Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using Genetic Programming

Journal of Hydrology

Volumn 454-455, Issue , 2012, Pages 26-41

  Kashid, Satishkumar S ^a   Maity, Rajib ^b  

^a WALCHAND INSTITUTE OF TECHNOLOGY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

El Ni o Southern Oscillation (ENSO); Equatorial Indian Ocean Oscillation (EQUINOO); Genetic Programming (GP); Indian Summer Monsoon Rainfall (ISMR)

Indexed keywords

ATMOSPHERIC CIRCULATION; ATMOSPHERIC CIRCULATION PATTERNS; CLIMATIC SYSTEMS; COMPLEX RELATIONSHIPS; CORRELATION COEFFICIENT; EQUINOO; INDIAN SUMMER MONSOON RAINFALL; INHERENT COMPLEXITY; LARGE-SCALE CIRCULATION PATTERNS; RAINFALL FORECASTING; RAINFALL PREDICTION; SEPARATE ANALYSIS; SUMMER MONSOON RAINFALL; TROPICAL INDIAN OCEAN; TROPICAL METEOROLOGY; TROPICAL PACIFIC OCEAN;

ARTIFICIAL INTELLIGENCE; ATMOSPHERIC PRESSURE; ATMOSPHERIC THERMODYNAMICS; CLIMATOLOGY; FORECASTING; GENETIC PROGRAMMING; LARGE SCALE SYSTEMS; NICKEL COMPOUNDS; SIGNAL PROCESSING;

RAIN;

ARTIFICIAL INTELLIGENCE; ATMOSPHERIC CIRCULATION; EL NINO-SOUTHERN OSCILLATION; HOMOGENEITY; HYDROMETEOROLOGY; MONSOON; PREDICTION; RAINFALL;

INDIAN OCEAN; INDIAN OCEAN (TROPICAL); PACIFIC OCEAN; PACIFIC OCEAN (TROPICAL);

EID: 84863880488     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2012.05.033     Document Type: Article

References (62)

1. Ashok K., Guan Z., Yamagata T. Impact of Indian Ocean dipole on the relationship between the Indian monsoon rainfall and ENSO. Geophys. Res. Lett. 2001, 28:4499-4502.
2. Ashok K., Guan Z., Saji N., Yamagata T. Individual and combined effect of ENSO and Indian Ocean Dipole on the Indian summer monsoon. J. Clim. 2004, 17(16):3141-3155. 10.1175/1520-0442(2004) 017<3141: IACIOE>2.0.CO; 2.
3. Aytek A., Kisi O. A genetic programming approach to suspended sediment modeling. J. Hydrol. 2008, 351:288-298.
4. Babovic V. Translating data to knowledge: a case study in sediment transport. Comput.-Aided Civil Infrastruct. Eng. 2000, 15(1):1-7.
5. Babovic V. Data mining in hydrology. Hydrol. Process. 2005, 19:1-7.
6. Babovic V., Abbott M.B. The evolution of equation from hydraulic data, Part I: Theory. J. Hydraul. Res. 1997, 35(3):397-410.
7. Babovic V., Abbott M.B. The evolution of equation from hydraulic data, Part II: Applications. J. Hydraul. Res. 1997, 35(3):411-430.
8. Babovic V., Keijzer M. Genetic programming as a model induction engine. J. Hydroinform. 2000, 2(1):35-60.
9. Babovic V., Keijzer M. Rainfall runoff modeling based on genetic programming. Nord. Hydrol. 2002, 33:331-343.
10. Banerjee A.K., Sen P.N., Raman C.R.V. On foreshadowing southwest monsoon rainfall over India with mid-tropospheric circulation anomaly of April. Indian J. Meteorol. Hydrol. Geophys. 1978, 29:425-431.
11. Baptist M., Babovic V., Rodrigues J., Keijzer M., Uittenbogaard R., Mynett A., Verwey A. On inducing equations for vegetation resistance. J. Hydraul. Res. 2007, 45(4):435-450.
12. Bhalme H.N., Jadhav S.K., Mooley D.A., Ramana Murty B.H.V. Forecasting of monsoon performance over India. J. Climatol. 1986, 6:347-354.
13. Brameier M., Banzhaf W. Evolving teams of predictors with linear genetic programming. Genetic Programming and Evolvable Machines 2001, 2(4):381-407.
14. Chau K.W. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J. Hydrol. 2006, 329(3-4):363-367.
15. Douglas W.W., Wasimi S.A., Islam S. The El Niño southern oscillation and long-range forecasting of flows in Ganges. Int. J. Climatol. 2001, 21:77-87.
16. Dracup J.A., Kahya E. The relationship between U.S. streamflow and La Niña Events. Water Resour. Res. 1994, 30(7):2133-2141.
17. Drecourt, J.P., 1999. Application of neural networks and genetic programming to rainfall runoff modeling. Danish Hydraulic Institute (Hydro-Informatics Technologies HIT). 3rd DHI Software Conference {minus 45 degree rule}& DHI Software Courses. http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/drecourt_1999uANNGPrrm.html.
18. Drunpob, A., Chang, N.B., Beaman, M., 2005. Stream flow rate prediction using genetic programming model in a semi-arid coastal watershed. Proceedings of EWRI 2005, ASCE.
19. Eltahir E.A.B. El Niño and the natural variability in the flow of the Nile River. Water Resour. Res. 1996, 32(1):131-137.
20. Francone F.D. Discipulus Owner's Manual 1998, Machine Learning Technologies, Inc., Littleton, Colorado.
21. Gadgil S., Vinayachandran P.N., Francis P.A. Droughts of the Indian summer monsoon: role of clouds over the Indian Ocean. Curr. Sci. 2003, 85(2):1713-1719.
22. Gadgil S., Vinayachandran P.N., Francis P.A., Gadgil S. Extremes of the Indian summer monsoon rainfall: ENSO and equatorial Indian Ocean Oscillation. Geograph. Res. Lett. 2004, 31(L 12213). doi:10.1029/2004GLO19733.
23. Giustolisi O. Using genetic programming to determine Chèzy resistance coefficient in corrugated channels. J. Hydroinform., IWA-IAHR Publishing, UK 2004, 6(3):157-173.
24. Giustolisi O., Savic D.A. A symbolic data-driven technique based on evolutionary polynomial regression. J. Hydroinform., IWA-IAHR Publishing, UK 2006, 8(3):207-222.
25. Gowariker V., Thapliyal V., Sarkar R.P., Mandal G.S., Sikka D.R. Parametric and power regression models - new approach to long range forecasting. Masaum 1989, 40:115-122.
26. Gowariker V., Thapliyal V., Kulshrestha S.M., Mandal G.S., Sen Roy N., Sikka D.R. A power regression models for long range forecast of southwest monsoon rainfall over India. Masaum 1991, 42:125-130.
27. Harris E., Falconer R., Babovic V. Velocity predictions in compound channels with vegetated floodplains using genetic programming. J. River Basin Manage. 2003, 1(2).
28. Hong Y.S., Bramidimarri R. Evolutionary self-organising modelling of a municipal wastewater treatment plant. Water Res. 2003, 37:1199-1212. 10.1016/S0043-1354(02)00493-1.
29. Jagannathan P. Seasonal Forecasting in India: A Review 1960, India Meteorological Department, Pune, p. 67.
30. Jain S., Lall U. Floods in a changing climate: does the past represent the future?. Water Resour. Res. 2001, 37(12):3193-3205.
31. Keijzer M., Babovic V. Declarative and preferential bias in GP-based scientific discovery. Gen. Programm. Evolut. Mach. 2002, 3(1):41-79.
32. Kisi O., Shiri J. Precipitation forecasting using wavelet genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resour. Manage. 2011, 10.1007/s11269-011-9849-3.
33. Koza J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection 1992, MIT Press, Cambridge, MA, USA.
34. Krishna Kumar K., Soman M.K., Rupa Kumar K. Seasonal forecasting of Indian summer monsoon: a review. Weather 1995, 12:449-467.
35. Krishna Kumar K., Rupa Kumar K., Pant G.B. Pre-monsoon maximum and minimum temperature over India in relation to the summer monsoon rainfall. Int. J. Climatol. 1997, 17:1115-1127.
36. Krishna Kumar K., Rajagopalan B., Cane M.A. On the weakening relationship between the Indian Monsoon and ENSO. Science 1999, 284(5423):2156-2159. DOI: 10.1126/science.284.5423.2156.
37. Kung E.C., Sharif T.A. Long-range forecasting of the Indian summer monsoon onset and rainfall with upper air conditions. J. Meteorol. Soc. Jpn. 1982, 60:672-681.
38. Li X.Y., Chau K.W., Cheng C.T., Li Y.S. A Web-based flood forecasting system for Shuangpai region. Adv. Eng. Softw. 2006, 37(3):146-158.
39. Lin J.Y., Cheng C.T., Chau K.W. Using support vector machines for long-term discharge prediction. Hydrol. Sci. J. 2006, 51(4):599-612.
40. Liong S.Y., Gautam T.R., Khu S.T., Babovic V., Keijzer M., Muttil N. Genetic programming: a new paradigm in rainfall-runoff modeling. J. Am. Water Resour. Assoc. 2002, 38(3):705-718.
41. Maity R., Nagesh Kumar D. Hydroclimatic association of monthly summer monsoon rainfall over India with large-scale atmospheric circulation from tropical Pacific Ocean and Indian Ocean region. Atmosph. Sci. Lett., R. Meteorol. Soc. (RMetS), Wiley InterScience 2006, 7:101-107. 10.1002/asl.141.
42. Maity R., Nagesh Kumar D. Probabilistic prediction of hydroclimatic variables with nonparametric quantification of uncertainty. J. Geophys. Res. - Atmosph. 2008, 113:D14105. 10.1029/2008JD009856.
43. Makkeasorn A., Chang N.B., Zhou X. Short-term streamflow forecasting with global climate change implications - a comparative study between genetic programming and neural network models. J. Hydrol. 2008, 352:336-354.
44. Marcella M.P., Eltahir E.A.B. The hydroclimatology of Kuwait: explaining the variability of rainfall at seasonal and interannual time scales. J. Hydrometeor. 2008, 9:1095-1105.
45. Muttil N., Chau K.W. Machine learning paradigms for selecting ecologically significant input variables. Eng. Appl. Artif. Intell. 2007, 20(6):735-744.
46. Muttil N., Liong S.Y. Improving Runoff forecasting by input variable selection in Genetic Programming. ASCE World Water Congress, Orlando, Florida, USA 2001, 111(2001):76. doi:10.1061/40569(2001)76.
47. Partal T., Kisi O. Wavelet and neuro fuzzy conjunction model for precipitation forecasting. J. Hydrol. 2007, 342:199-212.
48. Parthasarathy B., Diaz H.F., Eischeid J.K. Prediction of All India summer monsoon rainfall with regional and large-scale parameters. J. Geophys. Res. 1988, 93(5):5341-5350.
49. Parthasarathy B., Rupa Kumar K., Deshpande V.R. Indian summer monsoon rainfall and 200mb meridional wind index: application for long range prediction. Int. J. Climatol. 1991, 11:165-176.
50. Parthasarathy, B., Munot, A.A., Kothawale, D.R., 1995. Monthly and Seasonal Rainfall Series for All-India Homogeneous Regions and Meteorological Subdivisions: 1871-1994. IITM Research, Report No. RR-065.
51. Rajeevan M., Pai D.S., Dikshit S.K., Kelkar R.R. IMD's new operational models for long-range forecast of southwest monsoon rainfall over India and their verification for 2003. Curr. Sci. 2004, 86(3):22-431.
52. Rao, K.N., 1965. Seasonal forecasting - India. Proc. of Symp. on 'Research and Development Aspects of Long Range Forecasting'. WMO-IUGG Tech. Note No. 66 WMO-No. 162-TP-79. World Meteorological Organisation Geneva, pp. 17-30.
53. Rao, K.N., Rama Moorthy, K.S., 1960. Seasonal (Monsoon) rainfall forecasting in India. Proc. of Symp. on 'Monsoon of the World', held at New Delhi February 1958, Published by India Meteorological Department, New Delhi, pp. 237-250.
54. Saji N.H., Goswami B.N., Vinayachandran P.N., Yamagata T. A dipole mode in the tropical Indian Ocean. Nature 1999, 401:360-363.
55. Saks P., Maringer D. Evolutionary money management. Natural Computing in Computational Finance 2010, vol. 3. Springer-Verlag, Berlin, Heidelberg, ISBN-978-3-642-13949-9.
56. Savur, S.R., 1931. The seasonal Forecasting Formulae Used in the India Meteorological Department. Scientific Notes, vol. 4, no. 37. Published by India Meteorological Department, New Delhi.
57. Sette S., Boullart L. Genetic programming: principles and applications. Eng. Appl. Artif. Intell. 2001, 14:727-736.
58. Shiri J., Kisi O. Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan Province (Southwestern Iran). ASCE J. Irrig. Drain. Eng. 2011, 137(7):412-425.
59. Shukla J., Paolino D.A. The Southern Oscillation and long range forecasting of the summer monsoon over India. Mon. Weather Rev. 1983, 111:1830-1837.
60. Web reference. ftp://www.tropmet.res.in/pub/data/rain/iitm-regionrf.txt.
61. Whigham P.A., Crapper P.F. Modeling rainfall-runoff using genetic programming. Math. Comput. Model. 2001, 33:707-721. Canberra, Australia.
62. Willmott, C. J., Robeson S.M., and Matsuura K., 2011. Short Communication - a refined index of model performance. Int. J. Climatol., in press, http://dx.doi.org/10.1002/joc.2419.