Data-driven modelling: some past experiences and new approaches
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Solomatine:2008:JH,
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author = "Dimitri P. Solomatine and Avi Ostfeld",
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title = "Data-driven modelling: some past experiences and new
approaches",
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journal = "Journal of Hydroinformatics",
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year = "2008",
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volume = "10",
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number = "1",
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pages = "3--22",
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month = jan,
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keywords = "genetic algorithms, genetic programming, computational
intelligence, data-driven modelling, neural networks,
ANN, river basin management, simulation modelling",
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ISSN = "1464-7141",
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URL = "http://www.iwaponline.com/jh/010/0003/0100003.pdf",
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DOI = "doi:10.2166/hydro.2008.015",
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size = "20 pages",
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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.",
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notes = "GP mentioned with several other techniques",
- }
Genetic Programming entries for
Dimitri P Solomatine
Avi Ostfeld
Citations