An empirical method for approximating stream baseflow time series using groundwater table fluctuations

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

@Article{Meshgi:2014:JH,
  author =       "Ali Meshgi and Petra Schmitter and Vladan Babovic and 
                 Ting Fong May Chui",
  title =        "An empirical method for approximating stream baseflow
                 time series using groundwater table fluctuations",
  journal =      "Journal of Hydrology",
  volume =       "519, Part A",
  pages =        "1031--1041",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming, Baseflow,
                 Empirical equation, Numerical modelling",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2014.08.033",
  URL =          "http://www.sciencedirect.com/science/article/pii/S002216941400643X",
  abstract =     "Summary Developing reliable methods to estimate stream
                 base flow has been a subject of interest due to its
                 importance in catchment response and sustainable
                 watershed management. However, to date, in the absence
                 of complex numerical models, base-flow is most commonly
                 estimated using statistically derived empirical
                 approaches that do not directly incorporate
                 physically-meaningful information. On the other hand,
                 Artificial Intelligence (AI) tools such as Genetic
                 Programming (GP) offer unique capabilities to reduce
                 the complexities of hydrological systems without losing
                 relevant physical information. This study presents a
                 simple-to-use empirical equation to estimate baseflow
                 time series using GP so that minimal data is required
                 and physical information is preserved. A groundwater
                 numerical model was first adopted to simulate baseflow
                 for a small semi-urban catchment (0.043 km2) located in
                 Singapore. GP was then used to derive an empirical
                 equation relating baseflow time series to time series
                 of groundwater table fluctuations, which are relatively
                 easily measured and are physically related to baseflow
                 generation. The equation was then generalised for
                 approximating baseflow in other catchments and
                 validated for a larger vegetation-dominated basin
                 located in the US (24 km2). Overall, this study used GP
                 to propose a simple-to-use equation to predict baseflow
                 time series based on only three parameters: minimum
                 daily baseflow of the entire period, area of the
                 catchment and groundwater table fluctuations. It serves
                 as an alternative approach for baseflow estimation in
                 un-gauged systems when only groundwater table and soil
                 information is available, and is thus complementary to
                 other methods that require discharge measurements.",
}

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

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