Deep characteristic learning model for real-time flow monitoring based on H-ADCP
Study region: The Luohu hydrological station, located in southeastern China, which has unstable water level- discharge relationship caused by tides. Study focus: Real-time flow monitoring based on horizontal-acoustic Doppler current profiler (H-ADCP), which remains insufficient to deal with low moni...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824004646 |
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| author | Yu Li Xin Zhao Yibo Wang Ling Zeng |
| author_facet | Yu Li Xin Zhao Yibo Wang Ling Zeng |
| author_sort | Yu Li |
| collection | DOAJ |
| description | Study region: The Luohu hydrological station, located in southeastern China, which has unstable water level- discharge relationship caused by tides. Study focus: Real-time flow monitoring based on horizontal-acoustic Doppler current profiler (H-ADCP), which remains insufficient to deal with low monitoring accuracy, complex flow characteristics, and large data volumes caused by the construction and operation of hydraulic engineering, backwater, tides, siltation changes, and high-frequency monitoring. This study proposed a deep characteristic learning (DCL) model to identify and extract the nonlinear characteristics between flow velocity of H-ADCP cell and river cross section by incorporating multiple intelligent algorithms. New hydrological insights for the region: The DCL model performs efficiently with a determination coefficient (R2) of 0.93 between the simulated and observed discharge, which is obviously better than the single intelligent algorithm-based models. The DCL model allows for adaptive algorithm selection and parameter adjustment according to the characteristics of river cross section and H-ADCP data. It shows strong self-learning capability and good simulation accuracy even with few training samples. Additionally, the DCL model is demonstrated to be stable and applicable in terms of model structure and practical performance. This study can serve as a reference for real-time flow monitoring under complex hydrological conditions. |
| format | Article |
| id | doaj-art-722ce6138f6542028224c28393ef4433 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-722ce6138f6542028224c28393ef44332025-01-22T05:42:05ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102115Deep characteristic learning model for real-time flow monitoring based on H-ADCPYu Li0Xin Zhao1Yibo Wang2Ling Zeng3Bureau of Hydrology, Changjiang Water Resources Commission, Wuhan 430010, ChinaBureau of Hydrology, Changjiang Water Resources Commission, Wuhan 430010, ChinaCorresponding author.; Bureau of Hydrology, Changjiang Water Resources Commission, Wuhan 430010, ChinaBureau of Hydrology, Changjiang Water Resources Commission, Wuhan 430010, ChinaStudy region: The Luohu hydrological station, located in southeastern China, which has unstable water level- discharge relationship caused by tides. Study focus: Real-time flow monitoring based on horizontal-acoustic Doppler current profiler (H-ADCP), which remains insufficient to deal with low monitoring accuracy, complex flow characteristics, and large data volumes caused by the construction and operation of hydraulic engineering, backwater, tides, siltation changes, and high-frequency monitoring. This study proposed a deep characteristic learning (DCL) model to identify and extract the nonlinear characteristics between flow velocity of H-ADCP cell and river cross section by incorporating multiple intelligent algorithms. New hydrological insights for the region: The DCL model performs efficiently with a determination coefficient (R2) of 0.93 between the simulated and observed discharge, which is obviously better than the single intelligent algorithm-based models. The DCL model allows for adaptive algorithm selection and parameter adjustment according to the characteristics of river cross section and H-ADCP data. It shows strong self-learning capability and good simulation accuracy even with few training samples. Additionally, the DCL model is demonstrated to be stable and applicable in terms of model structure and practical performance. This study can serve as a reference for real-time flow monitoring under complex hydrological conditions.http://www.sciencedirect.com/science/article/pii/S2214581824004646Deep characteristic learningH-ADCPReal-time flow monitoringIntelligent algorithms |
| spellingShingle | Yu Li Xin Zhao Yibo Wang Ling Zeng Deep characteristic learning model for real-time flow monitoring based on H-ADCP Journal of Hydrology: Regional Studies Deep characteristic learning H-ADCP Real-time flow monitoring Intelligent algorithms |
| title | Deep characteristic learning model for real-time flow monitoring based on H-ADCP |
| title_full | Deep characteristic learning model for real-time flow monitoring based on H-ADCP |
| title_fullStr | Deep characteristic learning model for real-time flow monitoring based on H-ADCP |
| title_full_unstemmed | Deep characteristic learning model for real-time flow monitoring based on H-ADCP |
| title_short | Deep characteristic learning model for real-time flow monitoring based on H-ADCP |
| title_sort | deep characteristic learning model for real time flow monitoring based on h adcp |
| topic | Deep characteristic learning H-ADCP Real-time flow monitoring Intelligent algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824004646 |
| work_keys_str_mv | AT yuli deepcharacteristiclearningmodelforrealtimeflowmonitoringbasedonhadcp AT xinzhao deepcharacteristiclearningmodelforrealtimeflowmonitoringbasedonhadcp AT yibowang deepcharacteristiclearningmodelforrealtimeflowmonitoringbasedonhadcp AT lingzeng deepcharacteristiclearningmodelforrealtimeflowmonitoringbasedonhadcp |