Data-driven modelling: some past experiences and new approaches

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Dimitri P. Solomatine and Avi Ostfeld",
  title =        "Data-driven modelling: some past experiences and new
  journal =      "Journal of Hydroinformatics",
  year =         "2008",
  volume =       "10",
  number =       "1",
  pages =        "3--22",
  keywords =     "genetic algorithms, genetic programming, computational
                 intelligence, data-driven modelling, neural networks,
                 river basin management, simulation modelling",
  ISSN =         "1464-7141",
  URL =          "",
  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
  notes =        "GP mentioned with several other techniques",

Genetic Programming entries for Dimitri P Solomatine Avi Ostfeld