Miola, MariannaCabiddu, DanielaPittaluga, SimoneVetuschi Zuccolini, MarinoCabiddu, DanielaSchneider, TeseoAllegra, DarioCatalano, Chiara EvaCherchi, GianmarcoScateni, Riccardo2022-11-082022-11-082022978-3-03868-191-52617-4855https://doi.org/10.2312/stag.20221265https://diglib.eg.org:443/handle/10.2312/stag20221265To 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies -> Mesh geometry models; Uncertainty quantification; Software and its engineering -> Software prototyping; Applied computing -> Environmental sciencesComputing methodologiesMesh geometry modelsUncertainty quantificationSoftware and its engineeringSoftware prototypingApplied computingEnvironmental sciences10.2312/stag.20221265123-1253 pages