Improving pluvial flood mapping resolution of large coarse models with deep learning
Author:
Affiliation:
1. Klesse College of Engineering, University of Texas at San Antonio, San Antonio, Texas, USA
2. WADI Lab - Hydraulics & Sanitary Engineering, University of Sao Paulo, Sao Carlos, Brazil
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/02626667.2024.2329268
Reference36 articles.
1. Deep learning methods for flood mapping: a review of existing applications and future research directions
2. Rapid spatio- temporal flood modelling via hydraulics-based graph neural networks;Bentivoglio R.;EGUsphere,2023
3. An ensemble neural network model for real-time prediction of urban floods
4. Modeling storm surge flooding of an urban area with particular reference to modeling uncertainties: A case study of Canvey Island, United Kingdom
5. Brunner, G., 2016. Hec-ras river analysis system, 2d modeling user’s manual, version 5.0. Davis: US Army Corps of Engineers, hydrologic engineering center.
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