RivQNet: Deep Learning Based River Discharge Estimation Using Close-Range Water Surface Imagery

Ansari,S., Rennie, C. D., Jamieson, E. C., Seidou, O. and Clark, S. P., (2023). RivQNet: Deep Learning Based River Discharge Estimation Using Close-Range Water Surface Imagery. AGU, 59(2), e2021WR031841-n/a

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  • Sub-type Journal article
    Author Ansari,S.
    Rennie, C. D.
    Jamieson, E. C.
    Seidou, O.
    Clark, S. P.
    Title RivQNet: Deep Learning Based River Discharge Estimation Using Close-Range Water Surface Imagery
    Appearing in AGU
    Volume 59
    Issue No. 2
    Publication Date 2023-01-21
    Place of Publication Florida
    Publisher AGU
    Start page e2021WR031841
    End page n/a
    Language eng
    Abstract Streamflow data is often the most critical input for hydrologic and hydraulic research, modeling, and design studies. Streamflow measurement using close range non-contact sensing such as image velocimetry is a new technique that is yet far from maturity. Most current image-based surface velocimetry techniques use correlation approaches that require user input to run the algorithms. This input can bias results if the operator is inexperienced. The main goal of this study is to develop a novel, accurate and fast river velocimetry scheme called RivQNet that does not require subjective user input. RivQNet processes close-range non-contact water surface images using artificial intelligence techniques. The algorithm is a deep-learning optical flow estimation using a preferred available convolutional neural network architecture (i.e., FlowNet architecture). In this study the presented method is validated with common standard measurement methods and compared with conventional optical flow methodologies. The results indicate that the presented method yields accurate and dense spatial distributions of surface velocities.
    Keyword surface velocimetry
    discharge
    deep learning
    artificial intelligence
    rivers
    Copyright Holder author(s)
    Copyright Year 2023
    Copyright type Creative commons
    ISSN 0043-1397
    DOI 10.1029/2021WR031841
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    Created: Wed, 18 Sep 2024, 03:23:27 JST by Haideh Beigi on behalf of UNU INWEH