Genetic programming for the downscaling of extreme rainfall events on the east coast of peninsular Malaysia

Atmosphere

Volumn 5, Issue 4, 2014, Pages 914-936

  Pour, Sahar Hadi ^a   Harun, Sobri Bin ^a   Shahid, Shamsuddin ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Downscaling; Extreme rainfall indices; Genetic programming; Statistical downscaling model

Indexed keywords

CLIMATE CHANGE; GENETIC ALGORITHMS; RAIN; REGRESSION ANALYSIS;

CALIBRATION AND VALIDATIONS; DOWN-SCALING; ENVIRONMENTAL PREDICTION; EXTREME RAINFALL; LOGISTIC REGRESSION METHOD; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; STATISTICAL DOWNSCALING; STATISTICAL DOWNSCALING MODEL (SDSM);

GENETIC PROGRAMMING;

ARTIFICIAL NEURAL NETWORK; CLIMATE CHANGE; DOWNSCALING; EXTREME EVENT; GENETIC ALGORITHM; MODEL VALIDATION; PRECIPITATION INTENSITY; RAINFALL; STATISTICAL ANALYSIS;

MALAYSIA; WEST MALAYSIA;

EID: 84919717024     PISSN: None     EISSN: 20734433     Source Type: Journal    
DOI: 10.3390/atmos5040914     Document Type: Article

References (70)

1. Shahid, S., Harun, S.B., Katimon, A. Changes in diurnal temperature range in Bangladesh during the time period 1961-2008. Atmos. Res. 2012, 118, 260-270.
2. IPCC. Summary for policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of the Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK/New York, NY, USA, 2013.
3. Su, B.D., Jiang, T., Jin, W.B. Recent trends in observed temperature and precipitation extremes in the Yangtze River basin, China. Theor. Appl. Climatol. 2006, 83, 139-151.
4. Shahid, S. Trends in extreme rainfall events of Bangladesh. Theor. Appl. Climatol. 2011, 104, 489-499.
5. Shahid, S. Rainfall variability and the trends of wet and dry periods in Bangladesh. Int. J. Climatol. 2010, 30, 2299-2313.
6. Gao, T., Xie, L. Multivariate regression analysis and statistical modeling for summer extreme precipitation over the Yangtze River Basin, China. Adv. Meteorol. 2014, doi:10.1155/2014/269059.
7. Solman, S.A. Regional climate modeling over South America: A review. Adv. Meteorol. 2013, doi:10.1155/2013/504357.
8. Laprise, R. Regional climate modelling. J. Comput. Phys. 2008, 227, 3641-3666.
9. Liu, Y., Xie, L., Morrison, J.M., Kamykowski, D. Dynamic downscaling of the impact of climate change on the ocean circulation in the Galápagos Archipelago. Adv. Meteorol. 2013, doi:10.1155/2013/837432.
10. Wilby, R.L., Charles, S.P., Zorita, E., Timbal, B., Whetton, P., Mearns, L.O. Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods. Supporting Material of the Intergovernmental Panel on Climate Change. Available online: www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf (accessed on 24 November 2014).
11. Do Hoai, N., Udo, K., Mano, A. Downscaling global weather forecast outputs using ANN for flood prediction. J. Appl. Math. 2011, doi:10.1155/2011/246286.
12. Khalili, M., van Nguyen, V.T., Gachon, P. A statistical approach to multi-site multivariate downscaling of daily extreme temperature series. Int. J. Climatol. 2013, 33, 15-32.
13. Fan, L., Chen, D., Fu, C., Yan, Z. Statistical downscaling of summer temperature extremes in northern China. Adv. Atmos. Sci. 2013, 30, 1085-1095.
14. Flaounas, E., Drobinski, P., Vrac, M., Bastin, S., Lebeaupin-Brossier, C., Stéfanon, M., Borga, M., Calvet, J.C. Precipitation and temperature space-time variability and extremes in the Mediterranean region: Evaluation of dynamical and statistical downscaling methods. Clim. Dynam. 2013, 40, 2687-2705.
15. Bürger, G., Sobie, S.R., Cannon, A.J., Werner, A.T., Murdock, T.Q. Downscaling extremes: An intercomparison of multiple methods for future climate. J. Clim. 2013, 26, 3429-3449.
16. White, C.J., McInnes, K.L., Cechet, R.P., Corney, S.P., Grose, M.R., Holz, G.K., Katzfey, J.J., Bindoff, N.L. On regional dynamical downscaling for the assessment and projection of temperature and precipitation extremes across Tasmania, Australia. Clim. Dynam. 2013, 41, 3145-3165.
17. Jeong, D.I., St-Hilaire, A., Ouarda, T.B.M.J., Gachon, P. Projection of future daily precipitation series and extreme events by using a multi-site statistical downscaling model over the great montréal area, Québec, Canada. Hydrol. Res. 2013, 44, 147-168.
18. Christensen, R. Log-Linear Models and Logistic Regression, 2nd ed., Springer Verlag: New York, NY, USA, 1997.
19. Fealy, R., Sweeney, J. Statistical downscaling of precipitation for a selection of sites in Ireland employing a generalised linear modelling approach. Int. J. Climatol. 2007, 27, 2083-2094.
20. Yang, W., Bardossy, A., Caspary, H.J. Downscaling daily precipitation time series using a combined circulation- and regression-based approach. Theor. Appl. Climatol. 2010, 102, 439-454.
21. Levavasseur, G., Vrac, M., Roche, D.M., Paillard, D., Martin, A., Vandenberghe, J. Present and LGM permafrost from climate simulations: Contribution of statistical downscaling. Clim. Past. 2011, 7, 1225-1246.
22. Hashmi, M.Z., Shamseldin, A.Y., Melville, B.W. Statistical downscaling of watershed precipitation using gene expression programming (GEP). Environ. Model. Softw. 2011, 26, 1639-1646.
23. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D. Genetic Programming: An Introduction; Morgan Kaufmann, Inc.: San Francisco, CA, USA, 1998.
24. Coulibaly, P. Downscaling daily extreme temperatures with genetic programming. Geophys. Res. Lett. 2004, doi:10.1029/2004GL020075.
25. Shahid, S., Hasan, M., Mondal, R.U. Modeling monthly mean maximum temperature using genetic programming. Int. J. Soft Comput. 2007, 2, 612-616.
26. Liu, X., Coulibaly, P., Evora, N. Comparison of data-driven methods for downscaling ensemble weather forecasts. Hydrol. Earth Syst. Sci. 2008, 12, 615-624.
27. Hashmi, M.Z., Shamseldin, A.Y., Melville, B.W. Statistical downscaling of precipitation: State-of-the-art and application of Bayesian multi-model approach for uncertainty assessment. Hydrol. Earth Syst. Sci. Discuss. 2009, 6, 6535-6579.
28. Kisi, O., Shiri, J. Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour. Manag. 2011, 25, 3135-3152.
29. Zerenner, T., Venema, V., Simmer, C. Atmospheric Downscaling Using Genetic Programming. Available online: http://meetingorganizer.copernicus.org/EGU2013/EGU2013-3380.pdf (accessed on 24 November 2014)
30. Biesheuvel, C.J., Siccama, I., Grobbee, D.E., Moons, K.G. Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism. J. Clin. Epidemiol. 2004, 57, 551-560.
31. Engoren, M., Habib, R.H., Dooner, J.J., Schwann, T.A. Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery. J. Clin. Monit. Comput. 2013, 27, 455-464.
32. Yusuf, A.A., Francisco, H.A. Climate Change Vulnerability Mapping for Southwest Asia; Economy and Environment Program for Southeast Asia (EEPSEA): Singapore, 2009.
33. Deni, S.M., Suhaila, J., Zin, W.Z.W., Jemain, A.A. Spatial trends of dry spells over Peninsular Malaysia during monsoon seasons. Theor. Appl. Climatol. 2010, 99, 357-371.
34. Suhaila, J., Deni, S.M., Zin, W.Z.W., Jemain, A.A. Spatial patterns and trends of daily rainfall regime in Peninsular Malaysia during the southwest and northeast monsoons: 1975-2004. Meteorol. Atmos. Phys. 2010, 110, 1-8.
35. Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., et al. The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc. 1996, 77, 437-470.
36. Friederichs, P., Hense, A. Statistical downscaling of extreme precipitation events using censored quantile regression. Mon. Weather Rev. 2007, 135, 2365-2378.
37. Wang, J.F., Zhang, X.B. Downscaling and projection of winter extreme daily precipitation over North America. J. Clim. 2008, 21, 923-937.
38. Mannshardt-Shamseldin, E.C., Smith, R.L., Sain, S.R., Mearns, L.O., Cooley, D. Downscaling extremes: A comparison of extreme value distributions in point-source and gridded precipitation data. Ann. Appl. Stat. 2010, 4, 484-502
39. Kallache, M., Vrac, M., Naveau, P., Michelangeli, P.A. Nonstationary probabilistic downscaling of extreme precipitation. J. Geophys. Res. 2011, doi:10.1029/2010JD014892.
40. Leanna, M.M., King, S.I., Sarwar, KR., McLeod, A.I.A., Slobodan, P., Simonovic, P. The effects of climate change on extreme precipitation events in the Upper Thames River Basin: A comparison of downscaling approaches. Can. Water Resour. J. 2012, 37, 253-274.
41. Bürger, G., Murdock, T.Q., Werner, A.T., Sobie, S.R., Cannon, A.J. Downscaling extremes-An intercomparison of multiple statistical methods for present climate. J. Clim. 2012, 25, 4366-4388.
42. You, Q., Kang, S., Aguilar, E., Yan, Y. Changes in daily climate extremes in the eastern and central Tibetan Plateau during 1961-2005. J. Geophys. Res. 2008, doi:10.1029/2007JD009389.
43. Aguilar, E., Peterson, T.C., Ramírez Obando, P., Frutos, R., Retana, J.A., Solera, M., Soley, J., González García, I., Araujo, R.M., Rosa Santos, A., et al. Changes in precipitation and temperature extremes in CentralAmerica and northern South America, 1961-2003. J. Geophys. Res. 2005, doi:10.1029/2005JD006119.
44. WMO. The Role of Climatological Normals in A Changing Climate; WCDMP-No. 61, WMO-TD No. 1377, World Climate Data and Monitoring Programme; World Meteorological Organization: Geneva, Switzerland, 2007.
45. Tripathi, S., Srinivas, V.V., Nanjundiah, S.R. Support vector machine approach to downscale precipitation in climate change scenarios. J. Hydrol. 2006, 330, 621-640.
46. Wilby, R. Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection indices. Clim. Res. 1998, 10, 163-178.
47. Wilby, R.L., Wigley, T.M.L. Precipitation predictors for downscaling: Observed and general circulation model relations relationhips. Int. J. Climatol. 2000, 20, 641-661.
48. Wilby, R.L., Dawson, C.W., Barrow, E.M. SDSM-A decision support tool for the assessment of regional climate change impacts. Environ. Model. Softw. 2002, 17, 145-157.
49. Tavakol-Davani, H., Nasseri, M., Zahraie, B. Improved statistical downscaling of daily precipitation using SDSM platform and data-mining methods. Int. J. Climatol. 2012, 33, 2561-2578.
50. Dawson, C.W., Wilby, R.L. Hydrological modeling using artificial neural networks. Prog. Phys. Geogr. 2004, 25, 80-108.
51. Chadwick, R., Coppola, E., Giorgi, F. Anartificial neural network technique for downscaling GCM outputs to RCM spatial scale. Nonlinear Proc. Geoph. 2011, 18, 1013-1028.
52. Harpham, C., Dawson, C.W. The effect of different basis functions on a radial basis function network for time series prediction: A comparative study. Neurocomputing 2006, 69, 2161-2170.
53. Hsieh, W.W. Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels; Cambridge University Press: Cambridge, UK, 2009.
54. Haylock, M.R., Cawley, G.C., Harpham, C., Wilby, R.L., Goodess, C.M. Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios. Int. J. Climatol. 2006, 26, 1397-1415.
55. Karamouz, M., Fallahi, M., Nazif, S., Rahimi Farahani, M. Long lead rainfall prediction using statistical downscaling and articial neural network modeling. Trans. A: Civ. Eng. 2009, 16, 165-172.
56. Gardner, M.W., Dorling, S.R. Artificial neural networks (the multilayer perceptron)-A review of applications in the atmospheric sciences. Atmos. Environ. 1998, 32, 2627-2636.
57. Cannon, A.J. Probabilistic multisite precipitation downscaling by an expanded Bernoulli-Gamma density network. J. Hydrometeorol. 2008, 9, 1284-1300.
58. Makalic, E., Schmidt, D.F. Review of modern logistic regression methods with application to small and medium sample size problems. Lecture Notes Comput. Sci. 2011, doi:10.1007/978-3-642-17432-2_22.
59. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 1996, 58, 267-288.
60. Zou, H., Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 2005, 67, 301-320.
61. Hunter, A. Using multiobjective genetic programming to infer logistic polynomial regression models. Experimental Supplement. In Proceedings of the 15th European Conference on Artificial Intelligence, Lyon, France, 21-26 July 2002.
62. Siccama, I., Keijzer, M. Genetic Programming as a Method to Develop Powerful Predictive Models for Clinical Diagnosis; Association for Computing Machinery (ACM) Press: Washington, DC, USA, 2005; pp. 164-166.
63. Helfand, B.T., Loeb, S., Meeks, J.J., Fought, A.J., Kan, D., Catalona, W.J. Pathological outcomes associated with the 17q prostate cancer risk variants. J. Urol. 2009, 181, 2502-2507.
64. Ratner, B. Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data; Taylor & Francis: New York, NY, USA, 2011.
65. Koza, J.R. Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Program to Solve Problem; Technical Report STAN-CS-90-1314; Stanford University Computer Science Department: Stanford, CA, USA, 1990.
66. Francone, F.D. Discipulus™ Software Owner's Manual, Version 3.0 Draft; Machine Learning Technologies Inc.: Littleton, CO, USA, 1998.
67. Hassan, Z., Shamsudin, S., Harun, S.B. Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theor. Appl. Climatol. 2012, 116, 243-257.
68. Tukimat, N.N.A., Harun, S.B. The projection of future rainfall change over Kedah, Malaysia with the statistical downscaling model. Malays. J. Civ. Eng. 2013, 23, 67-79.
69. Hessami, M., Gachon, P., Ouarda, T.B.M.J., St-Hilaire, A. Automated regression-based statistical downscaling tool. Environ. Model. Softw. 2008, 23, 813-834.
70. Cullen, H. The Weather of the Future: Heat Waves, Extreme Storms, and Other Scenes from a Climate-Changed Planet; HarperCollins Publishers: New York, NY, USA, 2010