TY - JOUR
T1 - Towards an AI-driven framework for multi-scale urban flood resilience planning and design
AU - Ye, Xinyue
AU - Wang, Shaohua
AU - Lu, Zhipeng
AU - Song, Yang
AU - Yu, Siyu
N1 - Funding Information:
This material is partially based upon work supported by the National Science Foundation under Grant Nos. 1739491 and 1937908 as well as the start-up grant 241117–40000 from Texas A&M University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation and Texas A&M University.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Geodesign
KW - Resilience
KW - Urban flood
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U2 - 10.1007/s43762-021-00011-0
DO - 10.1007/s43762-021-00011-0
M3 - Review article
AN - SCOPUS:85137621842
SN - 2730-6852
VL - 1
JO - Computational Urban Science
JF - Computational Urban Science
IS - 1
M1 - 11
ER -