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DDDAS Predictions for Water Spills

  • Craig C. Douglas
  • Paul Dostert
  • Yalchin Efendiev
  • Richard E. Ewing
  • Deng Li
  • Robert A. Lodder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5103)

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.

Keywords

Source Term Sensor Location Piecewise Constant Forward Simulation Initial Condition Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Darema, F.: Introduction to the ICCS 2007 Workshop on Dynamic Data Driven Applications Systems. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4487, pp. 955–962. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Dostert, P.: Uncertainty Quantification Using Multiscale Methods for Porous Media Flows. PhD thesis, Texas A & M University, College Station, TX (December 2007)Google Scholar
  3. 3.
    Douglas, C., Cole, M., Dostert, P., Efendiev, Y., Ewing, R., Haase, G., Hatcher, J., Iskandarani, M., Johnson, C., Lodder, R.: Dynamic Contaminant Identification in Water. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 393–400. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Douglas, C., Cole, M., Dostert, P., Efendiev, Y., Ewing, R., Haase, G., Hatcher, J., Iskandarani, M., Johnson, C., Lodder, R.: Dynamically identifying and tracking contaminants in water bodies. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4487, pp. 1002–1009. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Douglas, C., Efendiev, Y., Ewing, R., Ginting, V., Lazarov, R., Cole, M., Jones, G., Johnson, C.: Multiscale interpolation, backward in time error analysis for data-driven contaminant simulation. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3515, pp. 640–6470. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Douglas, C., Efendiev, Y., Ewing, R., Ginting, V., Lazarov, R.: Dynamic data driven simulations in stochastic environments. Computing 77, 321–332 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Dostert, P.: http://math.arizona.edu/~dostert/dddasweb (last visited 2/1/2008)

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Craig C. Douglas
    • 1
    • 4
  • Paul Dostert
    • 2
  • Yalchin Efendiev
    • 3
  • Richard E. Ewing
    • 3
  • Deng Li
    • 1
  • Robert A. Lodder
    • 1
  1. 1.University of KentuckyLexington
  2. 2.University of ArizonaTucson
  3. 3.Texas A & M University, College Station
  4. 4.Yale UniversityNew HavenUSA

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