Predicting Rainfall in the Context of Rainfall Derivatives Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Cramer:2015:ieeeSSCI,
  author =       "Sam Cramer and Michael Kampouridis and 
                 Alex A. Freitas and Antonis Alexandridis",
  booktitle =    "2015 IEEE Symposium Series on Computational
                 Intelligence",
  title =        "Predicting Rainfall in the Context of Rainfall
                 Derivatives Using Genetic Programming",
  year =         "2015",
  pages =        "711--718",
  abstract =     "Rainfall is one of the most challenging variables to
                 predict, as it exhibits very unique characteristics
                 that do not exist in other time series data. Moreover,
                 rainfall is a major component and is essential for
                 applications that surround water resource planning. In
                 particular, this paper is interested in the prediction
                 of rainfall for rainfall derivatives. Currently in the
                 rainfall derivatives literature, the process of
                 predicting rainfall is dominated by statistical models,
                 namely using a Markov-chain extended with rainfall
                 prediction (MCRP). In this paper we outline a new
                 methodology to be carried out by predicting rainfall
                 with Genetic Programming (GP). This is the first time
                 in the literature that GP is used within the context of
                 rainfall derivatives. We have created a new tailored GP
                 to this problem domain and we compare the performance
                 of the GP and MCRP on 21 different data sets of cities
                 across Europe and report the results. The goal is to
                 see whether GP can outperform MCRP, which acts as a
                 benchmark. Results indicate that in general GP
                 significantly outperforms MCRP, which is the dominant
                 approach in the literature.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SSCI.2015.108",
  month =        dec,
  notes =        "Sch. of Comput., Univ. of Kent, Canterbury, UK

                 Also known as \cite{7376682}",
}

Genetic Programming entries for Sam Cramer Michael Kampouridis Alex Alves Freitas Antonis K Alexandridis

Citations