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,
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author = "Satishkumar S. Kashid and Rajib Maity",
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title = "Prediction of monthly rainfall on homogeneous monsoon
regions of India based on large scale circulation
patterns using Genetic Programming",
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journal = "Journal of Hydrology",
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volume = "454-455",
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pages = "26--41",
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year = "2012",
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ISSN = "0022-1694",
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DOI = "doi:10.1016/j.jhydrol.2012.05.033",
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URL = "http://www.sciencedirect.com/science/article/pii/S002216941200409X",
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keywords = "genetic algorithms, genetic programming, El
Nino-Southern Oscillation (ENSO), Equatorial Indian
Ocean Oscillation (EQUINOO), Indian Summer Monsoon
Rainfall (ISMR)",
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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
Citations