M.J., Balseiro, C.F., Pérez-Muñuzuri, V., Xue, M., Brewster, K.: Impact of Cloud Analysis on Numerical Weather Prediction in the Galician Region of Spain. Journal of Applied Meteorology (42), 129–140 (2003)

    Article  Google Scholar 

  5. Arnaud, Y., Desbios, M., Maizi, J.: Automatic Tacking and Characterization of African Convective Systems on Meteosat Pictures. Journal of Applied Meteorology (5), 443–453 (1992)

    Article  Google Scholar 

  6. Yang, Y.B.: Automatic Tracking and Characterization of Multiple Moving Clouds in Satellite Images. In: Thissen, W., Wieringa, P., Pantic, M., Ludema, M. (eds.) Proceedings of IEEE Conference on System, Man and Cybernetics, pp. 3088–3093. IEEE Press, Los Alamitos (2004)

    Google Scholar 

  7. Freeman, H.: Computer Processing of Line-drawing Image. Computing Surveys 6(1), 57–97 (1974)

    Article  MATH  Google Scholar 

  8. Goan, T.: Supporting the user: Conceptual modeling amp knowledge discovery. In: Chen, P., Akoka, J., Kangassalo, H., Thalheim, B. (eds.) Conceptual Modeling. LNCS, vol. 1565, pp. 100–104. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  9. Quinlan, J.: C4.5: Programs for machine learning. Morgan Kaufman, San Francisco (1993)

    Google Scholar 

  10. Salvatore, R.: Efficient C4.5. IEEE Transactions on Knowledge and Data Engineering (2), 438–444 (2002)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Yang, Y., Lin, H., Guo, Z., Jiang, J. (2005). A Meteorological Conceptual Modeling Approach Based on Spatial Data Mining and Knowledge Discovery. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_67

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  • DOI: https://doi.org/10.1007/11504894_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26551-1

  • Online ISBN: 978-3-540-31893-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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