Abstract
Agricultural reservoirs are major suppliers of water for farming, meeting approximately 61.3% of the agricultural water demand in South Korea. However, several challenges jeopardize the efficient supply of water demand and management of reservoirs. To address them, this study proposes a novel deep learning-based model for water level estimation in agricultural reservoirs using closed-circuit television (CCTV) image data. The model comprises three key components, namely (1) dataset construction, (2) image segmentation using U-Net, and (3) CCTV-based water level recognition employing deep learning architectures, and its performance is assessed on G-reservoir and M-reservoir datasets, which revealed excellent image segmentation results. However, the effectiveness of the water level recognition model depends on classification criteria (i.e., the number of classification classes) and complexity. The performance of the model can be improved once more data are collected.
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Data Availability
All data, models, and codes supporting the findings of this study are available from the corresponding author upon reasonable request.
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Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1C1C2004896).
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Data and code generation and their analysis were performed and the first draft of the manuscript was written by S.H.K.; S.L. guided the study and reviewed, edited, and approved the manuscript. Both authors edited and approved the final version of the manuscript.
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Kwon, S.H., Lee, S. Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery. Water Resour Manage 38, 1165–1180 (2024). https://doi.org/10.1007/s11269-023-03714-7
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DOI: https://doi.org/10.1007/s11269-023-03714-7