用机器学习动态预测德黑兰第6区洪水韧性，提升城市应对能力
Applying Machine Learning Tools for Urban Resilience Against Floods
- 基于CDRI框架融合2013-2022年数据，用机器学习建模预测韧性
- 首次实现对2025年五大韧性维度的动态预测，提升规划前瞻性
- 适合城市规划者与政策制定者参考，推动智能韧性城市建设
洪水是破坏性最强的自然灾害之一，尤其在资产密集、人口稠密的城市地区影响深远。伊朗德黑兰市频繁遭遇洪灾，凸显加强城市韧性的重要性。本文研究德黑兰第6区的洪水韧性模型，通过文献综述分析多种韧性框架，选定气候灾害韧性指数（CDRI）作为最适模型，其涵盖物理、社会、经济、组织及自然健康五大维度。尽管CDRI结构完整，但为静态模型，缺乏时间适应性。为此，本研究整合2013至2022年间每三年一次的数据，引入机器学习技术，预测2025年各维度韧性值。该动态模型可反映城市演化趋势，为德黑兰第6区提供数据驱动的洪水韧性规划基础，助力城市管理者科学决策。
原文摘要 · Abstract (English)
Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilience models to identify the most effective approach for District 6 in Tehran. Through an extensive literature review, various resilience models were analyzed, with the Climate Disaster Resilience Index (CDRI) emerging as the most suitable model for this district due to its comprehensive resilience dimensions: Physical, Social, Economic, Organizational, and Natural Health resilience. Although the CDRI model provides a structured approach to resilience measurement, it remains a static model focused on spatial characteristics and lacks temporal adaptability. An extensive literature review enhances the CDRI model by integrating data from 2013 to 2022 in three-year intervals and applying machine learning techniques to predict resilience dimensions for 2025. This integration enables a dynamic resilience model that can accommodate temporal changes, providing a more adaptable and data driven foundation for urban flood resilience planning. By employing artificial intelligence to reflect evolving urban conditions, this model offers valuable insights for policymakers and urban planners to enhance flood resilience in Tehrans critical District 6.
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