Potential of Genetic Programming in Hydroclimatic Prediction of Droughts: An Indian Perspective

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  author =       "Rajib Maity and Kironmala Chanda",
  title =        "Potential of Genetic Programming in Hydroclimatic
                 Prediction of Droughts: An Indian Perspective",
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  chapter =      "15",
  pages =        "381--398",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_15",
  abstract =     "Past studies have established the presence of
                 hydroclimatic teleconnection between hydrological
                 variables across the world and large-scale coupled
                 oceanic-atmospheric circulation patterns, such as El
                 Nino-Southern Oscillation (ENSO), Equatorial Indian
                 Ocean Oscillation (EQUINOO), Pacific Decadal
                 Oscillation (PDO), Atlantic Multi-decadal Oscillation
                 (AMO), Indian Ocean Dipole (IOD). For the purpose of
                 modelling hydroclimatic teleconnections, Artificial
                 intelligence (AI) tools including Genetic Programming
                 (GP) have been successfully applied in several studies.
                 In this chapter, we attempt to explore the potential of
                 Linear Genetic Programming (LGP) for the prediction of
                 droughts using the local and global climate inputs in
                 the context of Indian hydroclimatology. The global
                 anomaly fields of five different climate variables,
                 namely Sea Surface Temperature (SST), Surface Pressure
                 (SP), Air Temperature (AT), Wind Speed (WS) and Total
                 Precipitable Water (TPW), are explored during extreme
                 rainfall events (isolated by standardizing monthly
                 rainfall from 1959 to 2010 using an anomaly based
                 index) to identify the Global Climate Pattern (GCP).
                 The GCP for the target area is characterized by 14
                 variables where each variable is designated by a
                 particular climate variable from a distinct zone on the
                 globe. The potential of a LGP-based approach is
                 explored to extract the climate information hidden in
                 the GCP and to predict the ensuing drought status. The
                 LGP based approach is found to produce reasonably good
                 results. Many of the dry and wet events observed during
                 the last few decades are found to be predicted

Genetic Programming entries for Rajib Maity Kironmala Chanda