A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events

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@Article{DANANDEHMEHR2017397,
  author =       "Ali {Danandeh Mehr} and Vahid Nourani and 
                 Bahrudin Hrnjica and Amir Molajou",
  title =        "A binary genetic programing model for teleconnection
                 identification between global sea surface temperature
                 and local maximum monthly rainfall events",
  journal =      "Journal of Hydrology",
  year =         "2017",
  volume =       "555",
  pages =        "397--406",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Maximum
                 monthly rainfall, Sea surface temperature, Binary
                 classification, Forecasting",
  ISSN =         "0022-1694",
  URL =          "http://www.sciencedirect.com/science/article/pii/S002216941730714X",
  DOI =          "doi:10.1016/j.jhydrol.2017.10.039",
  abstract =     "The effectiveness of genetic programming (GP) for
                 solving regression problems in hydrology has been
                 recognized in recent studies. However, its capability
                 to solve classification problems has not been
                 sufficiently explored so far. This study develops and
                 applies a novel classification-forecasting model,
                 namely Binary GP (BGP), for teleconnection studies
                 between sea surface temperature (SST) variations and
                 maximum monthly rainfall (MMR) events. The BGP
                 integrates certain types of data pre-processing and
                 post-processing methods with conventional GP engine to
                 enhance its ability to solve both regression and
                 classification problems simultaneously. The model was
                 trained and tested using SST series of Black Sea,
                 Mediterranean Sea, and Red Sea as potential predictors
                 as well as classified MMR events at two locations in
                 Iran as predicted. Skill of the model was measured in
                 regard to different rainfall thresholds and SST lags
                 and compared to that of the hybrid decision
                 tree-association rule (DTAR) model available in the
                 literature. The results indicated that the proposed
                 model can identify potential teleconnection signals of
                 surrounding seas beneficial to long-term forecasting of
                 the occurrence of the classified MMR events.",
}

Genetic Programming entries for Ali Danandeh Mehr Vahid Nourani Bahrudin Hrnjica Amir Molajou

Citations