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dc.contributor.authorEidsvik, Jo
dc.contributor.authorMartinelli, Gabriele
dc.contributor.authorBhattacharjya, Debarun
dc.date.accessioned2018-09-05T07:12:15Z
dc.date.available2018-09-05T07:12:15Z
dc.date.created2018-09-04T11:45:10Z
dc.date.issued2018
dc.identifier.citationStochastic environmental research and risk assessment (Print). 2018, 32 (4), 1163-1177.nb_NO
dc.identifier.issn1436-3240
dc.identifier.urihttp://hdl.handle.net/11250/2560813
dc.description.abstractSeveral risk and decision analysis applications are characterized by spatial elements: there are spatially dependent uncertain variables of interest, decisions are made at spatial locations, and there are opportunities for spatial data acquisition. Spatial dependence implies that the data gathered at one coordinate could inform and assist a decision maker at other locations as well, and one should account for this learning effect when analyzing and comparing information gathering schemes. In this paper, we present concepts and methods for evaluating sequential information gathering schemes in spatial decision situations. Static and sequential information gathering schemes are outlined using the decision theoretic notion of value of information, and we use heuristics for approximating the value of sequential information in large-size spatial applications. We illustrate the concepts using a Bayesian network example motivated from risks associated with CO2 sequestration. We present a case study from mining where there are risks of rock hazard in the tunnels, and information about the spatial distribution of joints in the rocks may lead to a better allocation of resources for choosing rock reinforcement locations. In this application, the spatial variables are modeled by a Gaussian process. In both examples there can be large values associated with adaptive information gathering.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringer Verlagnb_NO
dc.titleSequential information gathering schemes for spatial risk and decision analysis applicationsnb_NO
dc.typeJournal articlenb_NO
dc.description.versionsubmittedVersionnb_NO
dc.source.pagenumber1163-1177nb_NO
dc.source.volume32nb_NO
dc.source.journalStochastic environmental research and risk assessment (Print)nb_NO
dc.source.issue4nb_NO
dc.identifier.doihttps://doi.org/10.1007/s00477-017-1476-y
dc.identifier.cristin1606546
dc.description.localcodeThis is a pre-print of an article published in [Stochastic environmental research and risk assessment]. The final authenticated version is available online at: https://doi.org/10.1007/s00477-017-1476-ynb_NO
cristin.unitcode194,63,15,0
cristin.unitnameInstitutt for matematiske fag
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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