MUSE: Modeling Uncertainty as a Support for Environment

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dc.contributor.authorMiola, Mariannaen_US
dc.contributor.authorCabiddu, Danielaen_US
dc.contributor.authorPittaluga, Simoneen_US
dc.contributor.authorVetuschi Zuccolini, Marinoen_US
dc.contributor.editorCabiddu, Danielaen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorAllegra, Darioen_US
dc.contributor.editorCatalano, Chiara Evaen_US
dc.contributor.editorCherchi, Gianmarcoen_US
dc.contributor.editorScateni, Riccardoen_US
dc.date.accessioned2022-11-08T11:44:46Z
dc.date.available2022-11-08T11:44:46Z
dc.date.issued2022
dc.description.abstractTo fully understand a Natural System, the representation of an environmental variable's distribution in 3D space is a mandatory and complex task. The challenge derives from a scarcity of samples number in the survey domain (e.g., logs in a reservoir, soil samples, fixed acquisition sampling stations) or an implicit difficulty in the in-situ measurement of parameters. Field or lab measurements are generally considered error-free, although not so. That aspect, combined with conceptual and numerical approximations used to model phenomena, makes the results intrinsically less performing, fading the interpretation. In this context, we design a computational infrastructure to evaluate spatial uncertainty in a multi-scenario application in Environment survey and protection, such as in environmental geochemistry, coastal oceanography, or infrastructure engineering. Our Research aims to expand the operative knowledge by developing an open-source stochastic tool, named MUSE, the acronym for Modeling Uncertainty as a Support for Environment. At this stage, the methodology mainly includes the definition of a flexible environmental data format, a geometry processing module to discretize the space, and geostatistics tools to evaluate the spatial continuity of sampled parameters, predicting random variable distribution. The implementation of the uncertainty module and the development of a graphic interface for ad-hoc visualization will be integrated as the next step. The poster summarizes research purposes, and MUSE computational code structure developed so far.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20221265
dc.identifier.isbn978-3-03868-191-5
dc.identifier.issn2617-4855
dc.identifier.pages123-125
dc.identifier.pages3 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20221265
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20221265
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Mesh geometry models; Uncertainty quantification; Software and its engineering -> Software prototyping; Applied computing -> Environmental sciences
dc.subjectComputing methodologies
dc.subjectMesh geometry models
dc.subjectUncertainty quantification
dc.subjectSoftware and its engineering
dc.subjectSoftware prototyping
dc.subjectApplied computing
dc.subjectEnvironmental sciences
dc.titleMUSE: Modeling Uncertainty as a Support for Environmenten_US
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