Modeling Dynamics of Community Resilience to Extreme Events with Explainable Deep Learning
Abstract
Community resilience provides a paradigm guiding communities’ preparedness and mitigation efforts to counter the increasing risks of extreme events (EEs). Knowledge regarding how communities perform during and after EEs can inform proactive resilience enhancement practices. However, the EE impacts on impacted communities are influenced by multiple variables and their implicit interactions, which are difficult to be understood and modeled with conventional mathematical or statistical models. Thus, we calibrate a spatio-temporal deep learning model to capture the dynamics of impacted communities and use explainable artificial intelligence (XAI) approach to interpret the influence of communities’ social and physical properties on EE impacts. Specifically, we couple graph convolutional neural network (GCN) and long short-term memory (LSTM) to model the mobility dynamics for 666 census tracts (i.e., communities) in 14 medium-sized US cities affected by recent hurricanes. The model takes both static variables characterizing communities’ preexisting conditions and dynamic variables depicting the changing hazard exposure. The interpretation of the model predictions based on DeepLIFT shows that community resilience, inferred from the perturbation of human mobility in this research, is highly event and geography dependent. Variables including walkability, green spaces, and civil participation generally contribute to fewer mobility perturbations, i.e., resilience contributive, while automobile-oriented accessibility and social vulnerability lead to more mobility perturbations when hazard conditions are controlled. This study empirically validates the mediative role of communities’ social and physical properties on EE impacts and promotes more data-driven approaches for understanding and anticipating complex, dynamic, and place-specific community resilience.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Data are available from the corresponding author upon reasonable request.
Acknowledgments
This material is based on work supported by the University of Florida’s faculty start-up funds and the Graduate School Fellowship Award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the University of Florida.
References
Allan, P., M. Bryant, C. Wirsching, D. Garcia, and M. Teresa Rodriguez. 2013. “The influence of urban morphology on the resilience of cities following an earthquake.” J. Urban Des. 18 (2): 242–262. https://doi.org/10.1080/13574809.2013.772881.
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.
Barzel, B., and A. L. Barabási. 2013. “Universality in network dynamics.” Nat. Phys. 9 (10): 673–681. https://doi.org/10.1038/nphys2741.
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.
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.
Bettencourt, L. M. 2015. “Cities as complex systems.” Chap. 10 in Modeling complex systems for public policies, 217–236, Brasilia, Brazil: Institute for Applied Economic Research.
Brody, S. D., W. E. Highfield, R. Blessing, T. Makino, and C. C. Shepard. 2017. “Evaluating the effects of open space configurations in reducing flood damage along the Gulf of Mexico coast.” Landscape Urban Plann. 167 (6): 225–231. https://doi.org/10.1016/j.landurbplan.2017.07.003.
Bruneau, M., S. E. Chang, R. T. Eguchi, G. C. Lee, T. D. O’Rourke, A. M. Reinhorn, and D. Von Winterfeldt. 2003. “A framework to quantitatively assess and enhance the seismic resilience of communities.” Earthquake Spectra 19 (4): 733–752. https://doi.org/10.1193/1.1623497.
Burger, A., W. G. Kennedy, and A. Crooks. 2021. “Organizing theories for disasters into a complex adaptive system framework.” Urban Sci. 5 (3): 61. https://doi.org/10.3390/urbansci5030061.
Cai, H., N. S. Lam, L. Zou, and Y. Qiang. 2018. “Modeling the dynamics of community resilience to coastal hazards using a Bayesian network.” Ann. Am. Assoc. Geogr. 108 (5): 1260–1279. https://doi.org/10.1080/24694452.2017.1421896.
Cai, H., N. S. N. Lam, L. Zou, Y. Qiang, and K. Li. 2016. “Assessing community resilience to coastal hazards in the Lower Mississippi River Basin.” Water 8 (2): 46. https://doi.org/10.3390/w8020046.
Carpenter, S., B. Walker, J. M. Anderies, and N. Abel. 2001. “From metaphor to measurement: Resilience of what to what?” Ecosystems 4 (8): 765–781. https://doi.org/10.1007/s10021-001-0045-9.
CDC/ATSDR. 2018. “Social vulnerability index 2018 database US.” Accessed May 1, 2022. https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html.
Chen, Z., Z. Gong, S. Yang, Q. Ma, and C. Kan. 2020. “Impact of extreme weather events on urban human flow: A perspective from location-based service data.” Comput. Environ. Urban Syst. 83 (5): 101520. https://doi.org/10.1016/j.compenvurbsys.2020.101520.
Cumming, G. S., G. Barnes, S. Perz, M. Schmink, K. E. Sieving, J. Southworth, and T. Van Holt. 2005. “An exploratory framework for the empirical measurement of resilience.” Ecosystems 8 (8): 975–987. https://doi.org/10.1007/s10021-005-0129-z.
Cutter, S. L. 2016. “Resilience to what? Resilience for whom?” Geogr. J. 182 (2): 110–113. https://doi.org/10.1111/geoj.12174.
Cutter, S. L., K. D. Ash, and C. T. Emrich. 2014. “The geographies of community disaster resilience.” Global Environ. Change 29 (41): 65–77. https://doi.org/10.1016/j.gloenvcha.2014.08.005.
Cutter, S. L., L. Barnes, M. Berry, C. Burton, E. Evans, E. Tate, and J. Webb. 2008. “A place-based model for understanding community resilience to natural disasters.” Global Environ. Change 18 (4): 598–606. https://doi.org/10.1016/j.gloenvcha.2008.07.013.
Cutter, S. L., B. J. Boruff, and W. L. Shirley. 2003. “Social vulnerability to environmental hazards.” Soc. Sci. Q. 84 (2): 242–261. https://doi.org/10.1111/1540-6237.8402002.
Cutter, S. L., C. T. Emrich, M. Gall, S. Harrison, R. R. McCaster, S. Derakhshan, and E. Pham. 2019. Existing longitudinal data and systems for measuring the human dimensions of resilience, health, and well-being in the gulf coast. Washington, DC: Gulf Res Program.
FCC (Federal Communication Commission). 2018. “Form 477 census tract data on internet access services.” Accessed May 1, 2022. https://www.fcc.gov/form-477-census-tract-data-internet-access-services.
FEMA. n.d. “FEMA flood map service center.” Accessed May 1, 2022. https://msc.fema.gov/portal/advanceSearch.
Finch, C., C. T. Emrich, and S. L. Cutter. 2010. “Disaster disparities and differential recovery in New Orleans.” Popul. Environ. 31 (4): 179–202. https://doi.org/10.1007/s11111-009-0099-8.
Gao, J., B. Barzel, and A. L. Barabási. 2016. “Universal resilience patterns in complex networks.” Nature 530 (7590): 307–312. https://doi.org/10.1038/nature16948.
Gao, T. T., and G. Yan. 2022. “Autonomous inference of complex network dynamics from incomplete and noisy data.” Nat. Comput. Sci. 2 (3): 160–168. https://doi.org/10.1038/s43588-022-00217-0.
Gil, Y., D. Garijo, D. Khider, C. A. Knoblock, V. Ratnakar, M. Osorio, and L. Shu. 2021. “Artificial intelligence for modeling complex systems: Taming the complexity of expert models to improve decision making.” ACM Trans. Interact. Intell. Syst. 11 (2): 1–49. https://doi.org/10.1145/3453172.
Gunning, D., M. Stefik, J. Choi, T. Miller, S. Stumpf, and G. Z. Yang. 2019. “XAI—Explainable artificial intelligence.” Sci. Rob. 4 (37): 7120. https://doi.org/10.1126/scirobotics.aay7120.
Ha, S., and H. Jeong. 2021. “Unraveling hidden interactions in complex systems with deep learning.” Sci. Rep. 11: 12804. https://doi.org/10.1038/s41598-021-91878-w.
Han, Y., L. Mao, X. Chen, W. Zhai, Z. R. Peng, and P. Mozumder. 2021. “Agent-based modeling to evaluate human–Environment interactions in community flood risk mitigation.” Risk Anal. 42 (9): 2041–2061.
Hao, H., and Y. Wang. 2022. “Disentangling relations between urban form and urban accessibility for resilience to extreme weather and climate events.” Landscape Urban Plann. 220 (5): 104352. https://doi.org/10.1016/j.landurbplan.2022.104352.
Hao, H., Y. Wang, and Q. Wang. 2022. “Simulating urban population activities under extreme events with data-driven agent-based modeling.” In Proc., Construction Research Congress 2022, 1125–1134. Reston, VA: ASCE. https://doi.org/10.1061/9780784483961.118.
Hemmati, M., H. N. Mahmoud, B. R. Ellingwood, and A. T. Crooks. 2021. “Unraveling the complexity of human behavior and urbanization on community vulnerability to floods.” Sci. Rep. 11: 20085. https://doi.org/10.1038/s41598-021-99587-0.
Hochrainer-Stigler, S., S. Velev, F. Laurien, K. Campbell, J. Czajkowski, A. Keating, and R. Mechler. 2021. “Differences in the dynamics of community disaster resilience across the globe.” Sci. Rep. 11: 17625. https://doi.org/10.1038/s41598-021-96763-0.
Holling, C. S. 2001. “Understanding the complexity of economic, ecological, and social systems.” Ecosystems 4 (5): 390–405. https://doi.org/10.1007/s10021-001-0101-5.
Hong, B., B. J. Bonczak, A. Gupta, and C. E. Kontokosta. 2021. “Measuring inequality in community resilience to natural disasters using large-scale mobility data.” Nat. Commun. 12: 1870. https://doi.org/10.1038/s41467-021-22160-w.
IPCC. 2021. “Weather and climate extreme events in a changing climate.” In Climate change 2021: The physical science basis. Cambridge, UK: Cambridge University Press.
Kang, Y., S. Gao, Y. Liang, M. Li, J. Rao, and J. Kruse. 2020. “Multiscale dynamic human mobility flow dataset in the US during the COVID-19 epidemic.” Sci. Data 7: 390. https://doi.org/10.1038/s41597-020-00734-5.
Kipf, T. N., and M. Welling. 2016. “Semi-supervised classification with graph convolutional networks.” Preprint, submitted September 9, 2016. https://arxiv.org/abs/1609.02907.
Kokhlikyan, N., V. Miglani, M. Martin, E. Wang, B. Alsallakh, J. Reynolds, and O. Reblitz-Richardson. 2020. “Captum: A unified and generic model interpretability library for Pytorch.” Preprint, submitted September 16, 2020. https://arxiv.org/abs/2009.07896.
Li, Y., M. Kappas, and Y. Li. 2018. “Exploring the coastal urban resilience and transformation of coupled human-environment systems.” J. Cleaner Prod. 195 (2): 1505–1511. https://doi.org/10.1016/j.jclepro.2017.10.227.
Liao, K. H., T. A. Le, and K. Van Nguyen. 2016. “Urban design principles for flood resilience: Learning from the ecological wisdom of living with floods in the Vietnamese Mekong Delta.” Landscape Urban Plann. 155 (Jan): 69–78. https://doi.org/10.1016/j.landurbplan.2016.01.014.
Logan, T. M., and S. D. Guikema. 2020. “Reframing resilience: Equitable access to essential services.” Risk Anal. 40 (8): 1538–1553. https://doi.org/10.1111/risa.13492.
Lu, D., and S. Yang. 2022. “A survey of the analysis of complex systems based on complex network theory and deep learning.” Int. J. Perform. Eng. 18 (4): 241–250. https://doi.org/10.23940/ijpe.22.04.p2.241250.
Luo, Y., J. Peng, and J. Ma. 2020. “When causal inference meets deep learning.” Nat. Mach. Intell. 2 (8): 426–427. https://doi.org/10.1038/s42256-020-0218-x.
Mahmoud, H., and A. Chulahwat. 2018. “Spatial and temporal quantification of community resilience: Gotham City under attack.” Comput.-Aided Civ. Infrastruct. Eng. 33 (5): 353–372. https://doi.org/10.1111/mice.12318.
Marsooli, R., N. Lin, K. Emanuel, and K. Feng. 2019. “Climate change exacerbates hurricane flood hazards along US Atlantic and Gulf Coasts in spatially varying patterns.” Nat. Commun. 10: 3785. https://doi.org/10.1038/s41467-019-11755-z.
Martin, D. C., and B. J. Newman. 2015. “Measuring aggregate social capital using census response rates.” Am. Politics Res. 43 (4): 625–642. https://doi.org/10.1177/1532673X14536923.
McAllister, T., T. McAllister, C. Clavin, B. Ellingwood, J. van de Lindt, D. Mizzen, and F. Lavelle. 2019. Data, information, and tools needed for community resilience planning and decision-making. Washington, DC: US Department of Commerce.
Meerow, S., J. P. Newell, and M. Stults. 2016. “Defining urban resilience: A review.” Landscape Urban Plann. 147 (11): 38–49. https://doi.org/10.1016/j.landurbplan.2015.11.011.
Miles, S. B. 2015. “Foundations of community disaster resilience: Well-being, identity, services, and capitals.” Environ. Hazards 14 (2): 103–121. https://doi.org/10.1080/17477891.2014.999018.
NHC (National Hurricane Center). n.d. “Tropical cyclone reports.” Accessed May 1, 2022. https://www.nhc.noaa.gov/data/tcr.
NOAA (National Oceanic and Atmospheric Administration). n.d. “Climate data online: Web services documentation.” Accessed May 1, 2022. https://www.ncdc.noaa.gov/cdo-web/webservices/v2.
Norris, F. H., S. P. Stevens, B. Pfefferbaum, K. F. Wyche, and R. L. Pfefferbaum. 2008. “Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness.” Am. J. Commun. Psychol. 41 (1–2): 127–150. https://doi.org/10.1007/s10464-007-9156-6.
NWS (National Weather Service). n.d. “Advanced hydrologic prediction service.” Accessed May 1, 2022. https://water.weather.gov/precip/download.php.
Pilkington, S. F., and H. N. Mahmoud. 2020. “Interpreting the socio-technical interactions within a wind damage–artificial neural network model for community resilience.” R. Soc. Open Sci. 7 (11): 200922. https://doi.org/10.1098/rsos.200922.
Podesta, C., N. Coleman, A. Esmalian, F. Yuan, and A. Mostafavi. 2021. “Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data.” J. R. Soc. Interface 18 (177): 20210158. https://doi.org/10.1098/rsif.2021.0158.
Qiang, Y., Q. Huang, and J. Xu. 2020. “Observing community resilience from space: Using nighttime lights to model economic disturbance and recovery pattern in natural disaster.” Sustainable Cities Soc. 57 (2): 102115. https://doi.org/10.1016/j.scs.2020.102115.
Rachunok, B., and R. Nateghi. 2021. “Overemphasis on recovery inhibits community transformation and creates resilience traps.” Nat. Commun. 12: 7331. https://doi.org/10.1038/s41467-021-27359-5.
Roy, K. C., M. Cebrian, and S. Hasan. 2019. “Quantifying human mobility resilience to extreme events using geo-located social media data.” EPJ Data Sci. 8: 18. https://doi.org/10.1140/epjds/s13688-019-0196-6.
SafeGraph. 2020. “What about bias in the SafeGraph dataset?” Accessed May 1, 2022. https://www.safegraph.com/blog/what-about-bias-in-the-safegraph-dataset.
Scheffer, M., S. Carpenter, J. A. Foley, C. Folke, and B. Walker. 2001. “Catastrophic shifts in ecosystems.” Nature 413: 591–596. https://doi.org/10.1038/35098000.
Sharifi, A. 2016. “A critical review of selected tools for assessing community resilience.” Ecol. Indic. 69 (Jun): 629–647. https://doi.org/10.1016/j.ecolind.2016.05.023.
Sharifi, A. 2019a. “Resilient urban forms: A macro-scale analysis.” Cities 85 (5): 1–14. https://doi.org/10.1016/j.cities.2018.11.023.
Sharifi, A. 2019b. “Urban form resilience: A meso-scale analysis.” Cities 93 (3): 238–252. https://doi.org/10.1016/j.cities.2019.05.010.
Sharifi, A., and Y. Yamagata. 2018. “Resilience-oriented urban planning.” In Resilience-oriented urban planning, 3–27. Berlin: Springer.
Shrikumar, A., P. Greenside, and A. Kundaje. 2017. “Learning important features through propagating activation differences.” In Proc., Int. Conf. on Machine Learning, 3145–3153. New York: Association for Computing Machinery.
US Census Bureau. 2018. “60 million live in the path of hurricanes.” Accessed May 1, 2022. https://www.census.gov/library/stories/2018/08/coastal-county-population-rises.html.
USEPA. 2021. “Smart location database.” Accessed May 1, 2022. https://www.epa.gov/smartgrowth/smart-location-mapping#SLD.
Villagra, P., C. Rojas, R. Ohno, M. Xue, and K. Gómez. 2014. “A GIS-base exploration of the relationships between open space systems and urban form for the adaptive capacity of cities after an earthquake: The cases of two Chilean cities.” Appl. Geogr. 48 (5): 64–78. https://doi.org/10.1016/j.apgeog.2014.01.010.
Voskamp, I. M., and F. H. Van de Ven. 2015. “Planning support system for climate adaptation: Composing effective sets of blue-green measures to reduce urban vulnerability to extreme weather events.” Build. Environ. 83 (6): 159–167. https://doi.org/10.1016/j.buildenv.2014.07.018.
Walker, B. 2020. “Resilience: What it is and is not.” Ecol. Soc. 25 (2): 20. https://doi.org/10.5751/ES-11647-250211.
Wang, S., X. Gu, S. Luan, and M. Zhao. 2021a. “Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory.” Int. J. Crit. Infrastruct. Prot. 35: 100459. https://doi.org/10.1016/j.ijcip.2021.100459.
Wang, Y., D. Hulse, J. Von Meding, M. Brown, and L. Dedenbach. 2020a. “Conceiving resilience: Lexical shifts and proximal meanings in the human-centered natural and built environment literature from 1990 to 2018.” Dev. Built Environ. 1 (Jun): 100003. https://doi.org/10.1016/j.dibe.2019.100003.
Wang, Y., and J. E. Taylor. 2018. “Coupling sentiment and human mobility in natural disasters: A Twitter-based study of the 2014 South Napa earthquake.” Nat. Hazard. 92 (2): 907–925. https://doi.org/10.1007/s11069-018-3231-1.
Wang, Y., J. E. Taylor, and M. J. Garvin. 2020b. “Measuring resilience of human–spatial systems to disasters: Framework combining spatial-network analysis and fisher information.” J. Manage. Eng. 36 (4): 04020019. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000782.
Wang, Y., Q. Wang, and J. E. Taylor. 2021b. “Loss of resilience in human mobility across severe tropical cyclones of different magnitudes.” In Proc., 18th Int. Conf. on Information Systems for Crisis Response and Management (ISCRAM2021), 755–765. Blacksburg, VA: Virginia Tech.
Winderl, T. 2014. Disaster resilience measurements: Stocktaking of ongoing efforts in developing systems for measuring resilience. New York: United Nations Development Programme.
Yabe, T., P. S. C. Rao, S. V. Ukkusuri, and S. L. Cutter. 2022. “Toward data-driven, dynamical complex systems approaches to disaster resilience.” Proc. Natl. Acad. Sci. U.S.A. 119 (8): e2111997119. https://doi.org/10.1073/pnas.211199711.
Yadav, N., S. Chatterjee, and A. R. Ganguly. 2020. “Resilience of urban transport network-of-networks under intense flood hazards exacerbated by targeted attacks.” Sci. Rep. 10: 10350. https://doi.org/10.1038/s41598-020-66049-y.
Yang, Y., S. T. Ng, S. Zhou, F. J. Xu, and H. Li. 2019. “A physics-based framework for analyzing the resilience of interdependent civil infrastructure systems: A climatic extreme event case in Hong Kong.” Sustainable Cities Soc. 47 (6): 101485. https://doi.org/10.1016/j.scs.2019.101485.
Yu, S., and J. Ma. 2021. “Deep learning for geophysics: Current and future trends.” Rev. Geophys. 59 (3): e2021RG000742. https://doi.org/10.1029/2021RG000742.
Yuan, F., A. Esmalian, B. Oztekin, and A. Mostafavi. 2021. “Unveiling spatial patterns of disaster impacts and recovery using credit card transaction variances.” Preprint, submitted January 15, 2021. https://arxiv.org/abs/2101.10090.
Information & Authors
Information
Published In
Natural Hazards Review
Volume 24 • Issue 2 • May 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 7, 2022
Accepted: Dec 22, 2022
Published online: Feb 25, 2023
Published in print: May 1, 2023
Discussion open until: Jul 25, 2023
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Disaster risk management
- Disasters and hazards
- Dynamic models
- Dynamic properties
- Energy engineering
- Engineering fundamentals
- Mathematical models
- Models (by type)
- Neural networks
- Practice and Profession
- Social factors
- Structural behavior
- Structural engineering
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.