Miola, MariannaCabiddu, DanielaMortara, MichelaPittaluga, SimoneSorgente, TommasoZuccolini, Marino VetuschiCaputo, ArielGarro, ValeriaGiachetti, AndreaCastellani, UmbertoDulecha, Tinsae Gebrechristos2024-11-112024-11-112024978-3-03868-265-32617-4855https://doi.org/10.2312/stag.20241350https://diglib.eg.org/handle/10.2312/stag20241350Modeling the distribution of environmental variables across spatial domains presents significant challenges. Geostatistics offers a robust set of tools for accurately predicting values and associated uncertainties at unsampled locations, accounting for spatial correlations. However, these tools are often constrained by their reliance on structured domain representations, limiting their flexibility in modeling complex or irregular structures. By exploring the use of unstructured meshes, we can achieve a more efficient and accurate representation of localized phenomena, thereby enhancing our ability to model spatial patterns. Our current efforts are focused on integrating unstructured meshes into the geostatistical modeling pipeline, encompassing everything from mesh generation (and possibly refinement) to their application in stochastic simulation and the segmentation of the domain into regions where the distribution of variables is homogeneous. Preliminary results are promising, demonstrating the potentialities of this innovative approach.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Mesh geometry models; Volumetric models; Mathematics of computing → Distribution functions; Applied computing → Environmental sciencesComputing methodologies → Mesh geometry modelsVolumetric modelsMathematics of computing → Distribution functionsApplied computing → Environmental sciences10.2312/stag.202413503 pages