DDDAS predictions for water spills

Craig C. Douglas, Paul Dostert, Yalchin Efendiev, Richard E. Ewing, Deng Li, Robert A. Lodder

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Time based observations are the linchpin of improving predictions in any dynamic data driven application systems. Our predictions are based on solutions to differential equation models with unknown initial conditions and source terms. In this paper we want to simulate a waste spill by a water body, such as near an aquifer or in a river or bay. We employ sensors that can determine the contaminant spill location, where it is at a given time, and where it will go. We estimate initial conditions and source terms using better and new techniques, which improves predictions for a variety of data-driven models.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2008 - 8th International Conference, Proceedings
Pages54-63
Number of pages10
EditionPART 3
DOIs
StatePublished - Jul 28 2008
Event8th International Conference on Computational Science, ICCS 2008 - Krakow, Poland
Duration: Jun 23 2008Jun 25 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume5103 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Computational Science, ICCS 2008
CountryPoland
CityKrakow
Period06/23/0806/25/08

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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