Interactive Visualization of Simulation Data for Geospatial Decision Support

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dc.contributor.authorCornel, Daniel
dc.date.accessioned2020-11-02T13:54:54Z
dc.date.available2020-11-02T13:54:54Z
dc.date.issued2020-02-12
dc.description.abstractFloods are catastrophic events that claim thousands of human lives every year. For the prediction of these events, interactive decision support systems with integrated flood simulation have become a vital tool. Recent technological advances made it possible to simulate flooding scenarios of unprecedented scale and resolution, resulting in very large time-dependent data. The amount of simulation data is further amplified by the use of ensemble simulations to make predictions more robust, yielding high-dimensional and uncertain data far too large for manual exploration. New strategies are therefore needed to filter these data and to display only the most important information to support domain experts in their daily work. This includes the communication of results to decision makers, emergency services, stakeholders, and the general public. A modern decision support system has to be able to provide visual results that are useful for domain experts, but also comprehensible for larger audiences. Furthermore, for an efficient workflow, the entire process of simulation, analysis, and visualization has to happen in an interactive fashion, putting serious time constraints on the system. In this thesis, we present novel visualization techniques for time-dependent and uncertain flood, logistics, and pedestrian simulation data for an interactive decision support system. As the heterogeneous tasks in flood management require very diverse visualizations for different target audiences, we provide solutions to key tasks in the form of task-specific and user-specific visualizations. This allows the user to show or hide detailed information on demand to obtain comprehensible and aesthetic visualizations to support the task at hand. In order to identify the impact of flooding incidents on a building of interest, only a small subset of all available data is relevant, which is why we propose a solution to isolate this information from the massive simulation data. To communicate the inherent uncertainty of resulting predictions of damages and hazards, we introduce a consistent style for visualizing the uncertainty within the geospatial context. Instead of directly showing simulation data in a time-dependent manner, we propose the use of bidirectional flow maps with multiple components as a simplified representation of arbitrary material flows. For the communication of flood risks in a comprehensible way, however, the direct visualization of simulation data over time can be desired. Apart from the obvious challenges of the complex simulation data, the discrete nature of the data introduces additional problems for the realistic visualization of water surfaces, for which we propose robust solutions suitable for real-time applications. All of our findings have been acquired through a continuous collaboration with domain experts from several flood-related fields of work. The thorough evaluation of our work by these experts confirms the relevance and usefulness of our presented solutions.en_US
dc.description.sponsorshipVRVis is funded by BMVIT, BMDW, Styria, SFG and Vienna Business Agency in the scope of COMET - Competence Centers for Excellent Technologies (854174) which is managed by FFG. This work was also supported by the preceding project 843272. Vienna Science and Technology Fund (WWTF): ICT12-009 (Scenario Pool). Austrian Science Fund (FWF): W1219-N22 (Vienna Doctoral Programme on Water Resource Systems) and P24597-N23 (VISAR).en_US
dc.identifier.urihttps://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-135226
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/2632949
dc.language.isoen_USen_US
dc.publisherTechnische Universität Wien, Vienna, Austriaen_US
dc.subjectInteractive visualizationen_US
dc.subjectFlood simulationen_US
dc.subjectFlood managementen_US
dc.titleInteractive Visualization of Simulation Data for Geospatial Decision Supporten_US
dc.typeThesisen_US
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