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Accurate water level predictions in a tidal reach: Integration of Physics-based and Machine learning approaches
Fei, Kai; Du, Haoxuan; Gao, Liang
2023-05-23
Source PublicationJournal of Hydrology
ISSN0022-1694
Volume622Pages:129705
Abstract

Accurate water level prediction is very important for coastal construction and flood prevention in an estuarine area. However, it is challenging to represent the water level processes accurately in a spatial domain due to the dual influences of river discharge from the upstream tidal reach (upstream discharge) and astronomical tides. In this study, a new method is proposed to predict water levels in tidal reaches by developing a riverine-estuary Hydrologic-Hydrodynamic Coupling model (H2C) and integrating the outputs (Upstream discharge and water levels, tidal levels) with long-short term memory. Then, the relative contribution of upstream discharge to water level predictions is assessed by extreme gradient boosting. The coupling model (H2C-XL) is driven by basin-scale Satellite-derived Meteorology Estimate datasets and TPXO9 tidal data. H2C-XL is tested in a tidal reach (Tianhe-Zhuyin reach) and found to be effective in enhancing the accuracy of water level predictions dramatically. The Nash coefficient and Kling-Gupta Efficiency values of predicted upstream discharge are 0.866 and 0.922, respectively. Accurate upstream discharge prediction is crucial for water level predictions in tidal reaches. This is reflected in that the Nash coefficient and Kling-Gupta Efficiency values are up to 0.781 and 0.806 for predicting water levels at upper hydrologic stations (Jiangmen and Daao stations), which are improved by 12.34% − 40.46% and 16.98% − 32.34% than those of above models. The relative contribution of upstream discharge to water level predictions in Tianhe-Zhuyin reach ranges from 0.37 to 0.55. The values vary at the same site depending on the hydrologic conditions each year and are relatively low near the coastline, but nonlinearly increase with the distance from the coastline.

KeywordHydrologic-hydrodynamic Coupling Model Machine Learning Relative Contribution Tidal Reach Upstream Discharge Water Level Prediction
DOI10.1016/j.jhydrol.2023.129705
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Geology ; Water Resources
WOS SubjectEngineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS IDWOS:001012684200001
PublisherELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
Scopus ID2-s2.0-85160527233
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorGao, Liang
AffiliationState Key Laboratory of Internet of Things for Smart City and Department of Civil Engineering and Environment,University of Macau,Macao,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Fei, Kai,Du, Haoxuan,Gao, Liang. Accurate water level predictions in a tidal reach: Integration of Physics-based and Machine learning approaches[J]. Journal of Hydrology, 2023, 622, 129705.
APA Fei, Kai., Du, Haoxuan., & Gao, Liang (2023). Accurate water level predictions in a tidal reach: Integration of Physics-based and Machine learning approaches. Journal of Hydrology, 622, 129705.
MLA Fei, Kai,et al."Accurate water level predictions in a tidal reach: Integration of Physics-based and Machine learning approaches".Journal of Hydrology 622(2023):129705.
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