A predictive Bayesian data-derived multi-modal Gaussian model ofsunken oil mass

M. Angelica Echavarria-Gregory, James Douglas Englehardt

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Hydrodynamic modeling of sunken oil is hindered by insufficient knowledge of bottom currents. In this paper, the development of a predictive Bayesian model, SOSim, for inferring the location of sunken oil in time, based on sparse, qualitative or quantitative near-real time field data collected immediately following a spill, is described. Mapped output represents unconditional multi-modal Gaussian relative probabilities of finding oil at points across a relatively flat bay bottom, in time. The method of images is extended to address curvilinear reflecting shorelines. The model is demonstrated to locate the entire DBL-152 spill, given field data covering part of the area affected, and to project oil movement near curvilinear shoreline boundaries given simulated field data at two points in time. Limitations include accountability for discontinuous boundary conditions. Further development is recommended, including development of capability for accepting bathymetric data, for modeling continuous oil releases, and for 3-D modeling of suspended oil.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalEnvironmental Modelling and Software
Volume69
DOIs
StatePublished - Jul 1 2015

Fingerprint

oil
Hazardous materials spills
shoreline
modeling
bottom current
accountability
Oils
boundary condition
Hydrodynamics
hydrodynamics
Boundary conditions

Keywords

  • Bayesian
  • Emergency response
  • Gaussian
  • Statistical model
  • Stochastic
  • Sunken oil

ASJC Scopus subject areas

  • Ecological Modeling
  • Environmental Engineering
  • Software

Cite this

A predictive Bayesian data-derived multi-modal Gaussian model ofsunken oil mass. / Echavarria-Gregory, M. Angelica; Englehardt, James Douglas.

In: Environmental Modelling and Software, Vol. 69, 01.07.2015, p. 1-13.

Research output: Contribution to journalArticle

@article{a9fc6168d27848878137babc77d3417e,
title = "A predictive Bayesian data-derived multi-modal Gaussian model ofsunken oil mass",
abstract = "Hydrodynamic modeling of sunken oil is hindered by insufficient knowledge of bottom currents. In this paper, the development of a predictive Bayesian model, SOSim, for inferring the location of sunken oil in time, based on sparse, qualitative or quantitative near-real time field data collected immediately following a spill, is described. Mapped output represents unconditional multi-modal Gaussian relative probabilities of finding oil at points across a relatively flat bay bottom, in time. The method of images is extended to address curvilinear reflecting shorelines. The model is demonstrated to locate the entire DBL-152 spill, given field data covering part of the area affected, and to project oil movement near curvilinear shoreline boundaries given simulated field data at two points in time. Limitations include accountability for discontinuous boundary conditions. Further development is recommended, including development of capability for accepting bathymetric data, for modeling continuous oil releases, and for 3-D modeling of suspended oil.",
keywords = "Bayesian, Emergency response, Gaussian, Statistical model, Stochastic, Sunken oil",
author = "Echavarria-Gregory, {M. Angelica} and Englehardt, {James Douglas}",
year = "2015",
month = "7",
day = "1",
doi = "10.1016/j.envsoft.2015.02.014",
language = "English (US)",
volume = "69",
pages = "1--13",
journal = "Environmental Modelling and Software",
issn = "1364-8152",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - A predictive Bayesian data-derived multi-modal Gaussian model ofsunken oil mass

AU - Echavarria-Gregory, M. Angelica

AU - Englehardt, James Douglas

PY - 2015/7/1

Y1 - 2015/7/1

N2 - Hydrodynamic modeling of sunken oil is hindered by insufficient knowledge of bottom currents. In this paper, the development of a predictive Bayesian model, SOSim, for inferring the location of sunken oil in time, based on sparse, qualitative or quantitative near-real time field data collected immediately following a spill, is described. Mapped output represents unconditional multi-modal Gaussian relative probabilities of finding oil at points across a relatively flat bay bottom, in time. The method of images is extended to address curvilinear reflecting shorelines. The model is demonstrated to locate the entire DBL-152 spill, given field data covering part of the area affected, and to project oil movement near curvilinear shoreline boundaries given simulated field data at two points in time. Limitations include accountability for discontinuous boundary conditions. Further development is recommended, including development of capability for accepting bathymetric data, for modeling continuous oil releases, and for 3-D modeling of suspended oil.

AB - Hydrodynamic modeling of sunken oil is hindered by insufficient knowledge of bottom currents. In this paper, the development of a predictive Bayesian model, SOSim, for inferring the location of sunken oil in time, based on sparse, qualitative or quantitative near-real time field data collected immediately following a spill, is described. Mapped output represents unconditional multi-modal Gaussian relative probabilities of finding oil at points across a relatively flat bay bottom, in time. The method of images is extended to address curvilinear reflecting shorelines. The model is demonstrated to locate the entire DBL-152 spill, given field data covering part of the area affected, and to project oil movement near curvilinear shoreline boundaries given simulated field data at two points in time. Limitations include accountability for discontinuous boundary conditions. Further development is recommended, including development of capability for accepting bathymetric data, for modeling continuous oil releases, and for 3-D modeling of suspended oil.

KW - Bayesian

KW - Emergency response

KW - Gaussian

KW - Statistical model

KW - Stochastic

KW - Sunken oil

UR - http://www.scopus.com/inward/record.url?scp=84924971163&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84924971163&partnerID=8YFLogxK

U2 - 10.1016/j.envsoft.2015.02.014

DO - 10.1016/j.envsoft.2015.02.014

M3 - Article

AN - SCOPUS:84924971163

VL - 69

SP - 1

EP - 13

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

ER -