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.
Recommended Citation
J. Hale et al., "Flood Management Deep Learning Model Inputs: A Review of Necessary Data and Predictive Tools," 2019 International Annual Conference Proceedings of the American Society for Engineering Management and 40th Meeting Celebration: A Systems Approach to Engineering Management Solutions, ASEM 2019, American Society for Engineering Education (ASEE), Oct 2019.
Meeting Name
American Society for Engineering Management Conference, ASEM 2019 (2019: Oct. 23-26, Philadelphia, PA)
Department(s)
Engineering Management and Systems Engineering
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
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.