干旱区生态环境知识资源中心(Arid): Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology

Arid

DOI	10.1016/j.eng.2021.12.014
	Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology
	Wu, Houfa; Zhang, Jianyun; Bao, Zhenxin; Wang, Guoqing; Wang, Wensheng; Yang, Yanqing; Wang, Jie
通讯作者	Bao, ZX
来源期刊	ENGINEERING
ISSN	2095-8099
EISSN	2096-0026
出版年	2023
卷号	28 页码:93-104
英文摘要	Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments. The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization, which is the most widely used approach. Runoff modeling was studied in 38 catchments located in the Yellow-Huai-Hai River Basin (YHHRB). The values of the Nash-Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and percent bias (PBIAS) indicated the acceptable performance of the soil and water assessment tool (SWAT) model in the YHHRB. Nine descriptors belonging to the categories of climate, soil, vegetation, and topography were used to express the catchment characteristics related to the hydrological processes. The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models, including linear regression (LR) equations, support vector regression (SVR), random forest (RF), k-nearest neighbor (kNN), decision tree (DT), and radial basis function (RBF). Each of the 38 catchments was assumed to be an ungauged catchment in turn. Then, the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments. Furthermore, the similaritybased regionalization scheme was used for comparison with the regression-based approach. The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments. Compared with the traditional LR-based approach, the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships. The performances of different approaches were similar in humid regions, while the advantages of the machine learning techniques were more evident in arid regions. When the study area contained nested catchments, the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance. The new findings could improve flood forecasting and water resources planning in regions that lack observed data. (c) 2022 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
英文关键词	Parameters estimation Ungauged catchments Regionalization scheme Machine learning algorithms Soil and water assessment tool model
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:001138790500001
WOS关键词	SWAT MODEL ; RIVER-BASIN ; FLOW ; PREDICTIONS ; CALIBRATION ; SIMULATION ; STREAMFLOW ; CHINA
WOS类目	Engineering, Multidisciplinary
WOS研究方向	Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396078
推荐引用方式 GB/T 7714	Wu, Houfa,Zhang, Jianyun,Bao, Zhenxin,et al. Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology[J],2023,28:93-104.
APA	Wu, Houfa.,Zhang, Jianyun.,Bao, Zhenxin.,Wang, Guoqing.,Wang, Wensheng.,...&Wang, Jie.(2023).Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology.ENGINEERING,28,93-104.
MLA	Wu, Houfa,et al."Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology".ENGINEERING 28(2023):93-104.