Towards an AI-driven framework for multi-scale urban flood resilience planning and design

Xinyue Ye, Shaohua Wang, Zhipeng Lu, Yang Song, Siyu Yu

Research output: Contribution to journalReview articlepeer-review

13 Scopus citations


Climate vulnerability is higher in coastal regions. Communities can largely reduce their hazard vulnerabilities and increase their social resilience through design and planning, which could put cities on a trajectory for long-term stability. However, the silos within the design and planning communities and the gap between research and practice have made it difficult to achieve the goal for a flood resilient environment. Therefore, this paper suggests an AI (Artificial Intelligence)-driven platform to facilitate the flood resilience design and planning. This platform, with the active engagement of local residents, experts, policy makers, and practitioners, will break the aforementioned silos and close the knowledge gaps, which ultimately increases public awareness, improves collaboration effectiveness, and achieves the best design and planning outcomes. We suggest a holistic and integrated approach, bringing multiple disciplines (architectural design, landscape architecture, urban planning, geography, and computer science), and examining the pressing resilient issues at the macro, meso, and micro scales.

Original languageEnglish (US)
Article number11
JournalComputational Urban Science
Issue number1
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Urban Studies
  • Artificial Intelligence
  • Computer Science Applications
  • Environmental Science (miscellaneous)


  • Artificial intelligence
  • Geodesign
  • Resilience
  • Urban flood


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