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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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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