Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree
文献类型：期刊论文
| 作者 | Zhai, Xiaoyan3; Zhang, Yongyong4; Xia, Jun5; Zhang, Yongqiang4; Tang, Qiuhong4; Shao, Quanxi2; Chen, Junxu1; Zhang, Fan1 |
| 刊名 | JOURNAL OF GEOGRAPHICAL SCIENCES
 |
| 出版日期 | 2026 |
| 卷号 | 36期号:1页码:149-176 |
| 关键词 | flood regime metrics class prediction machine learning algorithms hydrological model |
| ISSN号 | 1009-637X |
| DOI | 10.1007/s11442-026-2442-8 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Accurate prediction of flood events is important for flood control and risk management. Machine learning techniques contributed greatly to advances in flood predictions, and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques. However, class-based flood predictions have rarely been investigated, which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies. This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees. Five algorithms were adopted for this exploration. Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%, compared with the four classes clustered from nine regime metrics. The nonlinear algorithms (Multiple Linear Regression, Random Forest, and least squares-Support Vector Machine) outperformed the linear techniques (Multiple Linear Regression and Stepwise Regression) in predicting flood regime metrics. The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4% and 47.2%-76.0% in calibration and validation periods, respectively, particularly for the slow and late flood events. The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. |
| URL标识 | 查看原文 |
| WOS关键词 | NEURAL-NETWORKS ; MODEL ; SIMULATION ; REGIME ; CLASSIFICATION ; RISK ; PRECIPITATION ; NONLINEARITY ; VARIABILITY ; SENSITIVITY |
| WOS研究方向 | Physical Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:001673292900011 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221057]  |
| 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 | Zhang, Yongyong |
| 作者单位 | 1.Yunnan Univ, Sch Earth Sci, Kunming 650091, Peoples R China 2.CSIRO, Data 61,Private Bag 5, Wembley, WA 6913, Australia; 3.China Inst Water Resources & Hydropower Res, Res Ctr Flood & Drought Disaster Reduct, State Key Lab Water Cycle & Water Secur, Beijing 100038, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; 5.Wuhan Univ, State Key Lab Water Resources Engn & Management, Wuhan 430072, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhai, Xiaoyan,Zhang, Yongyong,Xia, Jun,et al. Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree[J]. JOURNAL OF GEOGRAPHICAL SCIENCES,2026,36(1):149-176. |
| APA | Zhai, Xiaoyan.,Zhang, Yongyong.,Xia, Jun.,Zhang, Yongqiang.,Tang, Qiuhong.,...&Zhang, Fan.(2026).Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree.JOURNAL OF GEOGRAPHICAL SCIENCES,36(1),149-176. |
| MLA | Zhai, Xiaoyan,et al."Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree".JOURNAL OF GEOGRAPHICAL SCIENCES 36.1(2026):149-176. |
入库方式： OAI收割
来源：地理科学与资源研究所
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