Modeling Dynamics of Community Resilience to Extreme Events with Explainable Deep Learning-Reference-Cited by-同舟云学术

Modeling Dynamics of Community Resilience to Extreme Events with Explainable Deep Learning

Published:2023-05 Issue:2 Volume:24 Page:
ISSN:1527-6988
Container-title:Natural Hazards Review
language:en
Short-container-title:Nat. Hazards Rev.

Author:

Hao Haiyan¹,Wang Yan²^ORCID

Affiliation:

1. Ph.D. Candidate, Dept. of Urban and Regional Planning and Florida Institute for Built Environment Resilience, Univ. of Florida, Gainesville, FL 32601.

2. Assistant Professor, Dept. of Urban and Regional Planning and Florida Institute for Built Environment Resilience, Univ. of Florida, P.O. Box 115706, Gainesville, FL 32611 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

General Social Sciences,General Environmental Science,Civil and Structural Engineering,Building and Construction

Link

https://ascelibrary.org/doi/pdf/10.1061/NHREFO.NHENG-1696

Reference82 articles.

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2. Ancona M. E. Ceolini C. Öztireli and M. Gross. 2017. “Towards better understanding of gradient-based attribution methods for deep neural networks.” Preprint submitted November 16 2017. https://arxiv.org/abs/1711.06104.

3. Universality in network dynamics

4. Batty, M. 2009. “Cities as complex systems: Scaling, interaction, networks, dynamics and urban morphologies.” In Encyclopedia of complexity and systems science. New York: Springer. https://doi.org/10.1007/978-0-387-30440-3_69.

5. Béné C. 2013. “Towards a quantifiable measure of resilience.” Accessed September 25 2014. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.2040-0209.2013.00434.x.

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