Inferring groundwater system dynamics from hydrological time-series data

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@Article{Doglioni:2010:HSJ,
  author =       "Angelo Doglioni and Davide Mancarella and 
                 Vincenzo Simeone and Orazio Giustolisi",
  title =        "Inferring groundwater system dynamics from
                 hydrological time-series data",
  journal =      "Hydrological Sciences Journal",
  year =         "2010",
  volume =       "55",
  number =       "4",
  pages =        "593--608",
  keywords =     "genetic algorithms, genetic programming, groundwater,
                 conceptual model, ordinary differential equations,
                 evolutionary modelling, shallow aquifer",
  ISSN =         "0262-6667",
  URL =          "http://www.tandfonline.com/doi/abs/10.1080/02626661003747556",
  DOI =          "doi:10.1080/02626661003747556",
  size =         "16 pages",
  abstract =     "The problem of identifying and reproducing the
                 hydrological behaviour of groundwater systems can often
                 be set in terms of ordinary differential equations
                 relating the inputs and outputs of their physical
                 components under simplifying assumptions. Conceptual
                 linear and nonlinear models described as ordinary
                 differential equations are widely used in hydrology and
                 can be found in several studies. Groundwater systems
                 can be described conceptually as an interlinked
                 reservoir model structured as a series of nonlinear
                 tanks, so that the groundwater table can be schematised
                 as the water level in one of the interconnected tanks.
                 In this work, we propose a methodology for inferring
                 the dynamics of a groundwater system response to
                 rainfall, based on recorded time series data. The use
                 of evolutionary techniques to infer differential
                 equations from data in order to obtain their intrinsic
                 phenomenological dynamics has been investigated
                 recently by a few authors and is referred to as
                 evolutionary modelling. A strategy named Evolutionary
                 Polynomial Regression (EPR) has been applied to a real
                 hydrogeological system, the shallow unconfined aquifer
                 of Brindisi, southern Italy, for which 528 recorded
                 monthly data over a 44-year period are available. The
                 EPR returns a set of non-dominated models, as ordinary
                 differential equations, reproducing the system
                 dynamics. The choice of the representative model can be
                 made both on the basis of its performance against a
                 test data set and based on its incorporation of terms
                 that actually entail physical meaning with respect to
                 the of the system.",
  notes =        "In English.",
}

Genetic Programming entries for Angelo Doglioni Davide Mancarella Vincenzo Simeone Orazio Giustolisi

Citations