Parameter estimation of atmospheric release incidents using maximal information collection

Reza Madankan, Puneet Singla, Tarunraj Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The effects of data measurement on source parameter estimation are studied. The concept of mutual information is applied to find the optimal location for each sensor to improve accuracy of the overall estimation process. For validation purposes, an advection - diffusion simulation code, called SCIPUFF, is used as a modeling testbed to study the effects of using dynamic data measurement. Bayesian inference framework is utilized for model-data fusion using stationary and mobile sensor networks, where in mobile sensors, the proposed approach is used to locate data observation sensors. As our numerical simulations show, using the proposed approach leads to a considerably better estimate of parameters comparing with stationary sensors.

Original languageEnglish (US)
Title of host publicationDynamic Data-Driven Environmental Systems Science - 1st International Conference, DyDESS 2014, Revised Selected Papers
EditorsAdrian Sandu, Sai Ravela
PublisherSpringer Verlag
Pages310-321
Number of pages12
ISBN (Print)9783319251370
DOIs
StatePublished - Jan 1 2015
Event1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014 - Cambridge, United States
Duration: Nov 5 2014Nov 7 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8964
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014
CountryUnited States
CityCambridge
Period11/5/1411/7/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Parameter estimation of atmospheric release incidents using maximal information collection'. Together they form a unique fingerprint.

  • Cite this

    Madankan, R., Singla, P., & Singh, T. (2015). Parameter estimation of atmospheric release incidents using maximal information collection. In A. Sandu, & S. Ravela (Eds.), Dynamic Data-Driven Environmental Systems Science - 1st International Conference, DyDESS 2014, Revised Selected Papers (pp. 310-321). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8964). Springer Verlag. https://doi.org/10.1007/978-3-319-25138-7_28