English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Community resilience is a complex and muti-faceted phenomenon that emergesfrom complex and nonlinear interactions among different socio-technical systemsand their resilience properties. However, present studies on communityresilience focus primarily on vulnerability assessment and utilize index-basedapproaches, with limited ability to capture heterogeneous features withincommunity socio-technical systems and their nonlinear interactions in shapingrobustness, redundancy, and resourcefulness components of resilience. Toaddress this gap, this paper presents an integrated three-layer deep learningmodel for community resilience rating (called Resili-Net). Twelve measurableresilience features are specified and computed within community socio-technicalsystems (i.e., facilities, infrastructures, and society) related to threeresilience components of robustness, redundancy, and resourcefulness. Usingpublicly accessible data from multiple metropolitan statistical areas in theUnited States, Resili-Net characterizes the resilience levels of spatial areasinto five distinct levels. The interpretability of the model outcomes enablesfeature analysis for specifying the determinants of resilience in areas withineach resilience level, allowing for the identification of specific resilienceenhancement strategies. Changes in community resilience profiles under urbandevelopment patterns are further examined by changing the value of relatedsocio-technical systems features. Accordingly, the outcomes provide novelperspectives for community resilience assessment by harnessing machineintelligence and heterogeneous urban big data.

Deep Learning-driven Community Resilience Rating based on Intertwined Socio-Technical Systems Features