Inverse groundwater modeling with emphasis on model parameterization

Kourakos, G; Mantoglou, A

dc.contributor.author	Kourakos, G	en
dc.contributor.author	Mantoglou, A	en
dc.date.accessioned	2014-03-01T02:09:23Z
dc.date.available	2014-03-01T02:09:23Z
dc.date.issued	2012	en
dc.identifier.issn	00431397	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29834
dc.subject.other	A-transform	en
dc.subject.other	Anisotropic distribution	en
dc.subject.other	Aquifer parameters	en
dc.subject.other	B-spline surface	en
dc.subject.other	Calibration error	en
dc.subject.other	Complex model	en
dc.subject.other	Control point	en
dc.subject.other	Decision variables	en
dc.subject.other	Error curves	en
dc.subject.other	Error minimization	en
dc.subject.other	Inverse groundwater modeling	en
dc.subject.other	Inverse methods	en
dc.subject.other	Inverse modeling	en
dc.subject.other	Linear least squares	en
dc.subject.other	Model complexity	en
dc.subject.other	Model parameterization	en
dc.subject.other	Parameter values	en
dc.subject.other	Parameterizations	en
dc.subject.other	Pareto set	en
dc.subject.other	Potential solutions	en
dc.subject.other	Prediction errors	en
dc.subject.other	Single objective optimization	en
dc.subject.other	Standard genetic algorithm	en
dc.subject.other	Transmissivity	en
dc.subject.other	Anisotropy	en
dc.subject.other	Aquifers	en
dc.subject.other	Calibration	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Groundwater resources	en
dc.subject.other	Inverse problems	en
dc.subject.other	Mathematical operators	en
dc.subject.other	Multiobjective optimization	en
dc.subject.other	Parameterization	en
dc.subject.other	anisotropy	en
dc.subject.other	calibration	en
dc.subject.other	complexity	en
dc.subject.other	error correction	en
dc.subject.other	genetic algorithm	en
dc.subject.other	groundwater	en
dc.subject.other	hydrological modeling	en
dc.subject.other	inverse analysis	en
dc.subject.other	numerical model	en
dc.subject.other	parameterization	en
dc.subject.other	prediction	en
dc.subject.other	transmissivity	en
dc.title	Inverse groundwater modeling with emphasis on model parameterization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1029/2011WR011068	en
heal.identifier.secondary	http://dx.doi.org/10.1029/2011WR011068	en
heal.identifier.secondary	W05540	en
heal.publicationDate	2012	en
heal.abstract	This study develops an inverse method aiming to circumvent the subjective decision regarding model parameterization and complexity in inverse groundwater modeling. The number of parameters is included as a decision variable along with parameter values. A parameterization based on B-spline surfaces (BSS) is selected to approximate transmissivity, and genetic algorithms were selected to perform error minimization. A transform based on linear least squares (LLS) is developed, so that different parameterizations may be combined by standard genetic algorithm operators. First, three applications, with isotropic, anisotropic, and zoned aquifer parameters, are examined in a single objective optimization problem and the estimated transmissivity is found to be near the true one. Interestingly, in the anisotropic case, the algorithm converged to a solution with an anisotropic distribution of control points. Next, a single objective optimization with regularization, penalizing complex models, is considered, and last, the problem is expressed in a multiobjective optimization framework (MOO), where the goals are simultaneous minimization of calibration error and model complexity. The result of MOO is a Pareto set of potential solutions where the user can examine the tradeoffs between calibration error and model complexity and select the most suitable model. By comparing calibration with prediction errors, it appears, that the most promising models are the ones near a region where the rate of decrease of calibration error as model complexity increases drops (bend of error curve). This is a useful result of practical interest in real inverse modeling applications. © 2012. American Geophysical Union.	en
heal.journalName	Water Resources Research	en
dc.identifier.doi	10.1029/2011WR011068	en
dc.identifier.volume	48	en
dc.identifier.issue	5	en