Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery

Soon Ho Kwon () and Seungyub Lee ()
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Soon Ho Kwon: Hannam University
Seungyub Lee: Hannam University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 3, No 19, 1165-1180

Abstract: 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.

Keywords: Deep learning; Water level recognition; Reservoir; CCTV; Image processing (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-023-03714-7

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