干旱区生态环境知识资源中心(Arid): Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau

Arid

DOI	10.1016/j.jhydrol.2023.129115
	Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau
	Yu, Qiang; Jiang, Liguang; Wang, Yanjun; Liu, Junguo
通讯作者	Jiang, LG
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2023
卷号	617
英文摘要	Accurate and efficient streamflow simulations are crucial in arid and semi-arid regions for water resources management. Process-based hydrological models generally perform inferior in arid and semi-arid catchments. Data-driven machine learning methods show very promising results in terms of prediction accuracy. In this study, we explore the synergies between process-based hydrological model and machine learning model to improve the predictive capability for semi-arid basins. We developed three hybridization approaches that combine the simulations of the Hydrologiska Byrans Vattenbalansavdelning (HBV) model with Long Short-Term Memory (LSTM) neural networks. In particular, one tight hybridization model is developed to consider the feedback between the LSTM model and the HBV model. Further, we investigated the predictive capability of both standalone HBV and LSTM models with short-length data for training, i.e., one-year data in the context of poorly gauged basins. The results show distinct improvements in the three types of hybrid models when compared with the HBV model and standalone LSTM model in terms of both NSE (12.3 % - 25.6 %) and KGE (6 % - 67.9 %). The model performance of the tight hybridization is the best among all the hybrid models, not only in terms of metrics but also hydrological signatures and the simulation of extreme flows. When calibrated with short-length data records, the LSTM was more robust than HBV, producing acceptable NSE and KGE values. Moreover, there is a strong correlation (0.92) between LSTM model performance and the similarity of flow duration curves (FDCs) between streamflow series in the calibration and validation periods. The results suggest that the hybridization of LSTM and HBV may provide an enhanced simulation capacity for semi-arid regions. Besides, the LSTM model can be successfully calibrated with representative short-length data and the characteristics of the representative short-length data are found. This study provides new insights into the potential use of hybridized machine learning in hydrological simulations.
英文关键词	Hybridization Machine learning LSTM HBV Streamflow simulation Calibration
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000922039800001
WOS关键词	RAINFALL-RUNOFF MODELS ; PARAMETER-ESTIMATION ; WATER-BALANCE ; CALIBRATION ; INTELLIGENCE ; HYDROLOGY ; PERFORMANCE ; INDEXES ; PERIOD
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397379
推荐引用方式 GB/T 7714	Yu, Qiang,Jiang, Liguang,Wang, Yanjun,et al. Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau[J],2023,617.
APA	Yu, Qiang,Jiang, Liguang,Wang, Yanjun,&Liu, Junguo.(2023).Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau.JOURNAL OF HYDROLOGY,617.
MLA	Yu, Qiang,et al."Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau".JOURNAL OF HYDROLOGY 617(2023).