Abstract

Current flood management models are often hampered by the lack of robust predictive analytics, as well as incomplete datasets for river basins prone to heavy flooding. This research uses a State-of-the-Art matrix (SAM) analysis and integrative literature review to categorize existing models by method and scope, then determines opportunities for integrating deep learning techniques to expand predictive capability. Trends in the SAM analysis are then used to determine geospatial characteristics of the region that can contribute to flash flood scenarios, as well as develop inputs for future modeling efforts. Preliminary progress on the selection of one urban and one rural test site are presented subject to available data and input from key stakeholders. The transportation safety or disaster planner can use these results to begin integrating deep learning methods in their planning strategies based on region-specific geospatial data and information.

Meeting Name

American Society for Engineering Management Conference, ASEM 2019 (2019: Oct. 23-26, Philadelphia, PA)

Department(s)

Engineering Management and Systems Engineering

Comments

The authors gratefully acknowledge partial support for this project through funding provided by the Missouri Department of Transportation, TR201912, and the Mid-America Transportation Center, 25-1121-0005-130.

Keywords and Phrases

Deep learning; Flood management; State-of-the-Art Matrix (SAM) analysis

International Standard Book Number (ISBN)

978-099751956-3

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2019 American Society for Engineering Management (ASEM), All rights reserved.

Publication Date

26 Oct 2019

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