Feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

Acta Geophysica

Volumn 55, Issue 3, 2007, Pages 369-382

  Chattopadhyay, Surajit ^a  

^a PAILAN COLLEGE OF MANAGEMENT AND TECHNOLOGY   (India)

Author keywords

Artificial Neural Network model; Multiple Linear Regression forecast; Prediction of monsoon rainfall; Summer monsoon rainfall

Indexed keywords

MULTIPLE LINEAR REGRESSION FORECAST; SEA SURFACE TEMPERATURE; SUMMER-MONSOON RAINFALL;

LINEAR REGRESSION; NEURAL NETWORKS; RAIN GAGES; WEATHER FORECASTING;

RAIN;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; FORECASTING METHOD; MONSOON; MULTIPLE REGRESSION; PREDICTION; RAINFALL; SEA SURFACE TEMPERATURE;

ASIA; EURASIA; INDIA; SOUTH ASIA;

EID: 34447256610     PISSN: 18956572     EISSN: 18957455     Source Type: Journal    
DOI: 10.2478/s11600-007-0020-8     Document Type: Article

References (45)

1. Brown, B.G., and A.H. Murphy, 1988, The economic value of weather forecasts in wildfire suppression mobilization decisions, Canadian J. Forest Res. 18, 1641-1649.
2. Cartalis, C., and C. Varotsos, 1994, Surface ozone in Athens, Greece, at the beginning and at the end of the 20th-century, Atmos. Environ. 28, 3-8.
3. Chin, E.H., 1977, Modeling daily precipitation occurrence process with Markov chain, Water Resour. Res. 13, 949-956.
4. Clark, O.C., J.E. Cole and P.J. Webster, 2000, Indian Ocean SST and Indian summer monsoon rainfall: Predictive relationships and their decadal variability, J. Climate 14, 2503-2519.
5. Elsner, J.B., and A.A. Tsonis, 1992, Non-linear prediction, chaos, and noise, Bull. Am. Meteor. Soc. 73, 49-60.
6. Ferranti, L., T.N. Palmer, F. Molteni and E. Klinker, 1990, Tropical-extratropical interaction associated with the 30-60 day oscillation and its impact on medium and extended range prediction, J. Atmos. Sci. 47, 2177-2199.
7. Gadgil, S., M. Rajeevan and R. Nanjundiah, 2005, Monsoon prediction - Why yet another failure?, Current Science 88, 1389-1499.
8. Gardner, M.W., and S.R. Dorling, 1998, Artificial Neural Network (Multilayer Perceptron) - A review of applications in atmospheric sciences, Atmos. Environ. 32, 2627-2636.
9. Gevrey, M., I. Dimopoulos and S. Lek, 2003, Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecological Modeling 160, 249-264.
10. Gregory, J.M., T.M.L. Wigley and P.D. Jones, 1993, Application of Markov models to area average daily precipitation series and inter annual variability in seasonal totals, Climate Dynamics 8, 299-310.
11. Guhathakurta, P., 2006, Long-range monsoon rainfall prediction of 2005 for the districts and sub-division Kerala with artificial neural network, Current Science 90, 773-779.
12. Haykin, S., 2001, Neural Networks: A Comprehensive Foundation, 2 ed., Pearson Education Inc., New Delhi, India.
13. Hsieh, W.W., and T. Tang, 1998, Applying neural network models to prediction and data analysis in meteorology and oceanography, Bull. Am. Meteor. Soc. 79, 1855-1869.
14. Hu, M.J.C., 1964, Application of ADALINE system to weather forecasting, Technical Report, Stanford Electron, Stanford, CA.
15. Jacovides, C.P., C. Varotsos, N.A. Kaltsounides, M. Petrakis and D.P. Lalas, 1994, Atmospheric turbidity parameters in the highly polluted site of Athens basin, Renewable Energy 4, 5, 465-470.
16. Kalogirou, S.A., C.N. Constantinos, S.C. Michaelides and C.N. Schizas, 1997, A time series construction of precipitation records using Artificial Neural Networks, EUFIT' 97, September 8-11, 2409-2413.
17. Kamarthi, S.V., and S. Pittner, 1999, Accelerating neural network training using weight extrapolation, Neural Networks 12, 1285-1299.
18. Kartalopoulos, S.V., 1996, Understanding Neural Networks and Fuzzy Logic - Basic Concepts and Applications, Prentice Hall, New-Delhi.
19. Kishtawal, C.M., S. Basu, F. Patadia and P.K. Thapliyal, 2003, Forecasting summer rainfall over India using Genetic Algorithm, Geophys. Res. Lett. 30, doi: 10.1029/2003GL 018504.
20. Kondratyev, K.Y., and C.A. Varotsos, 2001a, Global tropospheric ozone dynamics - Part I: Tropospheric ozone precursors, Environ. Sci. Pollution Res. 8, 1, 57-62.
21. Kondratyev, K.Y., and C.A. Varotsos, 2001b, Global tropospheric ozone dynamics - Part II: Numerical modelling of tropospheric ozone variability, Environ. Sci. Pollution Res. 8, 2, 113-119.
22. Lee, S., S. Cho and P.M. Wong, 1998, Rainfall prediction using Artificial Neural Network, J. Geograph. Inform. Decision Anal. 2, 233-242.
23. Matthews, A.J., 2004, Atmospheric response to observed intraseasonal tropical sea surface temperature anomalies, Geophys. Res. Lett. 31, doi:10.1029/2004GL020474.
24. Matthews, A.J., B.J. Hoskins and M. Masutani, 2004, The global response to tropical heating in the madden-julian oscillation during northern winter, Quart. J. Roy. Met. Soc. 130, 1-20.
25. Men, B., Z. Xiejing and C. Liang, 2004, Chaotic analysis on monthly precipitation on Hills Region in Middle Sichuan of China, Nature and Science 2, 45-51.
26. Michaelides, S.C., C.C. Neocleous and C.N. Schizas, 1995, Artificial Neural Networks and multiple linear regression in estimating missing rainfall data, Proc. DSP95 Intern. Confer. "Digital Signal Processing", Limassol, Cyprus. 668-673.
27. Nagendra, S.M.S., and M. Khare, 2006, Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions, Ecological Modeling 190, 99-115.
28. Pal, S.K., and S. Mitra, 1999, Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, Willey Intersc. Publ., New York.
29. Perez, P., and J. Reyes, 2001, Prediction of particulate air pollution using neural techniques, Neural Computing and Application 10, 165-171.
30. Perez, P., A. Trier and J. Reyes, 2000, Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile, Atmosph. Environ. 34, 1189-1196.
31. Reddy, P.R.C., and P.S. Salvekar, 2003, Equatorial East Indian Ocean sea surface temperature: A new predictor for seasonal and annual rainfall, Current Science 85, 1600-1604.
32. Sardeshmukh, P.D., and B.J. Hoskins, 1988, The generation of global rotational flow by steady idealized tropical divergence, J. Atmos. Sci. 45, 1228-1251.
33. Sarle, W., 1997, Neural network frequently asked questions, ftp:// ftp.sas.com/pub/neural/FAQ.html.
34. Sejnowski, T.J., and C.R. Rosenberg, 1987, Parallel networks that learn to pronounce English text, Complex Systems 1, 145-168.
35. Shinoda, T., and H.H. Hendon, 1998, Mixed layer modeling of intraseasonal variability in the tropical Pacific and Indian Oceans, J. Clim. 11, 2668-2685.
36. Sivakumar, B., 2000, Chaos theory in hydrology: Important issues and interpretations, J. Hydrology 227, 1-20.
37. Sivakumar, B., 2001, Rainfall dynamics in different temporal scales: A chaotic perspective, Hydrology and Earth System Sci. 5, 645-651.
38. Sivakumar, B., S.Y. Liong, C.Y. Liaw and K.K. Phoon, 1999, Singapore rainfall behavior: Chaotic?, J. Hydrol. Eng., ASCE 4, 38-48.
39. Smith, T.M., R.W. Reynolds, R.E. Livezey and D.C. Stokes, 1996, Reconstruction of historical sea surface temperatures using empirical orthogonal functions, J. Climate 9, 1403-1420.
40. Varotsos, C., 2005, Power-law correlations in column ozone over Antarctica, Intern. J. Remote Sensing 26, 3333-3342.
41. Varotsos, C., and D. Krik-Davidoff, 2006, Long-memory processes in ozone and temperature variations at the region 60 degrees S-60 degrees N, Atmos. Chem. Phys. 6, 4093-4100.
42. Varotsos, C., K.Y. Kondratyev and M. Efstathiou, 2001, On the seasonal variation of the surface ozone in Athens, Greece, Atmos. Environ. 35, 2, 315-320.
43. Widrow, B., and M.A. Lehr, 1990, 30 years of Adoptive Neural Netwoks; Perceptron, Madaline, and Back propagation, Proc. IEEE 78, 1415-1442.
44. Wilks, D.S., 1991, Representing serial correlation of meteorological events and forecasts in dynamic decision-analytic models, Month. Weather Rev. 119, 1640-1662.
45. Wilks, D.S., 1998, Multisite generalization of a daily stochastic precipitation generation model, J. Hydrology 210, 178-191.