Predicting flood damage using the Flood Peak Ratio and Giovanni Flooded Fraction - Code

Ghaedi, Hamed; Baroud, Hiba; Ferreira, Celso; Perrucci, Daniel; Reilly, Allison

Predicting flood damage using the Flood Peak Ratio and Giovanni Flooded Fraction - Code

dc.contributor.advisor	Reilly, Allison
dc.contributor.author	Ghaedi, Hamed
dc.contributor.author	Baroud, Hiba
dc.contributor.author	Ferreira, Celso
dc.contributor.author	Perrucci, Daniel
dc.contributor.author	Reilly, Allison
dc.date.accessioned	2022-07-11T18:36:11Z
dc.date.available	2022-07-11T18:36:11Z
dc.date.issued	2022-07-11
dc.description.abstract	A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.	en_US
dc.description.sponsorship	This work was made possible by the National Academies Gulf Research Program Early-Career Research Fellowship. Any opinions, findings, conclusions, or recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the National Academies. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.	en_US
dc.description.uri	https://doi.org/10.1371/journal.pone.0271230
dc.identifier	https://doi.org/10.13016/gyyb-bfgp
dc.identifier.uri	http://hdl.handle.net/1903/29061
dc.language.iso	en_US	en_US
dc.relation.isAvailableAt	A. James Clark School of Engineering	en_us
dc.relation.isAvailableAt	Civil & Environmental Engineering	en_us
dc.relation.isAvailableAt	Digital Repository at the University of Maryland	en_us
dc.relation.isAvailableAt	University of Maryland (College Park, MD)	en_us
dc.rights	Attribution-NonCommercial-ShareAlike 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/us/	*
dc.subject	National Flood Insurance Program	en_US
dc.subject	Flood peak ratio	en_US
dc.subject	Giovanni Flooded Fraction	en_US
dc.subject	machine learning	en_US
dc.title	Predicting flood damage using the Flood Peak Ratio and Giovanni Flooded Fraction - Code	en_US
dc.type	Software	en_US