Wavelet support vector machines for forecasting precipitation in tropical cyclones: Comparisons with GSVM, regression, and MM5

Weather and Forecasting

Volumn 27, Issue 2, 2012, Pages 438-450

  Wei, Chih Chiang ^a  

^a TOKO UNIVERSITY   (Taiwan)

Author keywords

Flood events; Forecasting techniques; Hydrologic models; Neural networks

Indexed keywords

ATMOSPHERIC RESEARCH; CUMULATIVE RAINFALL; FLOOD EVENT; FORECASTING TECHNIQUES; GAUSSIAN KERNELS; HYDROLOGIC MODELS; LAG TIME; MESOSCALE MODEL; MM5 MODELS; PENNSYLVANIA; RAINFALL PREDICTION; REGRESSION MODEL; SHIH-MEN RESERVOIR; STATISTICAL MODELS; TROPICAL CYCLONE; WAVELET KERNEL; WAVELET SUPPORT VECTOR MACHINES;

CLIMATOLOGY; FORECASTING; HURRICANES; MATHEMATICAL MODELS; NEURAL NETWORKS; RAIN; REGRESSION ANALYSIS; WATERSHEDS;

SUPPORT VECTOR MACHINES;

ARTIFICIAL NEURAL NETWORK; FLOOD FORECASTING; HYDROLOGICAL MODELING; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; PRECIPITATION ASSESSMENT; PRECIPITATION INTENSITY; RAINFALL; TYPHOON; WEATHER FORECASTING;

SHIHMEN RESERVOIR; TAIWAN;

EID: 84863003352     PISSN: 08828156     EISSN: None     Source Type: Journal    
DOI: 10.1175/WAF-D-11-00004.1     Document Type: Article

References (55)

1. Blackadar, A. K., 1979: High resolution models of the planetary boundary layer. Advances in Environmental Sciences and Engineering, J. Pfafflin and E. Ziegler, Eds., Vol. 1, Gordon and Breach, 50-85.
2. Blaker, H., 2000: Minimax estimation in linear regression under restrictions. J. Stat. Plann. Inference, 90, 35-55.
3. Burges, C. J. C., 1998: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discovery, 2, 955-974.
4. Businger, S., D. I. Knapp, and G. F. Watson, 1990: Storm following climatology of precipitation associated with winter cyclones originating over the Gulf of Mexico. Wea. Forecasting, 5, 378-403.
5. Chen, G. Y., and W. F. Xie, 2007: Pattern recognition with SVM and dual-tree complex wavelets. Image Vision Comput., 25, 960-966.
6. Chen, S.-H., R. C. Guido, T.-K. Truong, and Y. Chang, 2010: Improved voice activity detection algorithm using wavelet and support vector machine. Comput. Speech Lang., 24, 531-543.
7. Chen, X., Y. Li, R. Harrison, and Y.-Q. Zhang, 2008: Type-2 fuzzy logic-based classifier fusion for support vector machines. Appl. Soft Comput., 8, 1222-1231.
8. Cortes, C., and V. Vapnik, 1995: Support-vector networks. Mach. Learn., 20, 273-297.
9. Donner, L. J., 1988: An initialization for cumulus convection in numerical weather prediction models. Mon. Wea. Rev., 116, 377-385.
10. Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale twodimensional model. J. Atmos. Sci., 46, 3077-3107.
11. Ebert, E. E., U. Damrath, W. Wergen, and M. E. Baldwin, 2003: The WGNE assessment of short-term quantitative precipitation forecasts (QPFs) from operational numerical weather prediction models. Bull. Amer. Meteor. Soc., 84, 481-492.
12. Fan, T.-H., and Y.-H. Lee, 2007: A Bayesian mixture model with application to typhoon rainfall predictions in Taipei. Int. J. Contemp. Math. Sci., 2, 639-648.
13. Fread, D. L., and Coauthors, 1995: Modernization in the National Weather Service River and Flood Program. Wea. Forecasting, 10, 477-484.
14. Genell, A., S. Nemes, G. Steineck, and P.W. Dickman, 2010:Model selection in medical research: A simulation study comparing Bayesian model averaging and stepwise regression. BMC Med. Res. Methodol., 10, doi:10.1186/1471-2288-10-108.
15. Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev., 121, 764-787.
16. Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-3981STR, 117 pp.
17. Hao, P.-Y., 2008: Fuzzy one-class support vector machines. Fuzzy Sets Syst., 159, 2317-2336.
18. Heckley, W. A., 1985: Systematic errors of the ECMWF operational forecasting model in tropical regions. Quart. J. Roy. Meteor. Soc., 111, 709-738.
19. Hsu, N.-S., and C.-C. Wei, 2007: A multipurpose reservoir realtime operation model for flood control during typhoon invasion. J. Hydrol., 336, 282-293.
20. Jian, G.-J., S.-L. Shieh, and J. A. McGinley, 2003: Precipitation simulation associated with Typhoon Sinlaku (2002) in the Taiwan area using the LAPS diabatic initialization for MM5. Terr. Atmos. Oceanic Sci., 14, 261-288.
21. Jian, G.-J., C.-S. Lee, and G. T.-J. Chen, 2006: Numerical simulation of Typhoon Dot (1990) during TCM-90: Typhoon Dot's discontinuous track across Taiwan. Terr. Atmos. Oceanic Sci., 17, 23-52.
22. Joachims, T., 1999: Making large-scale support vector machine learning practical. Advances in KernelMethods: Support Vector Machines, B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds., The MIT Press, 169-184.
23. Kivanc, D., G. Q. Li, and H. Liu, 2003: Computationally efficient bandwidth allocation and power control for OFDMA. IEEE Trans. Wireless Commun., 2, 1150-1158.
24. Lee, C.-S., L.-R. Huang, H.-S. Shen, and S.-T. Wang, 2006: A climatology model for forecasting typhoon rainfall in Taiwan. Nat. Hazards, 37, 87-105.
25. Li, M.-H., M.-J. Yang, R.-T. Soong, and H.-L. Huang, 2005: Simulating typhoon floods with gauge data and mesoscale-modeled rainfall in amountainouswatershed. J. Hydrometeor., 6, 306-323.
26. Lin, G.-F., and L.-H. Chen, 2005: Application of artificial neural network to typhoon rainfall forecasting. Hydrol. Processes, 19, 1825-1837.
27. Liu, Y., D. L. Zhang, and M. K. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: Explicit simulation and verification. Mon. Wea. Rev., 125, 3073-3093.
28. Lonfat, M., F. D. Marks, and S. Chen, 2004: Precipitation distribution in tropical cyclones using the Tropical Rainfall MeasuringMission (TRMM) Microwave Imager: A global perspective. Mon. Wea. Rev., 132, 1645-1660.
29. Lonfat, M., R. Rogers, T. Marchok, and F. D. Marks Jr., 2007: A parametric model for predicting hurricane rainfall. Mon. Wea. Rev., 135, 3086-3097.
30. McBride, J. L., and E. E. Ebert, 2000: Verification of quantitative precipitation forecasts from operational numerical weather prediction models over Australia. Wea. Forecasting, 15, 103-121.
31. Mercer, J., 1909: Functions of positive and negative type and their connection with the theory of integral equation. Philos. Trans. Roy. Soc. London, 209A, 415-446.
32. Platt, J., 1999: Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods: Support Vector Machines, B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds., The MIT Press, 185-208.
33. Salvador, P., R. Valadas, and A. Pacheco, 2003: Multiscale fitting procedure using Markov modulated Poisson processes. Telecommun. Syst., 23, 123-148.
34. Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570-575.
35. Schölkopf, B., A. J. Smola, R. Williamson, and P. Bartlett, 2000: Newsupport vector algorithms. NeuralComput., 12, 1207-1245.
36. Sheng, W.-B., R.-P. Zhang, and C.-L. Lu, 2008: Application of SVM method in tropical cyclone rainfall forecast. Guangdong Meteor., 3, 10-12.
37. SPSS Inc., 2006: Clementine 10.1 algorithms guide. Integral Solutions Ltd., 129-134.
38. Szu, H. H., B. Telfer, and S. Kadambe, 1992: Neural network adaptive wavelets for signal representational and classification. Opt. Eng., 31, 1907-1916.
39. Tolambiya, A., and P. K. Kalra, 2010: Relevance vector machine with adaptive wavelet kernels for efficient image coding. Neurocomputing, 73, 1417-1424.
40. Tuleya, R. E., M. DeMaria, and R. J. Kuligowski, 2007: Evaluation of GFDL and simple statistical model rainfall forecasts for U.S. landfalling tropical storms. Wea. Forecasting, 22, 56-70.
41. Vapnik, V., 1995: The Nature of Statistical Learning Theory. 2nd ed. Statistics for Engineering Information Science, Springer, 333 pp.
42. Wang, S., 1999: Nonlinear regression: A hybrid model. Comput. Oper. Res., 26, 799-817.
43. Wang, Y., 2002: An explicit simulation of tropical cyclones with a triply nested movable mesh primitive equation model: TCM3. Part II: Model refinements and sensitivity to cloud microphysics parameterization. Mon. Wea. Rev., 130, 3022-3036.
44. Wei, C.-C., and N.-S. Hsu, 2009: Optimal tree-based release rules for real-time flood control operations on a multipurpose multireservoir system. J. Hydrol., 365, 213-224.
45. Widodo, A., and B.-S. Yang, 2008: Wavelet support vector machine for induction machine fault diagnosis based on transient current signal. Expert Syst. Appl., 35, 307-316.
46. Wu, C.-C., T.-H. Yen, Y.-H. Kuo, and W. Wang, 2002: Rainfall simulation associated with Typhoon Herb (1996) near Taiwan. Part I: Topographic effect. Wea. Forecasting, 17, 1001-1015.
47. Wu, Q., 2009: Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wn-SVM. Expert Syst. Appl., 37, 194-201.
48. Yang, M.-H., and R.-C. Wang, 2008: DDoS detection based on wavelet kernel support vector machine. J. China Univ. Post Telecommun., 15, 59-63.
49. Yang, M.-J., and R. A. Houze Jr., 1995: Sensitivity of squall-line rear inflow to ice microphysics and environmental humidity. Mon. Wea. Rev., 123, 3175-3193.
50. Yang, M.-J, and Q.-C. Tung, 2003: Evaluation of rainfall forecasts over Taiwan by four cumulus parameterization schemes. J. Meteor. Soc. Japan, 81, 1163-1183.
51. Yang, M.-J, and L. Ching, 2005: A modeling study of Typhoon Toraji (2001): Physical parameterization sensitivity and topographic effect. Terr. Atmos. Oceanic Sci., 16, 177-213.
52. Yao, Z., D. Mei, and Z. Chen, 2010: On-line chatter detection and identification based on wavelet and support vector machine. J. Mater. Process. Technol., 210, 713-719.
53. Yeh, T.-C., 2002: Typhoon rainfall over Taiwan area: The empirical orthogonal function modes and their applications on the rainfall forecasting. Terr. Atmos. Oceanic Sci., 13, 449-468.
54. Zhang, L., W. Zhou, and L. Jiao, 2004: Wavelet support vector machine. IEEE Trans. Syst. Man Cybern., 34B, 34-39.
55. Zhang, Q. H., and A. Benveniste, 1992: Wavelet networks. IEEE Trans. Neural Networks, 3, 889-898.