中文版 | English
Title

Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau

Author
Corresponding AuthorJiang, Liguang
Publication Years
2023-02-01
DOI
Source Title
ISSN
0022-1694
EISSN
1879-2707
Volume617
Abstract
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.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
Nature Science Foundation of Hubei Province["Y01296129","Y01296229"] ; null[2021CFB146]
WOS Research Area
Engineering ; Geology ; Water Resources
WOS Subject
Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS Accession No
WOS:000922039800001
Publisher
ESI Research Field
ENGINEERING
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:3
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/475086
DepartmentSchool of Environmental Science and Engineering
Affiliation
1.Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen 518055, Peoples R China
2.Lower Reaches Yangtze River Minist Water Resources, Yangtze River Sci Res Inst, Key Lab River & Lake Regulat & Flood Control Middl, Wuhan 430010, Peoples R China
3.North China Univ Water Resources & Elect Power, Henan Prov Key Lab Hydrosphere & Watershed Water S, Zhangzhou 450046, Peoples R China
First Author AffilicationSchool of Environmental Science and Engineering
Corresponding Author AffilicationSchool of Environmental Science and Engineering
First Author's First AffilicationSchool of Environmental Science and Engineering
Recommended Citation
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]. JOURNAL OF HYDROLOGY,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).
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