Machine learning insights into the evolution of flood Resilience: A synthesized framework study
文献类型：期刊论文
| 作者 | Wang, Yongyang1; Zhang, Pan1; Xie, Yulei1; Chen, Lei2; Cai, Yanpeng1 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 643页码:14 |
| 关键词 | Comprehensive framework Flood resilience Machine learning Convolutional Neural Networks The Pearl River Delta |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2024.131991 |
| 通讯作者 | Cai, Yanpeng(yanpeng.cai@gdut.edu.cn) |
| 英文摘要 | Enhancing urban resilience represented a viable strategy to mitigate flooding induced by intense human activities and climate change. However, existing studies often concentrated on system attributes or isolated resilience characteristics, failing to offer a holistic evaluation of urban flood resilience performance. Thus, it was imperative to develop a comprehensive flood resilience framework that incorporated the resilience evolution process including resistance, economic and function recovery. Consequently, this study endeavored to devise a synthesized framework for evaluating urban flood resilience, subsequently employing a Convolutional Neural Network (CNN) model for simulation. The findings indicated that: (1) Guangzhou's maximum resistance capacity diminished from 0.52 to 0.50 as rainfall return periods altered, while Dongguan exhibited the lowest resistance, decreasing from 0.42 to 0.40. Regarding functional recovery capacity, Guangzhou ranked highest (0.35) and Foshan lowest (0.19); (2) according to Triangular Fuzzy Number-based AHP (TFN-AHP) analysis, the area classified as highest in resilience decreased from 15.6% to 12.1% of the total, whereas the low resilience area increased from 7.6% to 8.7%; (3) Zhuhai and Zhaoqing were primarily clustered along the resistance axis, in contrast, Dongguan was distinguished by its advancement along the axis of functional recovery.(4) CNN simulations yielded precise outcomes, with the Area Under the Receiver Operating Characteristic Curve (AUC) and predictive accuracy (ACC) values exceeding 0.8,respectively. The insights provided by this research were crucial for entities tasked with flood risk management. |
| WOS关键词 | SYSTEM |
| 资助项目 | National Natural Science Foun-dation of China[52439005] ; Program for Guangdong Intro-ducing Innovative and Enterpreneurial Teams[2021ZT090543] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001319836700001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foun-dation of China ; Program for Guangdong Intro-ducing Innovative and Enterpreneurial Teams |
| 源URL | [http://ir.giec.ac.cn/handle/344007/43049]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Cai, Yanpeng |
| 作者单位 | 1.Guangdong Univ Technol, Guangdong Basic Res Ctr Excellence Ecol Secur & Gr, Sch Ecol Environm & Resources, Guangdong Prov Key Lab Water Qual Improvement & Ec, Guangzhou 510006, Peoples R China 2.Chinese Acad Sci, Guangzhou Inst Energy Convers, 2,Nengyuan Rd, Guangzhou 510640, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yongyang,Zhang, Pan,Xie, Yulei,et al. Machine learning insights into the evolution of flood Resilience: A synthesized framework study[J]. JOURNAL OF HYDROLOGY,2024,643:14. |
| APA | Wang, Yongyang,Zhang, Pan,Xie, Yulei,Chen, Lei,&Cai, Yanpeng.(2024).Machine learning insights into the evolution of flood Resilience: A synthesized framework study.JOURNAL OF HYDROLOGY,643,14. |
| MLA | Wang, Yongyang,et al."Machine learning insights into the evolution of flood Resilience: A synthesized framework study".JOURNAL OF HYDROLOGY 643(2024):14. |
入库方式： OAI收割
来源：广州能源研究所
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