Time series Modelling Using Genetic Programming: An Application to Rainfall-Runoff Models

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

@InCollection{whigham:1999:aigp3,

• author = "Peter A. Whigham and Peter F. Crapper",
• title = "Time series Modelling Using Genetic Programming: An Application to Rainfall-Runoff Models",
• booktitle = "Advances in Genetic Programming 3",
• publisher = "MIT Press",
• year = "1999",
• editor = "Lee Spector and William B. Langdon and Una-May O'Reilly and Peter J. Angeline",
• chapter = "5",
• pages = "89--104",
• address = "Cambridge, MA, USA",
• month = jun,
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-262-19423-6",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/aigp3/ch05.pdf",
• language = "en",
• oai = "oai:CiteSeerXPSU:10.1.1.136.2581",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.2581",
• DOI = "doi:10.7551/mitpress/1110.003.0008",
• abstract = "We describes the application of a grammatically-based Genetic Programming system to discover rainfall-runoff relationships for two vastly different catchments. A context-free grammar is used to define the search space for the mathematical language used to express the evolving programs. A daily time series of rainfall-runoff is used to train the evolving population. A deterministic lumped parameter model, based on the unit hydrograph, is compared with the results of the evolved models on an independent data set. The favourable results of the Genetic Programming approach show that machine learning techniques are potentially a useful tool for developing hydrological models, especially when the relationship between rainfall and runoff is poor.",
• notes = "AiGP3 See http://cognet.mit.edu",

}

Genetic Programming entries for Peter Alexander Whigham Peter F Crapper

Citations