Predicting Baseflow Using Genetic Programing

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Meshgi:2014:HIC,
  author =       "Ali Meshgi and Petra Schmitter and Vladan Babovic and 
                 Ting Fong May Chui",
  title =        "Predicting Baseflow Using Genetic Programing",
  booktitle =    "11th International Conference on Hydroinformatics",
  year =         "2014",
  address =      "New York, USA",
  month =        aug # " 17-21",
  organisation = "IAHR/IWA Joint Committee on Hydroinformatics",
  keywords =     "genetic algorithms, genetic programming, Baseflow,
                 Recursive Digital Filters, Numerical modeling",
  isbn13 =       "978-0-692-28129-1",
  URL =          "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1589/1153.pdf",
  size =         "8 pages",
  abstract =     "Developing reliable methods to estimate baseflow has
                 been a subject of research interest over the past
                 decades due to its importance in catchment response and
                 sustainable watershed management (e.g. ground water
                 recharge vs. extraction). Limitations and complexities
                 of existing methods have been addressed by a number of
                 researchers. For instance, physically based numerical
                 models are complex, requiring substantial computational
                 time and data which may not be always available.
                 Artificial Intelligence (AI) tools such as Genetic
                 Programming (GP) have been used widely to reduce the
                 challenges associated with complex hydrological systems
                 without losing the physical meanings. However, up to
                 date, in the absence of complex numerical models,
                 baseflow is frequently estimated using statistically
                 derived empirical equations without significant
                 physical insights. This study investigates the
                 capability of GP in estimating baseflow for a small
                 intensively monitored semi-urban catchment (8.5 ha)
                 located in Singapore. The validated GP model for
                 Singapore is tested on a larger vegetation-dominated
                 basin located in the USA (24 km2). For each study case,
                 the baseflow predictions from the established GP model
                 were compared with baseflow estimates obtained through
                 the use of the Recursive Digital Filters (RDFs) method
                 using the available discharge time series. The
                 Nash-Sutcliffe efficiency of 0.94 and 0.91 are found
                 with comparing the baseflow estimated by GP and RDFs in
                 the first and second study sites, respectively. These
                 results indicate that GP is an effective tool in
                 determining baseflow. Overall, this study proposes a
                 new approach which can predict the baseflow with only
                 information on three parameters including minimum
                 baseflow in dry period, area of the catchment and
                 groundwater table.",
  notes =        "http://www.hic2014.org/xmlui/",
}

Genetic Programming entries for Ali Meshgi Petra Schmitter Vladan Babovic Ting Fong May Chui

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