Towards an AI-driven framework for multi-scale urban flood resilience planning and design
Towards an AI-driven framework for multi-scale urban flood resilience planning and design
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DOI：
10.1007/s43762-021-00011-0
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Xinyue Ye;Shaohua Wang;Zhipeng Lu;Yang Song;Siyu Yu
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文献类型：
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作者：
Xinyue Ye;Shaohua Wang;Zhipeng Lu;Yang Song;Siyu Yu
沿海地区的气候脆弱性更高。社区可以通过设计和规划在很大程度上减少其灾害脆弱性，提高其社会复原力，这可以使城市走上长期稳定的轨道。然而，设计和规划界内部的孤岛以及研究与实践之间的差距使得难以实现洪水抵御环境的目标。因此，本文提出了一个AI（人工智能）驱动的平台，以促进防洪设计和规划。在当地居民、专家、政策制定者和从业人员的积极参与下，这个平台将打破上述孤岛，缩小知识差距，最终提高公众意识，提高协作效率，实现最佳设计和规划成果。我们建议采取整体和综合的方法，将多个学科（建筑设计，景观设计，城市规划，地理和计算机科学），并在宏观，中观和微观尺度上研究紧迫的弹性问题。
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.