一个跨城市可转移的卷积神经网络框架,用于评估城市网络中的街道规模洪水风险
Mo Wang1, Ji'an Zhuang2, Jiayu Zhao3
1College of Architecture and Urban Planning, Guangzhou University, Guangzhou, 510006, China; Architectural Design and Research Institute of Guangzhou University, Guangzhou, 510091, China; Department of Architecture, National University of Singapore, 4 Architecture Drive, Singapore, 117566, Singapore.
Journal of environmental management
|November 16, 2025
概括
这项研究开发了一种使用深度学习的AI框架,用于在街道层面绘制城市洪水风险图. 该模型准确地预测了洪水,确定了针对目标缓解策略的关键高风险区域.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 人工智能的人工智能
背景情况:
- 城市洪水对快速城市化地区的基础设施和公共安全构成重大风险.
- 气候变化加剧了洪水风险,需要先进的评估工具.
研究的目的:
- 开发和验证人工智能驱动的街道城市洪水风险评估框架.
- 整合水文气象学,地形学和城市形态学数据,以改善洪水预测.
- 评估AI模型在不同城市环境中的空间可转移性.
主要方法:
- 使用基于卷积神经网络 (CNN) 的深度学习框架.
- 该模型使用深的数据进行训练,并应用于香港.
- 在各种降雨情景下预测洪水淹没,包括100年复发间隔事件.
主要成果:
- 在极端降雨的情况下,预计香港64.1平方公里的城市地区容易被洪水淹没,平均水深为15.6厘米.
- 一项道路水平评估确定了501个高风险道路段 (18.9%的网络).
- 美国有线电视新闻网 (CNN) 模型显示出强大的空间可转移性,表明其具有更广泛应用的潜力.
结论:
- 人工智能框架为详细的城市洪水风险评估提供了一个强大的方法.
- 调查结果强调需要针对特定地区的洪水减缓战略.
- 该模型的跨区域可转移性为类似城市环境中的洪水风险管理提供了一种创新的方法.
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