Inferring groundwater system dynamics from hydrological time-series data

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@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