Calibration of aquifer models using neural network parameterization with a simple regularization

Mantoglou, A

dc.contributor.author	Mantoglou, A	en
dc.date.accessioned	2014-03-01T01:51:50Z
dc.date.available	2014-03-01T01:51:50Z
dc.date.issued	2002	en
dc.identifier.issn	01447815	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/26476
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0141745896&partnerID=40&md5=824e08bbf4e6e4a02770066e3cbd50bb	en
dc.subject	Aquifer modelling	en
dc.subject	Inverse modelling	en
dc.subject	Model calibration	en
dc.subject	Neural networks	en
dc.subject	Parameter estimation	en
dc.subject	Ridge functions	en
dc.subject.other	Calibration	en
dc.subject.other	Functions	en
dc.subject.other	Neural networks	en
dc.subject.other	Topology	en
dc.subject.other	Neural network parametrization	en
dc.subject.other	Aquifers	en
dc.subject.other	aquifer	en
dc.subject.other	artificial neural network	en
dc.subject.other	groundwater	en
dc.subject.other	parameterization	en
dc.subject.other	transmissivity	en
dc.title	Calibration of aquifer models using neural network parameterization with a simple regularization	en
heal.type	journalArticle	en
heal.publicationDate	2002	en
heal.abstract	A parameterization of transmissivity based on neural networks is developed. By defining an appropriate network topology it is possible to express the two-dimensional transmissivity map as a sum of one-dimensional functions similarly to the turning bands method. By selecting appropriate neural activation functions it is possible to get flexible and concise parameterizations that can handle gradual as well as abrupt large-scale changes of the real transmissivity map. A simple regularization is proposed that can dampen the erratic high frequency components in the estimated parameters. Various examples indicate that the proposed parameterization can handle well various types of transmissivity variations and is particularly suited when the true transmissivity map exhibits specific sorts of heterogeneity with large anisotropies or abrupt changes along lines.	en
heal.journalName	IAHS-AISH Publication	en
dc.identifier.issue	277	en
dc.identifier.spage	219	en
dc.identifier.epage	226	en