Preprints
https://doi.org/10.5194/egusphere-2023-659
https://doi.org/10.5194/egusphere-2023-659
05 May 2023
 | 05 May 2023

Technical Note: Monitoring discharge of mountain streams by retrieving image features with deep learning

Chenqi Fang, Genyu Yuan, Ziying Zheng, Qirui Zhong, and Kai Duan

Abstract. Traditional discharge monitoring usually relies on measuring flow velocity and cross-section area with various velocimeters or remote-sensing approaches. However, the topography of mountain streams in remote sites largely hinders the applicability of velocity-area methods. We here present a method to continuously monitor mountain stream discharge using a low-cost commercial camera and deep learning algorithm. A procedure of automated image categorization and discharge classification was developed to extract information on flow patterns and volumes from high-frequency red–green–blue (RGB) images with deep convolutional neural networks (CNNs). The method was tested at a small, steep, natural stream reach in southern China. Reference discharge data was acquired from a V-shaped weir and ultrasonic flowmeter installed a few meters downstream of the camera system. Results show that the discharge-relevant stream features implicitly embedded in RGB information can be effectively recognized and retrieved by CNN to achieve satisfactory accuracy in discharge measurement. Coupling CNN and traditional machine learning models (e.g., support vector machine and random forest) can potentially synthesize individual models’ diverse merits and improve generalization performance. Besides, proper image pre-processing and categorization are critical for enhancing the robustness and applicability of the method under environmental disturbances (e.g., weather and vegetation on river banks). Our study highlights the usefulness of deep learning in analyzing complex flow images and tracking flow changes over time, which provides a reliable and flexible alternative apparatus for continuous discharge monitoring of rocky mountain streams.

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Journal article(s) based on this preprint

10 Sep 2024
Technical note: Monitoring discharge of mountain streams by retrieving image features with deep learning
Chenqi Fang, Genyu Yuan, Ziying Zheng, Qirui Zhong, and Kai Duan
Hydrol. Earth Syst. Sci., 28, 4085–4098, https://doi.org/10.5194/hess-28-4085-2024,https://doi.org/10.5194/hess-28-4085-2024, 2024
Short summary
Chenqi Fang, Genyu Yuan, Ziying Zheng, Qirui Zhong, and Kai Duan

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-659', Anonymous Referee #1, 26 May 2023
    • AC1: 'Reply on RC1', Chenqi Fang, 14 Oct 2023
  • RC2: 'Comment on egusphere-2023-659', Anonymous Referee #2, 07 Sep 2023
    • AC2: 'Reply on RC2', Chenqi Fang, 14 Oct 2023
    • AC1: 'Reply on RC1', Chenqi Fang, 14 Oct 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-659', Anonymous Referee #1, 26 May 2023
    • AC1: 'Reply on RC1', Chenqi Fang, 14 Oct 2023
  • RC2: 'Comment on egusphere-2023-659', Anonymous Referee #2, 07 Sep 2023
    • AC2: 'Reply on RC2', Chenqi Fang, 14 Oct 2023
    • AC1: 'Reply on RC1', Chenqi Fang, 14 Oct 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (04 Nov 2023) by Yue-Ping Xu
AR by Chenqi Fang on behalf of the Authors (04 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (30 Dec 2023) by Yue-Ping Xu
RR by Anonymous Referee #1 (07 Feb 2024)
RR by Anonymous Referee #2 (12 Feb 2024)
RR by Anonymous Referee #3 (12 Jun 2024)
RR by Anonymous Referee #4 (28 Jun 2024)
ED: Publish subject to minor revisions (review by editor) (05 Jul 2024) by Yue-Ping Xu
AR by Chenqi Fang on behalf of the Authors (16 Jul 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (30 Jul 2024) by Yue-Ping Xu
AR by Chenqi Fang on behalf of the Authors (31 Jul 2024)

Journal article(s) based on this preprint

10 Sep 2024
Technical note: Monitoring discharge of mountain streams by retrieving image features with deep learning
Chenqi Fang, Genyu Yuan, Ziying Zheng, Qirui Zhong, and Kai Duan
Hydrol. Earth Syst. Sci., 28, 4085–4098, https://doi.org/10.5194/hess-28-4085-2024,https://doi.org/10.5194/hess-28-4085-2024, 2024
Short summary
Chenqi Fang, Genyu Yuan, Ziying Zheng, Qirui Zhong, and Kai Duan
Chenqi Fang, Genyu Yuan, Ziying Zheng, Qirui Zhong, and Kai Duan

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Short summary
Measuring discharge at steep, rocky mountain streams is challenging due to the difficulties in identifying cross-section characteristics and establishing stable stage-discharge relationships. We present a novel method using only a low-cost commercial camera and deep learning algorithms. Our study shows that deep convolutional neural networks can automatically recognize and retrieve complex stream features embedded in RGB images to achieve continuous discharge monitoring.