Abstract
One of the major challenges for predicting tropical cyclone intensity is that we lack the understanding of coupling relationships of physical processes governing tropical cyclone intensification. This paper presents a Java-based case-based reasoning model to assist tropical cyclone forecasters to determine the intensity change of the tropical cyclone. Cases are constructed by using the data mining algorithms to uncover the hidden relationships between physical processes and tropical cyclone intensity. We specify the domain data, definitions of features from the data, tool for data exploration, and architecture of case-based reasoning model. Preliminary results are found to be useful to forecasters when faced with some unusual problem and under different weather situations.
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Notes
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A datamining toolkit developed by Integral Solutions Ltd: http://www.isl.co.hk/
References
Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. AICom – Artificial Intelligence Communications, IOS Press, 7(1):39–59
Dvorak VF (1975) Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon Wea Rev 103:420–430
Feng L, Dillon T, Liu J (2001) Inter-transactional association rules for prediction and their application to studying meteorological data. Data Knowl Eng, Elsevier, 37:85–115
Hansen B, Riordan D (1998) Fuzzy case-based prediction of ceiling and visibility. In: Pro. of 1st Conf. on Artificial Intelligence, American Meteorological Society, pp 118–123
Lee RST, Liu JNK (2000) Tropical cyclone identification and tracking system using integrated neural oscillatory elastic graph matching and hybrid RBF network track mining techniques. IEEE Trans NN 11(3):680–689
Li B, Liu J, Dai H (1998) Forecasting from low quality data with applications in weather forecasting. Int J Comp Inform 22(3):351–358
McGregor JL, Walsh KJ, Katzfey JJ (2000) Climate simulations for Tasmania. In: Pro. of 4th Int. Conf. on Southern Hemisphere Meteorological and Oceanography, American Meteorological Society, pp 514–515
Pal Sankar K, Shiu Simon CK (2004) Foundations of soft case-based reasoning. Wiley
San Pedro J, Burstein F (2003) A framework for case-based fuzzy multi-criteria decision support for tropical cyclone forecasting. In: Pro. of 36th Hawaii Int. Conf., System Sciences
Tang J, Yang R, Kafatos M (2005) Data mining for tropical cyclone intensity prediction. In: Pro. of 6th Conf. on Coastal Atmospheric and Oceanic Prediction and Processes, Session 7.5
TC Formation Regions, NOAA, http://www.srh.noaa.gov/jetstream/tropics/ tc_basins.htm
Acknowledgments
The authors would like to acknowledge the partial support of CRG grant G-U186 of The Hong Kong Polytechnic University.
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Liu, J.N.K., Shiu, S.C.K., You, J. (2009). Tropical Cyclone Forecaster Integrated with Case-Based Reasoning. In: Mastorakis, N., Mladenov, V., Kontargyri, V. (eds) Proceedings of the European Computing Conference. Lecture Notes in Electrical Engineering, vol 28. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85437-3_23
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DOI: https://doi.org/10.1007/978-0-387-85437-3_23
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