Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability
Abstract Urban flooding threatens urban resilience and challenges SDGs 11 and 13. This study assesses urban building flood risk in Guangzhou by integrating flood susceptibility with building function vulnerability. Using a Random Forest (RF) model, it predicts flood susceptibility based on flood rec...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Urban Sustainability |
| Online Access: | https://doi.org/10.1038/s42949-025-00208-w |
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| author | Xiaoling Qin Shifu Wang Meng Meng Haiyan Long Huilan Zhang Haochen Shi |
| author_facet | Xiaoling Qin Shifu Wang Meng Meng Haiyan Long Huilan Zhang Haochen Shi |
| author_sort | Xiaoling Qin |
| collection | DOAJ |
| description | Abstract Urban flooding threatens urban resilience and challenges SDGs 11 and 13. This study assesses urban building flood risk in Guangzhou by integrating flood susceptibility with building function vulnerability. Using a Random Forest (RF) model, it predicts flood susceptibility based on flood records, hydrological, topographical, and anthropogenic features. The Categorical Boosting (CatBoost) model identifies building functions using POI and AOI data. Results reveal significant spatial variations: central districts exhibit higher flood susceptibility, while peripheral areas remain less affected. Over half of the buildings are moderately vulnerable, with only a small fraction highly vulnerable. Based on flood susceptibility and functional vulnerability, Guangzhou is classified into three district types: central urban (Type I), intermediate urban (Type II), and suburban/rural (Type III). The study underscores the need for tailored flood risk management strategies to address these disparities and mitigate climate change-induced water hazards. |
| format | Article |
| id | doaj-art-19cfc5936e084b15a4fa80cf023592c8 |
| institution | OA Journals |
| issn | 2661-8001 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Urban Sustainability |
| spelling | doaj-art-19cfc5936e084b15a4fa80cf023592c82025-08-20T01:47:29ZengNature Portfolionpj Urban Sustainability2661-80012025-05-015111910.1038/s42949-025-00208-wEnhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerabilityXiaoling Qin0Shifu Wang1Meng Meng2Haiyan Long3Huilan Zhang4Haochen Shi5School of Architecture, South China University of TechnologySchool of Architecture, South China University of TechnologySchool of Architecture, South China University of TechnologySchool of Architecture, South China University of TechnologyGuangzhou Urban Planning And Consulting CO.,LTDCollege of Architecture and Urban Planning, Guangzhou UniversityAbstract Urban flooding threatens urban resilience and challenges SDGs 11 and 13. This study assesses urban building flood risk in Guangzhou by integrating flood susceptibility with building function vulnerability. Using a Random Forest (RF) model, it predicts flood susceptibility based on flood records, hydrological, topographical, and anthropogenic features. The Categorical Boosting (CatBoost) model identifies building functions using POI and AOI data. Results reveal significant spatial variations: central districts exhibit higher flood susceptibility, while peripheral areas remain less affected. Over half of the buildings are moderately vulnerable, with only a small fraction highly vulnerable. Based on flood susceptibility and functional vulnerability, Guangzhou is classified into three district types: central urban (Type I), intermediate urban (Type II), and suburban/rural (Type III). The study underscores the need for tailored flood risk management strategies to address these disparities and mitigate climate change-induced water hazards.https://doi.org/10.1038/s42949-025-00208-w |
| spellingShingle | Xiaoling Qin Shifu Wang Meng Meng Haiyan Long Huilan Zhang Haochen Shi Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability npj Urban Sustainability |
| title | Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability |
| title_full | Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability |
| title_fullStr | Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability |
| title_full_unstemmed | Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability |
| title_short | Enhancing urban resilience through machine learning-supported flood risk assessment: integrating flood susceptibility with building function vulnerability |
| title_sort | enhancing urban resilience through machine learning supported flood risk assessment integrating flood susceptibility with building function vulnerability |
| url | https://doi.org/10.1038/s42949-025-00208-w |
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