Towards an AI-driven framework for multi-scale urban flood resilience planning and design Academic Article uri icon


  • AbstractClimate 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.

published proceedings

  • Computational Urban Science

author list (cited authors)

  • Ye, X., Wang, S., Lu, Z., Song, Y., & Yu, S.

citation count

  • 8

complete list of authors

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

publication date

  • July 2021