Abstract

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.

Details

Title
Towards an AI-driven framework for multi-scale urban flood resilience planning and design
Author
Ye, Xinyue 1   VIAFID ORCID Logo  ; Wang, Shaohua 2 ; Lu, Zhipeng 3 ; Song, Yang 1 ; Yu, Siyu 1 

 Texas A&M University, Department of Landscape Architecture and Urban Planning, College Station, USA (GRID:grid.264756.4) (ISNI:0000 0004 4687 2082) 
 New Jersey Institute of Technology, Department of Informatics, Newark, USA (GRID:grid.260896.3) (ISNI:0000 0001 2166 4955) 
 Texas A&M University, Department of Architecture, College Station, USA (GRID:grid.264756.4) (ISNI:0000 0004 4687 2082) 
Publication year
2021
Publication date
Dec 2021
Publisher
Springer Nature B.V.
e-ISSN
27306852
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2730346932
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.