A review of dynamic monitoring methods for intermittent rivers: Integrating remote sensing and machine learning
文献类型：期刊论文
| 作者 | Xie, Chaoshuai1,2; Lv, Aifeng1,2 |
| 刊名 | JOURNAL OF GEOGRAPHICAL SCIENCES
 |
| 出版日期 | 2026-03-01 |
| 卷号 | 36期号:3页码:763-796 |
| 关键词 | machine learning intermittent rivers and ephemeral streams remote sensing framework algorithm selection |
| ISSN号 | 1009-637X |
| DOI | 10.1007/s11442-026-2469-x |
| 产权排序 | 1 |
| 文献子类 | Review |
| 英文摘要 | Intermittent rivers and ephemeral streams (IRES), also known as non-perennial river segments (NPRs), have garnered attention due to their significant roles in watershed hydrology and ecosystem services, especially in the context of climate change and escalating human activities. Recent advances in machine learning (ML) techniques have significantly improved the analysis of dynamic changes in IRES. Various ML models, including random forest (RF), long short-term memory (LSTM), and U-Net, demonstrate clear advantages in processing complex hydrological data, enhancing the efficiency and accuracy of IRES extraction from remote sensing data. Furthermore, hybrid ML approaches enhance predictive performance in complex hydrological scenarios by integrating multiple algorithms. However, ML methods still face challenges, including high data dependence, computational complexity, and scalability issues with models. This review proposes an IRES monitoring framework that combines satellite data with ML algorithms, integrating remote sensing technologies such as optical imaging and synthetic aperture radar, and evaluates the advantages and limitations of different ML methods. It further highlights the potential of integrating multiple ML techniques and high-resolution remote sensing data to monitor IRES dynamics, conduct ecological assessments, and support sustainable water management, offering a scientific foundation for addressing environmental and anthropogenic pressures. |
| URL标识 | 查看原文 |
| WOS关键词 | NATURAL FLOW REGIMES ; WATER INDEX NDWI ; CLIMATE-CHANGE ; CLASSIFICATION ; CHALLENGES ; PREDICTION ; STREAMFLOW ; FRAMEWORK ; SELECTION ; QUANTIFY |
| WOS研究方向 | Physical Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:001717879700010 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221322]  |
| 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 | Lv, Aifeng |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Water Cycle & Surface Proc, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Xie, Chaoshuai,Lv, Aifeng. A review of dynamic monitoring methods for intermittent rivers: Integrating remote sensing and machine learning[J]. JOURNAL OF GEOGRAPHICAL SCIENCES,2026,36(3):763-796. |
| APA | Xie, Chaoshuai,&Lv, Aifeng.(2026).A review of dynamic monitoring methods for intermittent rivers: Integrating remote sensing and machine learning.JOURNAL OF GEOGRAPHICAL SCIENCES,36(3),763-796. |
| MLA | Xie, Chaoshuai,et al."A review of dynamic monitoring methods for intermittent rivers: Integrating remote sensing and machine learning".JOURNAL OF GEOGRAPHICAL SCIENCES 36.3(2026):763-796. |
入库方式： OAI收割
来源：地理科学与资源研究所
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