Prediction of rainfall from WSR-88D radar using kernel-based methods

International Journal of Smart Engineering System Design

Volumn 5, Issue 4, 2003, Pages 429-438

  Trafalis, Theodore B ^a   Santosa, Budi ^a   Richman, Michael B ^b  

^a USA   (United States)

^b UNIVERSITY OF OKLAHOMA   (United States)

Author keywords

Generalization error; Kernel function; Least squares support vector regression; Rainfall estimation; Regression analysis; Support vector regression

Indexed keywords

GENERALIZATION ERROR; KERNEL FUNCTION; RAINFALL ESTIMATION; SUPPORT VECTOR REGRESSION;

ALGORITHMS; DATA REDUCTION; ERROR ANALYSIS; FUNCTIONS; NEURAL NETWORKS; NONLINEAR SYSTEMS; OPTIMIZATION; PROBABILITY; PROBLEM SOLVING; RADAR; REGRESSION ANALYSIS;

RAIN;

EID: 3042533554     PISSN: 10255818     EISSN: None     Source Type: Journal    
DOI: 10.1080/10255810390243737     Document Type: Article

References (23)

1. Basara, J. B., Brotzge, J. A., and Crawford, K. C. (1999) Investigating land-atmosphere interactions using the Oklahoma Mesonet. Third Symposium on Integrated Observing Systems, January 10-15, Dallas, TX, American Meteorological Society, pp. 120-123.
2. Fulton, R. A., Breidenbach, J. P., Seo, D.-J., and Miller. D.A, (1998) The WSR-88D rainfall algorithm. Weather and Forecasting, 13, 377-395.
3. Golub G. H., and Van Loan C. F. (1996) Matrix Computations. Third Edition, Johns Hopkins (Ed).
4. Gunn, S. R. (1998) Support Vector Machines for classification and regression, Technical Report, Dept. of Electronics and Computer Science, University of Southampton, (Southampton, U.K.).
5. Haykin, S. (1999) Neural Network: A Comprehensive Foundation. (Prentice Hall, New Jersey).
6. Marshall, J. S., and Palmer, W. M. (1948) The distribution of raindrops with size. Journal of Meteorology, 5, pp. 165-166.
7. Müller, K. R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., and Vapnik, V. N. (1997) Predicting time series with support vector machines, Artificial Neural Networks ICANN'97, Gerstner, W., Germond, A., Hasler, M., and Nicoud, J.D., (eds.), Springer Lecture Notes in Computer Science, 1327, pp. 999-1004.
8. Navonne, H. D., and. Ceccatto, H. A. (1994) Predicting Indian monsoon rainfall: a neural network approach. Climate Dynamics, 10, pp. 305-312.
9. OFCM (1990) Doppler Radar Meteorological Observations, Part B, Doppler Radar Theory and Methodology. Federal Meteorological Handbook 11, FCM-H11 B-1990, (Office of the Federal Coordinator for Meteorological Services and Supporting Research, Washington, D. C.).
10. OFCM (1991) Doppler Radar Meteorological Observations, Part C, WSR-88D Products and Algorithms. Federal Meteorological Handbook 11, FCM-H11 C-1991, (Office of the Federal Coordinator for Meteorological Services and Supporting Research, Washington, D. C.).
11. Pelckmans K., Suykens J. A. K., Van Gestel T., De Brabanter J., Lukas L. Hamers B., De Moor B., and Vandewalle J. (2002) LS-SVMlab: a Matlab/C toolbox for Least Squares Support Vector Machines, Internal Report 02-44, ESAT-SISTA, K. U. Leuven (Leuven, Belgium).
12. Richman, M. B. (1986) Review paper: Rotation of principal components. International Journal of Climatology, 6, pp. 293-335.
13. Rosenfeld, D., Wolff, D. B., and Atlas, D. (1993) General probability-matched relations between radar reflectivity and rain rate. Journal of Applied Meteorology, 32, pp. 50-72.
14. Saunders, C., Gammerman, A., and Vovk, V. (1998) Ridge regression learning algorithm in dual variables, Proceedings of the 15th International Conference on Machine Learning, pp. 515-521.
15. Stephenson, D. B. (2000) Use of the "odds ratio" for diagnosing forecast skill. Weather and Forecasting, 15, pp. 221-232.
16. Suykens, J. A. K., and Vandewalle, J. (1999) Least squares support vector machine classifiers, Neural Processing Letters, 9, No. 3, June, pp. 293-300.
17. Suykens, J. A. K., De Brabanter, J., Lukas, L., and Vandewalle, J. (2002) Weighted Least Squares Support Vector Machines: robustness and sparse approximation, Neurocomputing, Special issue on fundamental and information processing aspects of neurocomputing, 48, No. 1-4, October, pp. 85-105.
18. Suykens, J. A. K.,Van Gestel, T., De Brabanter, J. De Moor, B., Vandewalle, J. (2002) Least Squares Support Vector Machines, (World Scientific, Singapore).
19. Trafalis T. B., and Santosa, B. (2001) Predicting monthly flour prices through Neural Networks, RBFs and SVR, Intelligent Engineering Systems Through Artificial Neural Networks, Dagli, C.H., Buczak, A.L., Ghosh, J., Embrechts, M., Ersoy, O., and Kercel, S., (eds.), ASME Press, 11, pp. 745-750.
20. Trafalis, T. B., White, A., Santosa, B., and Richman, M. B. (2002) Data mining techniques for improved WSR-88D rainfall estimation. Computers in Industrial Engineering, 43, pp. 775-786.
21. Vapnik, V. N. (1995) The Nature of Statistical Learning Theory. (John Wiley & Sons, New York)
22. Wilks, D. S. (1995) Statistical Methods in the Atmospheric Sciences, (Academic Press, London).
23. Wilson, J. W., and Brandes, E. A. (1979) Radar measurement of rainfall- a summary. Bulletin American Meteorological Society, 60, pp. 1048-1058.