A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilience
Urban resilience evaluates systems’ capacities to prepare for, adapt to, absorb, and recover from disruptions. Evaluation frameworks incorporate metrics like recovery speed, adaptive ability, and absorptive capacity. Assessing critical infrastructure interdependencies is challenging yet vital to lim...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941925000870 |
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| author | Yu Chen Wenxing You Lu Ou Hui Tang |
| author_facet | Yu Chen Wenxing You Lu Ou Hui Tang |
| author_sort | Yu Chen |
| collection | DOAJ |
| description | Urban resilience evaluates systems’ capacities to prepare for, adapt to, absorb, and recover from disruptions. Evaluation frameworks incorporate metrics like recovery speed, adaptive ability, and absorptive capacity. Assessing critical infrastructure interdependencies is challenging yet vital to limit failure propagation. While static assessments, multi-layer frameworks, and software like Hazus are used, limitations persist. Machine learning often focuses on infrastructure data for recovery monitoring. A common workflow entails acquiring and organizing data, then applying supervised, unsupervised, or reinforcement learning models. Supervised learning uses labeled data while unsupervised learning detects patterns in unlabeled data. Reinforcement learning optimizes rewards through trial-and-error interactions. Machine learning assists in meeting intensifying urbanization and climate change challenges. Leveraging advances in sensors, IoT, and computing enables tasks like image labeling and semantic segmentation. The techniques facilitate resilience through real-time data analytics for informed decision-making and responsive disaster management. |
| format | Article |
| id | doaj-art-60a765c857484541bd4102192dd9199e |
| institution | OA Journals |
| issn | 2772-9419 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-60a765c857484541bd4102192dd9199e2025-08-20T01:55:37ZengElsevierSystems and Soft Computing2772-94192025-12-01720026910.1016/j.sasc.2025.200269A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilienceYu Chen0Wenxing You1Lu Ou2Hui Tang3College of Architecture and Urban Planning, Hunan City University, Yiyang, 413000, China; Hunan Provincial Key Laboratory of Urban Planning Information Technology, Yiyang, 413000, China; School of Architecture and Planning, Hunan University, Changsha, 410000, ChinaBeijing Century Chief International Architecture Design Co., Ltd, Beijing, 110000, ChinaSchool of Architecture, Changsha University of Science & Technology, Changsha, Hunan 410000, ChinaCollege of Architecture and Urban Planning, Hunan City University, Yiyang, 413000, China; Hunan Provincial Key Laboratory of Urban Planning Information Technology, Yiyang, 413000, China; Corresponding author.Urban resilience evaluates systems’ capacities to prepare for, adapt to, absorb, and recover from disruptions. Evaluation frameworks incorporate metrics like recovery speed, adaptive ability, and absorptive capacity. Assessing critical infrastructure interdependencies is challenging yet vital to limit failure propagation. While static assessments, multi-layer frameworks, and software like Hazus are used, limitations persist. Machine learning often focuses on infrastructure data for recovery monitoring. A common workflow entails acquiring and organizing data, then applying supervised, unsupervised, or reinforcement learning models. Supervised learning uses labeled data while unsupervised learning detects patterns in unlabeled data. Reinforcement learning optimizes rewards through trial-and-error interactions. Machine learning assists in meeting intensifying urbanization and climate change challenges. Leveraging advances in sensors, IoT, and computing enables tasks like image labeling and semantic segmentation. The techniques facilitate resilience through real-time data analytics for informed decision-making and responsive disaster management.http://www.sciencedirect.com/science/article/pii/S2772941925000870Machine learningUrban resilienceDisaster managementData analyticsArtificial intelligence |
| spellingShingle | Yu Chen Wenxing You Lu Ou Hui Tang A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilience Systems and Soft Computing Machine learning Urban resilience Disaster management Data analytics Artificial intelligence |
| title | A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilience |
| title_full | A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilience |
| title_fullStr | A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilience |
| title_full_unstemmed | A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilience |
| title_short | A review of machine learning techniques for urban resilience research: The application and progress of different machine learning techniques in assessing and enhancing urban resilience |
| title_sort | review of machine learning techniques for urban resilience research the application and progress of different machine learning techniques in assessing and enhancing urban resilience |
| topic | Machine learning Urban resilience Disaster management Data analytics Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2772941925000870 |
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