Data-driven modelling: some past experiences and new approaches

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

@Article{Solomatine:2008:JH,

• author = "Dimitri P. Solomatine and Avi Ostfeld",
• title = "Data-driven modelling: some past experiences and new approaches",
• journal = "Journal of Hydroinformatics",
• year = "2008",
• volume = "10",
• number = "1",
• pages = "3--22",
• month = jan,
• keywords = "genetic algorithms, genetic programming, computational intelligence, data-driven modelling, neural networks, ANN, river basin management, simulation modelling",
• ISSN = "1464-7141",
• URL = "http://www.iwaponline.com/jh/010/0003/0100003.pdf",
• DOI = "doi:10.2166/hydro.2008.015",
• size = "20 pages",
• abstract = "Physically based (process) models based on mathematical descriptions of water motion are widely used in river basin management. During the last decade the so-called data-driven models are becoming more and more common. These models rely upon the methods of computational intelligence and machine learning, and thus assume the presence of a considerable amount of data describing the modelled system's physics (i.e. hydraulic and/or hydrologic phenomena). This paper is a preface to the special issue on Data Driven Modelling and Evolutionary Optimisation for River Basin Management, and presents a brief overview of the most popular techniques and some of the experiences of the authors in data-driven modelling relevant to river basin management. It also identifies the current trends and common pitfalls, provides some examples of successful applications and mentions the research challenges.",
• notes = "GP mentioned with several other techniques",

}

Genetic Programming entries for Dimitri P Solomatine Avi Ostfeld

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