Bedient, Philip B2023-08-092023-052023-03-21May 2023Garcia, Matthew Steven. "Novel Urban Floodplain Modeling Methods for Applications in Coupling Surrogate Machine Learning Methods." (2023) Diss., Rice University. <a href="https://hdl.handle.net/1911/115079">https://hdl.handle.net/1911/115079</a>.https://hdl.handle.net/1911/115079The work shown is a solution to the limitations of long-term use for surrogate machine learning (ML) models on dynamic domains for the purpose of improved flood warning systems. The solution includes three major components, including a single model for combined flood control structural operations and inundation mapping; the modularization of a single model to minimize the computational cost for future domain updates; and a model input bootstrapping method to leverage observations while minimizing biases in the resulting surrogate ML training dataset. Together, these solutions are tested with various ML architectures to prove viability and highlight the final hurdles for implementation.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Flood ModelingHEC-RASFlood WarningMachine LearningModularizationThesis2023-08-09