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

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@Article{Kashid201226,
  author =       "Satishkumar S. Kashid and Rajib Maity",
  title =        "Prediction of monthly rainfall on homogeneous monsoon
                 regions of India based on large scale circulation
                 patterns using Genetic Programming",
  journal =      "Journal of Hydrology",
  volume =       "454-455",
  pages =        "26--41",
  year =         "2012",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2012.05.033",
  URL =          "http://www.sciencedirect.com/science/article/pii/S002216941200409X",
  keywords =     "genetic algorithms, genetic programming, El
                 Nino-Southern Oscillation (ENSO), Equatorial Indian
                 Ocean Oscillation (EQUINOO), Indian Summer Monsoon
                 Rainfall (ISMR)",
  abstract =     "Prediction of Indian Summer Monsoon Rainfall (ISMR) is
                 of vital importance for Indian economy, and it has been
                 remained a great challenge for hydro-meteorologists due
                 to inherent complexities in the climatic systems. The
                 Large-scale atmospheric circulation patterns from
                 tropical Pacific Ocean (ENSO) and those from tropical
                 Indian Ocean (EQUINOO) are established to influence the
                 Indian Summer Monsoon Rainfall. The information of
                 these two large scale atmospheric circulation patterns
                 in terms of their indices is used to model the complex
                 relationship between Indian Summer Monsoon Rainfall and
                 the ENSO as well as EQUINOO indices. However,
                 extracting the signal from such large-scale indices for
                 modelling such complex systems is significantly
                 difficult. Rainfall predictions have been done for `All
                 India' as one unit, as well as for five `homogeneous
                 monsoon regions of India', defined by Indian Institute
                 of Tropical Meteorology. Recent `Artificial
                 Intelligence' tool `Genetic Programming' (GP) has been
                 employed for modelling such problem. The Genetic
                 Programming approach is found to capture the complex
                 relationship between the monthly Indian Summer Monsoon
                 Rainfall and large scale atmospheric circulation
                 pattern indices - ENSO and EQUINOO. Research findings
                 of this study indicate that GP-derived monthly rainfall
                 forecasting models, that use large-scale atmospheric
                 circulation information are successful in prediction of
                 All India Summer Monsoon Rainfall with correlation
                 coefficient as good as 0.866, which may appears
                 attractive for such a complex system. A separate
                 analysis is carried out for All India Summer Monsoon
                 rainfall for India as one unit, and five homogeneous
                 monsoon regions, based on ENSO and EQUINOO indices of
                 months of March, April and May only, performed at end
                 of month of May. In this case, All India Summer Monsoon
                 Rainfall could be predicted with 0.70 as correlation
                 coefficient with somewhat lesser Correlation
                 Coefficient (C.C.) values for different `homogeneous
                 monsoon regions'.",
}

Genetic Programming entries for Satishkumar S Kashid Rajib Maity

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