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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Maity:2015:hbgpa,

• 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 successfully.",

}

Genetic Programming entries for Rajib Maity Kironmala Chanda

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