A novel machine learning-based framework to extract the urban flood susceptible regions
文献类型：期刊论文
| 作者 | Tang, Xianzhe4,5; Tian, Juwei5; Huang, Xi5; Shu, Yuqin3; Liu, Zhenhua5; Long, Shaoqiu5; Xue, Weixing5; Liu, Luo5; Lin, Xueming4; Liu, Wei1,2 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2024-08-01 |
| 卷号 | 132页码:104050 |
| 关键词 | Urban flood Susceptibility Machine learning Spatiotemporal characteristics Shapley additive explanation |
| DOI | 10.1016/j.jag.2024.104050 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | The frequent occurrence of urban floods (UFs) poses significant threats to citizens' lives and the national economy. Utilizing machine learning to assess urban flood susceptibility (UFS) provides valuable decision support for UF management. However, the precision of current studies is usually influenced by the variability of temporal factors like extreme rainfall, which limits the accurate identification of urban flood-susceptible regions (UFSRs). To address this issue, we present a novel approach that leverages the spatiotemporal distribution and characteristics of UFS to accurately identify UFSRs. In our case study of the Greater Bay Area (GBA) in China, we employed the Random Forest to assess the spatiotemporal distribution of UFS. We then used the Savitzky-Golay filter to correct UFS data based on the UFS time series from 2011 to 2020. The Theil-Sen median slope, Mann- Kendall test, and Hurst analysis were used to explore the spatiotemporal patterns of UFS. Shapley additive explanation was applied to quantify the contribution of selected variables. Our findings include: (1) UFS in the GBA demonstrates a rising trend, with high susceptibility areas increasing from 6.3 % in 2011 to 7.4% in 2020; (2) UFSRs, covering approximately 11 % of the GBA, are primarily concentrated in the cities located around the central GBA; and (3) human behavior factors have a more significant influence on UF than natural ones. We believe the presented framework for the accurate extraction of UFSRs provides valuable decision support for sustainable city development. |
| WOS关键词 | ARTIFICIAL-INTELLIGENCE APPROACH ; DATA MINING TECHNIQUES ; FIRE SUSCEPTIBILITY ; SPATIAL PREDICTION ; SIMULATION ; WEIGHT ; MODELS ; FOREST ; RISK ; SWMM |
| WOS研究方向 | Remote Sensing |
| WOS记录号 | WOS:001281756900001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206922]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Liu, Wei |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.South China Normal Univ, Sch Geog, Guangzhou 510631, Peoples R China 4.South China Agr Univ, Joint Inst Environm & Educ, Coll Nat Resources & Environm, Guangzhou 510642, Peoples R China 5.South China Agr Univ, Guangdong Prov Key Lab Land Use & Consolidat, Guangzhou 510642, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tang, Xianzhe,Tian, Juwei,Huang, Xi,et al. A novel machine learning-based framework to extract the urban flood susceptible regions[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,132:104050. |
| APA | Tang, Xianzhe.,Tian, Juwei.,Huang, Xi.,Shu, Yuqin.,Liu, Zhenhua.,...&Liu, Wei.(2024).A novel machine learning-based framework to extract the urban flood susceptible regions.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,132,104050. |
| MLA | Tang, Xianzhe,et al."A novel machine learning-based framework to extract the urban flood susceptible regions".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 132(2024):104050. |
入库方式： OAI收割
来源：地理科学与资源研究所
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