Uncovering Drivers of Atmospheric River Flood Damage Using Interpretable Machine Learning-Reference-Cited by-同舟云学术

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Uncovering Drivers of Atmospheric River Flood Damage Using Interpretable Machine Learning

Published:2024-08 Issue:3 Volume:25 Page:
ISSN:1527-6988
Container-title:Natural Hazards Review
language:en
Short-container-title:Nat. Hazards Rev.

Author:

Bowers Corinne¹^ORCID,Serafin Katherine A.²^ORCID,Baker Jack W.³^ORCID

Affiliation:

1. Dept. of Civil and Environmental Engineering, Stanford Univ., 450 Jane Stanford Way, Stanford, CA 94305; presently, U.S. Geological Survey, Reston, VA 20192 (corresponding author). ORCID: .

2. Professor, Dept. of Geography, Univ. of Florida, Gainesville, FL 32611. ORCID:

3. Professor, Dept. of Civil and Environmental Engineering, Stanford Univ., 450 Jane Stanford Way, Stanford, CA 94305. ORCID:

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/NHREFO.NHENG-1995

Reference77 articles.

1. ACS (American Community Survey). 2023a. “DP04: Selected housing characteristics 2009-2021.” Accessed November 21 2023. https://data.census.gov/.

2. ACS (American Community Survey). 2023b. “DP05: ACS demographic and housing estimates 2009-2021.” Accessed November 21 2023. https://data.census.gov/.

3. Leveraging machine learning for predicting flash flood damage in the Southeast US

4. Visualizing the effects of predictor variables in black box supervised learning models

5. What drives households to buy flood insurance? New evidence from Georgia

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