DDDAS predictions for water spills

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

doi:10.1007/978-3-540-69389-5_8

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 proceeding › Conference contribution › peer-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 language	English
Title of host publication	Computational Science - ICCS 2008 - 8th International Conference, Proceedings
Pages	54-63
Number of pages	10
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-540-69389-5_8
State	Published - 2008
Event	8th International Conference on Computational Science, ICCS 2008 - Krakow, Poland Duration: Jun 23 2008 → Jun 25 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	5103 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Computational Science, ICCS 2008
Country/Territory	Poland
City	Krakow
Period	6/23/08 → 6/25/08

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-69389-5_8

Cite this

Douglas, C. C., Dostert, P., Efendiev, Y., Ewing, R. E., Li, D., & Lodder, R. A. (2008). DDDAS predictions for water spills. In Computational Science - ICCS 2008 - 8th International Conference, Proceedings (PART 3 ed., pp. 54-63). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5103 LNCS, No. PART 3). https://doi.org/10.1007/978-3-540-69389-5_8

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title = "DDDAS predictions for water spills",

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.",

author = "Douglas, {Craig C.} and Paul Dostert and Yalchin Efendiev and Ewing, {Richard E.} and Deng Li and Lodder, {Robert A.}",

year = "2008",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Douglas, CC, Dostert, P, Efendiev, Y, Ewing, RE, Li, D & Lodder, RA 2008, DDDAS predictions for water spills. in Computational Science - ICCS 2008 - 8th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 5103 LNCS, pp. 54-63, 8th International Conference on Computational Science, ICCS 2008, Krakow, Poland, 6/23/08. https://doi.org/10.1007/978-3-540-69389-5_8

DDDAS predictions for water spills. / Douglas, Craig C.; Dostert, Paul; Efendiev, Yalchin et al.
Computational Science - ICCS 2008 - 8th International Conference, Proceedings. PART 3. ed. 2008. p. 54-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5103 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lodder, Robert A.

PY - 2008

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AB - 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.

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Douglas CC, Dostert P, Efendiev Y, Ewing RE, Li D, Lodder RA. DDDAS predictions for water spills. In Computational Science - ICCS 2008 - 8th International Conference, Proceedings. PART 3 ed. 2008. p. 54-63. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-540-69389-5_8

DDDAS predictions for water spills

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