Using Gaussian Stochastic Processes (GaSP) for Hazard Mapping

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Mentor: Elaine Spiller

Approach: Develop and evaluate efficient statistical surrogates for computationally complex computer models

Summary: Hazard mapping is an essential tool used to estimate the risk faced by residents living in areas susceptible to natural disasters. However, accurate computer models are often computationally complex and can take hours or even days to compute. This limits the utility of computer models during storms, where conditions are frequently changing. An alternative is to create a statistical surrogate using Gaussian stochastic process (GaSP) models that can be computed more efficiently than a computer model. In this project, we hope to apply this method to model landslides, using data gathered from previously conducted simulations.

Previous Student Researchers: John Bihn, Tao Cui, and Dakota Sullivan