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Image-based recognition and processing system for monitoring water levels in an irrigation and drainage channel

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Abstract

The water level provides critically important information for disaster mitigation and water resource management. Image-based recognition and processing systems employed to monitor flow in channels/rivers have become a progressive technique. However, whether the observational goal can be achieved by utilizing a low image resolution is questionable. In this study, a closed-circuit television surveillance camera with a low image resolution, which was installed by the local government in an irrigation and drainage channel, is analyzed. To demonstrate the availability and capability of a low imaging resolution, the statistical errors between image recognition/manual identification and measured water levels in six field experiments were compared. The root-mean-square error, mean absolute error , and mean absolute percentage error values ranged from 0.05 to 0.18 m, from 0.04 to 0.20 m, and from 4.23% to 18.29%, respectively, for image recognition. The results indicate that the technique of image recognition and processing achieves automatic monitoring goals and acceptable error. Furthermore, there are some on-site environmental factors and image processing techniques to be explored. The results indicated that the shadows of a large range of buildings, uneven brightness, watermarks formed on the riverbank, glared images, and water level measurements by image recognition and processing caused inaccuracies. In this study, the double identification region used to detect the water level yielded better results than the single identification region. The Gaussian filter best preserves the edge information and eliminates noise. Increasing the resolution of the original image fivefold achieves a better balance between preserving edge information and eliminating noise, which was the most suitable for water level measurement.

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Acknowledgements

This study was partially supported by the National Science and Technology Council (NSC), Taiwan, under grant no. 109-2625-M-239-002. The financial support is greatly appreciated.

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Authors and Affiliations

  1. Department of Civil and Disaster Prevention Engineering, National United University, Miaoli, 360302, Taiwan

    Wen-Cheng Liu, Chun-Kai Chung & Wei-Che Huang

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  1. Wen-Cheng Liu
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  2. Chun-Kai Chung
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  3. Wei-Che Huang
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Correspondence to Wen-Cheng Liu.

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Liu, WC., Chung, CK. & Huang, WC. Image-based recognition and processing system for monitoring water levels in an irrigation and drainage channel. Paddy Water Environ 21, 417–431 (2023). https://doi.org/10.1007/s10333-023-00935-9

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  • DOI: https://doi.org/10.1007/s10333-023-00935-9

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