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

Urban flood risk emerges from complex and nonlinear interactions amongmultiple features related to flood hazard, flood exposure, and social andphysical vulnerabilities, along with the complex spatial flood dependencerelationships. Existing approaches for characterizing urban flood risk,however, are primarily based on flood plain maps, focusing on a limited numberof features, primarily hazard and exposure features, without consideration offeature interactions or the dependence relationships among spatial areas. Toaddress this gap, this study presents an integrated urban flood-risk ratingmodel based on a novel unsupervised graph deep learning model (calledFloodRisk-Net). FloodRisk-Net is capable of capturing spatial dependence amongareas and complex and nonlinear interactions among flood hazards and urbanfeatures for specifying emergent flood risk. Using data from multiplemetropolitan statistical areas (MSAs) in the United States, the modelcharacterizes their flood risk into six distinct city-specific levels. Themodel is interpretable and enables feature analysis of areas within eachflood-risk level, allowing for the identification of the three archetypesshaping the highest flood risk within each MSA. Flood risk is found to bespatially distributed in a hierarchical structure within each MSA, where thecore city disproportionately bears the highest flood risk. Multiple cities arefound to have high overall flood-risk levels and low spatial inequality,indicating limited options for balancing urban development and flood-riskreduction. Relevant flood-risk reduction strategies are discussed consideringways that the highest flood risk and uneven spatial distribution of flood riskare formed.

Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas