Ensemble learning of daily river discharge modeling for two watersheds with different climates
文献类型：期刊论文
| 作者 | Xu Jingwen; Zhang Qun2; Liu Shuang3; Zhang Shaojie1 ; Jin Shengjie3; Li Dongyu2; Wu Xiaobo2; Liu Xiaojing2; Li Ting2; Li Hao2
|
| 刊名 | ATMOSPHERIC SCIENCE LETTERS
 |
| 出版日期 | 2020 |
| 页码 | e1000 |
| 关键词 | daily runoff ensemble learning model improvement TOPMODEL |
| ISSN号 | 1530-261X |
| DOI | 10.1002/asl.1000 |
| 产权排序 | 3 |
| 通讯作者 | Liu, Shuang(liushuang@imde.ac.cn) |
| 文献子类 | Article;Early Access |
| 英文摘要 | In order to reduce the uncertainties and improve the river discharge modeling accuracy, several topography-based hydrological models (TOPMODEL), generated by different combinations of parameters, were incorporated into an ensemble learning framework with the boosting method. Both the Baohe River Basin (BRB) with humid climate, and the Linyi River Basin (LRB) with semi-arid climate were chosen for model testing. Observed daily precipitation, pan evaporation and stream flow data were used for model development and testing. Different Nash-Sutcliffe efficiency coefficients, the coefficient of determination and the Root Mean Square Error were adopted to implement a comprehensive assessment on model performances. Testing results indicated that ensemble learning method could improve the modeling accuracy by comparing with the best single TOPMODEL. During the validation periods, the boosting method could increase the modeling accuracy by 9 and 16% for BRB and LRB, respectively. The ensemble method significantly narrowed the gap of model performances over watersheds with different climatic conditions. Hence, using the ensemble learning to enhance the feasibility of hydrological models for different climatic regions is promising. |
| WOS关键词 | RAINFALL-RUNOFF MODEL ; HYDROLOGICAL MODELS ; PREDICTION ; SIMULATION ; CLASSIFICATION ; TOPMODEL |
| 资助项目 | National Key R&D Program of China[2018YFC1505205] ; Research on Intelligent Monitoring and Early Warning Technology of Debris Flow on Sichuan-Tibet Railway[K2019G006] ; Research and Demonstration Program of Precise Warning and the Emergence Disposal Technologies against geo-hazards in the Jiuzhaigou scenic area[KJ-2018-23] ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences[SDS-QN-1907] ; CAS Light of WestChina Program |
| WOS研究方向 | Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000538628600001 |
| 出版者 | WILEY |
| 资助机构 | National Key R&D Program of China ; Research on Intelligent Monitoring and Early Warning Technology of Debris Flow on Sichuan-Tibet Railway ; Research and Demonstration Program of Precise Warning and the Emergence Disposal Technologies against geo-hazards in the Jiuzhaigou scenic area ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences ; CAS Light of WestChina Program |
| 源URL | [http://ir.imde.ac.cn/handle/131551/34906]  |
| 专题 | 成都山地灾害与环境研究所_山地灾害与地表过程重点实验室 |
| 通讯作者 | Liu Shuang |
| 作者单位 | 1.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Earth Surface Proc, Chengdu, Peoples R China 2.Sichuan Agr Univ, Coll Resources, Chengdu 611130, Peoples R China; 3.Sichuan Inst Land & Space Ecol Restorat & Geol Ha, Chengdu, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Xu Jingwen,Zhang Qun,Liu Shuang,et al. Ensemble learning of daily river discharge modeling for two watersheds with different climates[J]. ATMOSPHERIC SCIENCE LETTERS,2020:e1000. |
| APA | Xu Jingwen.,Zhang Qun.,Liu Shuang.,Zhang Shaojie.,Jin Shengjie.,...&Li Hao.(2020).Ensemble learning of daily river discharge modeling for two watersheds with different climates.ATMOSPHERIC SCIENCE LETTERS,e1000. |
| MLA | Xu Jingwen,et al."Ensemble learning of daily river discharge modeling for two watersheds with different climates".ATMOSPHERIC SCIENCE LETTERS (2020):e1000. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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