干旱区生态环境知识资源中心(Arid): Ensemble learning of daily river discharge modeling for two watersheds with different climates

Arid

DOI	10.1002/asl.1000
	Ensemble learning of daily river discharge modeling for two watersheds with different climates
	Xu, Jingwen; Zhang, Qun; Liu, Shuang; Zhang, Shaojie; Jin, Shengjie; Li, Dongyu; Wu, Xiaobo; Liu, Xiaojing; Li, Ting; Li, Hao
通讯作者	Liu, S
来源期刊	ATMOSPHERIC SCIENCE LETTERS
ISSN	1530-261X
出版年	2020
卷号	21 期号:11
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.
英文关键词	daily runoff ensemble learning model improvement TOPMODEL
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000538628600001
WOS关键词	RAINFALL-RUNOFF MODEL ; HYDROLOGICAL MODELS ; PREDICTION ; SIMULATION ; CLASSIFICATION ; TOPMODEL
WOS类目	Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences
WOS研究方向	Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324663
作者单位	[Xu, Jingwen; Li, Dongyu; Wu, Xiaobo; Liu, Xiaojing; Li, Ting; Li, Hao] Sichuan Agr Univ, Coll Resources, Chengdu 611130, Peoples R China; [Zhang, Qun; Jin, Shengjie] Sichuan Inst Land & Space Ecol Restorat & Geol Ha, Chengdu, Peoples R China; [Liu, Shuang; Zhang, Shaojie] Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Hazards & Earth Surface Proc, 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],2020,21(11).
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,21(11).
MLA	Xu, Jingwen,et al."Ensemble learning of daily river discharge modeling for two watersheds with different climates".ATMOSPHERIC SCIENCE LETTERS 21.11(2020).