Statistical spatio-temporal models with applications to natural processes
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Wei, Guanzhou
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Abstract
In response to increasingly frequent extreme natural events (e.g., floods, hurricanes, and wildfires), numerous Earth observation programs have been launched in recent decades. As the volume, resolution, and complexity of Earth-monitoring data increase, both opportunities and challenges arise in modeling and understanding the underlying natural processes. This thesis aims to develop statistical spatial-temporal models for analyzing and understanding critical natural processes using Earth observation data. In Chapter 1, we introduce the research background and discuss the challenges associated with Earth-monitoring data for extreme natural events. Chapter 2 explores power-line fire risk modeling for power delivery infrastructures. We propose a new spatio-temporal point process that captures both the instantaneous and historical effects of key environmental covariates on power-line fire risk, as well as the spatio-temporal dependency among different segments of the power delivery network. Chapter 3 develops a physics-informed statistical spatio-temporal model for wildfire aerosol propagation, leveraging multisource remote-sensing data streams and the advection-diffusion equation that governs the process. Chapter 4 extends a recently proposed Partial Differential Equation (PDE)-based statistical spatio-temporal model by incorporating a data-flipping method. This approach ensures that the physical spatial process becomes fully periodic and has a complete waveform without boundary discontinuities. Thus, the Gibbs phenomenon (GP) is eliminated even when the Fourier series is truncated in the PDE-based statistical spatio-temporal model. Chapter 5 concludes this thesis and discusses several potential research directions.
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2025-04-29
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Dissertation