Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML
文献类型：期刊论文
| 作者 | He, Fei1,2; Liu, Suxia1,2; Mo, Xingguo1,2; Wang, Zhonggen2,3 |
| 刊名 | SCIENTIFIC REPORTS
 |
| 出版日期 | 2025-01-11 |
| 卷号 | 15期号:1页码:1702 |
| 关键词 | Flash flood susceptibility Yarlung Tsangpo River Basin Machine learning Model interpretability |
| ISSN号 | 2045-2322 |
| DOI | 10.1038/s41598-024-84655-y |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims to evaluate the flash flood susceptibility in the Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by the H2O automated ML platform. The best-performing model was used to generate a flash flood susceptibility map, and its interpretability was analyzed using the Shapley Additive Explanations (SHAP) tree interpretation method. The results revealed that the top four models, including both single and ensemble models, demonstrated high accuracy in the tests. The flash flood susceptibility map generated by the best-performing eXtreme Randomized Trees (XRT) model showed that 8.92%, 12.95%, 15.42%, 31.34%, and 31.37% of the study area exhibited very high, high, moderate, low, and very low flash flood susceptibility, respectively, with approximately 74.9% of the historical flash floods occurring in areas classified as moderate to very high susceptibility. The SHAP plot identified topographic factors as the primary drivers of flash floods, with the importance analysis ranking the most influential factors in such descending order as DEM, topographic wetness index, topographic position index, normalized difference vegetation index, and average multi-year precipitation. This study demonstrates the benefits of interpretable machine learning, which can provide guidance for flash flood mitigation. |
| URL标识 | 查看原文 |
| WOS关键词 | EVENTS ; MODEL |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001396241000018 |
| 出版者 | NATURE PORTFOLIO |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211334]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Liu, Suxia |
| 作者单位 | 1.Univ Chinese Acad Sci UCAS, Coll Resources & Environm, Sino Danish Coll, Beijing 100190, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res IGSNRR, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; 3.Minist Emergency Management Peoples Republ China, Natl Inst Nat Hazards, Beijing 100085, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Fei,Liu, Suxia,Mo, Xingguo,et al. Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML[J]. SCIENTIFIC REPORTS,2025,15(1):1702. |
| APA | He, Fei,Liu, Suxia,Mo, Xingguo,&Wang, Zhonggen.(2025).Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML.SCIENTIFIC REPORTS,15(1),1702. |
| MLA | He, Fei,et al."Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML".SCIENTIFIC REPORTS 15.1(2025):1702. |
入库方式： OAI收割
来源：地理科学与资源研究所
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