Novel Urban Floodplain Modeling Methods for Applications in Coupling Surrogate Machine Learning Methods
| dc.contributor.advisor | Bedient, Philip B | en_US |
| dc.creator | Garcia, Matthew Steven | en_US |
| dc.date.accessioned | 2023-08-09T15:09:46Z | en_US |
| dc.date.created | 2023-05 | en_US |
| dc.date.issued | 2023-03-21 | en_US |
| dc.date.submitted | May 2023 | en_US |
| dc.date.updated | 2023-08-09T15:09:46Z | en_US |
| dc.description.abstract | The 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. | en_US |
| dc.embargo.lift | 2024-05-01 | en_US |
| dc.embargo.terms | 2024-05-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Garcia, 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>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/115079 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright 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. | en_US |
| dc.subject | Flood Modeling | en_US |
| dc.subject | HEC-RAS | en_US |
| dc.subject | Flood Warning | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Modularization | en_US |
| dc.title | Novel Urban Floodplain Modeling Methods for Applications in Coupling Surrogate Machine Learning Methods | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Civil and Environmental Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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