Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review
The impacts of natural hazards on urban areas are becoming more severe and frequent. As a leading theory of resilient cities on an international level, ecosystem-based disaster risk reduction (Eco-DRR) is a crucial approach for disaster risk reduction (DRR) and urban resilience. The reasonable use o...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24000220 |
| _version_ | 1797356853206712320 |
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| author | Daixin Dai Mingyang Bo Xiaosong Ren Kaiyu Dai |
| author_facet | Daixin Dai Mingyang Bo Xiaosong Ren Kaiyu Dai |
| author_sort | Daixin Dai |
| collection | DOAJ |
| description | The impacts of natural hazards on urban areas are becoming more severe and frequent. As a leading theory of resilient cities on an international level, ecosystem-based disaster risk reduction (Eco-DRR) is a crucial approach for disaster risk reduction (DRR) and urban resilience. The reasonable use of artificial intelligence (AI) technology can effectively address the uncertainty problem faced in urban ecosystems and natural hazard impacts. However, current research applying AI in urban Eco-DRR is still limited, and the evidence and concepts of the interplay between Eco-DRR and AI are still not clear. We utilized the PRISMA-ScR framework to survey and analyze studies that apply AI technology in urban Eco-DRR, ultimately selecting 76 studies for a scoping review. We qualitatively analyzed the case studies from 3 perspectives: natural disaster risk, Eco-DRR, and AI technology, and identified the spatiotemporal characteristics, objectives, AI algorithms, and data source of selected Eco-DRR cases. Based on the findings, we conducted a theoretical framework of AI applied in urban Eco-DRR by organizing out the logical relationship among the 3 perspectives. We proposed and discussed the key points of AI application in Eco-DRR practice: (1) The scales and types of disaster point out the aims of Eco-DRR. (2) The selection of AI algorithms should align with the objectives of AI to achieve the aims of Eco-DRR. (3) Data sources of disaster risk elements can effectively support AI applied in Eco-DRR. Finally, we summarized 4 approaches to integrating ecosystems with traditional Disaster Risk Reduction (DRR) using AI technology: ecosystem service, ecosystem indicators, dynamic change prediction, and green infrastructure (GI) construction. The logical framework, progress, and trend of this field summarized in this study provide a theoretical basis and reference for the application of AI technology in urban Eco-DRR, which is beneficial for urban security and resilience. |
| first_indexed | 2024-03-08T14:35:51Z |
| format | Article |
| id | doaj.art-0a439a7fcfbd4a47870ae7ee43a870d9 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-03-08T14:35:51Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-0a439a7fcfbd4a47870ae7ee43a870d92024-01-12T04:55:17ZengElsevierEcological Indicators1470-160X2024-01-01158111565Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping reviewDaixin Dai0Mingyang Bo1Xiaosong Ren2Kaiyu Dai3College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China; State Key Laboratory of Civil Engineering Disaster Prevention, Tongji University, Shanghai 200092, ChinaCollege of Architecture and Urban Planning, Tongji University, Shanghai 200092, China; State Key Laboratory of Civil Engineering Disaster Prevention, Tongji University, Shanghai 200092, ChinaState Key Laboratory of Civil Engineering Disaster Prevention, Tongji University, Shanghai 200092, ChinaSchool of Computer Science, Fudan University, Shanghai 200433, China; Corresponding author.The impacts of natural hazards on urban areas are becoming more severe and frequent. As a leading theory of resilient cities on an international level, ecosystem-based disaster risk reduction (Eco-DRR) is a crucial approach for disaster risk reduction (DRR) and urban resilience. The reasonable use of artificial intelligence (AI) technology can effectively address the uncertainty problem faced in urban ecosystems and natural hazard impacts. However, current research applying AI in urban Eco-DRR is still limited, and the evidence and concepts of the interplay between Eco-DRR and AI are still not clear. We utilized the PRISMA-ScR framework to survey and analyze studies that apply AI technology in urban Eco-DRR, ultimately selecting 76 studies for a scoping review. We qualitatively analyzed the case studies from 3 perspectives: natural disaster risk, Eco-DRR, and AI technology, and identified the spatiotemporal characteristics, objectives, AI algorithms, and data source of selected Eco-DRR cases. Based on the findings, we conducted a theoretical framework of AI applied in urban Eco-DRR by organizing out the logical relationship among the 3 perspectives. We proposed and discussed the key points of AI application in Eco-DRR practice: (1) The scales and types of disaster point out the aims of Eco-DRR. (2) The selection of AI algorithms should align with the objectives of AI to achieve the aims of Eco-DRR. (3) Data sources of disaster risk elements can effectively support AI applied in Eco-DRR. Finally, we summarized 4 approaches to integrating ecosystems with traditional Disaster Risk Reduction (DRR) using AI technology: ecosystem service, ecosystem indicators, dynamic change prediction, and green infrastructure (GI) construction. The logical framework, progress, and trend of this field summarized in this study provide a theoretical basis and reference for the application of AI technology in urban Eco-DRR, which is beneficial for urban security and resilience.http://www.sciencedirect.com/science/article/pii/S1470160X24000220Eco-DRRArtificial intelligenceDisaster riskResilient cities |
| spellingShingle | Daixin Dai Mingyang Bo Xiaosong Ren Kaiyu Dai Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review Ecological Indicators Eco-DRR Artificial intelligence Disaster risk Resilient cities |
| title | Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review |
| title_full | Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review |
| title_fullStr | Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review |
| title_full_unstemmed | Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review |
| title_short | Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review |
| title_sort | application and exploration of artificial intelligence technology in urban ecosystem based disaster risk reduction a scoping review |
| topic | Eco-DRR Artificial intelligence Disaster risk Resilient cities |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24000220 |
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