skip to main content

Sistem pengukuran ketinggian air sungai berbasis deteksi tepi Sobel

River water level measurement system using Sobel edge detection method

Department of Computer Science and Electronic, Universitas Gadjah Mada. Gedung C, Lantai 4, Sekip Utara, Bulaksumur, Yogyakarta 55281, Indonesia

Received: 24 Feb 2021; Revised: 11 Dec 2021; Accepted: 20 Jan 2022; Published: 31 Jan 2022.
Open Access Copyright (c) 2022 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Flood is a natural disaster that often occurs in Indonesia. Therefore, a flood warning system is required to reduce the number of losses due to flooding. In this study, a Sobel edge detection-based framework is proposed to measure the river water level, which is expected to be used as an early flood warning system. Sobel edge detection is used to determine the edge of the water surface, which is then taken by the position of the pixels, and the height is calculated by comparing the image with actual conditions. The test results of the system implemented on the prototype show that this system has an RMSE less than 0.6986 mm and can run at 12 fps which in the future can be implemented directly on rivers.
Keywords: flood; sobel edge detection; digital image processing; water level measurement
Funding: Universitas Gadjah Mada

Article Metrics:

  1. S. N. Jonkman and I. Kelman, “An analysis of the causes and circumstances of flood disaster deaths,” Disasters, vol. 29, no. 1, pp. 75–97, 2005. doi: 10.1111/j.0361-3666.2005.00275.x
  2. M. E. Sulaiman et al., “Analisis penyebab banjir di kota Samarinda,” Jurnal Geografi Gea, vol. 20, no. 1, pp. 39-43, 2020. doi: 10.17509/gea.v20i1.22021
  3. D. R. Prabawadhani, B. Harsoyo, T. H. Seto, and B. R. Prayoga, “Karakteristik temporal dan spasial curah hujan penyebab banjir di wilayah DKI Jakarta dan sekitarnya,” Jurnal Sains & Teknologi Modifikasi Cuaca, vol. 17, no. 1, pp. 21-25, 2016. doi: 10.29122/jstmc.v17i1.957
  4. D. Satria, S. Yana, R. Munadi, and S. Syahreza, “Sistem peringatan dini banjir secara real-time berbasis web menggunakan Arduino dan ethernet,” Jurnal Teknologi Informasi dan Komunikasi, vol. 1, no. 1, pp. 1-6, 2017
  5. Y. M. Akbar, A. Musafa, and I. Riyanto, “Image processing-based flood detection for online flood early warning system,” in the 6th Indonesia-Japan Joint Scientific Symposium, Yogyakarta, Indonesia, Oct. 2014, pp. 1-7.doi: 10.31227/osf.io/ayn2c
  6. J. Kim, Y. Han, and H. Hahn, “Embedded implementation of image-based water-level measurement system,” IET Computer Vision, vol. 5, no. 2, pp. 125-133, 2011. doi: 10.1049/iet-cvi.2009.0144
  7. T. E. Gilmore, F. Birgand, and K. W. Chapman, “Source and magnitude of error in an inexpensive image-based water level measurement system,” Journal Hydrolic, vol. 496, pp. 178–186, 2013. doi: 10.1016/j.jhydrol.2013.05.011
  8. A. Kurniawan, I. W. Mustika, and S. S. Kusumawardani, “Pengujian tracking color menggunakan ip webcam untuk deteksi ketinggian air,” in the Conference on Information Technology and Electrical Engineering, Yogyakarta, Indonesia, Oct. 2014, pp. 1–6. doi: 10.13140/2.1.2542.5603
  9. R. Priantama, “Implementasi algoritme background subtraction untuk deteksi tinggi muka air (tma) pada aplikasi peringatan dini banjir bandang berbasis pengolahan citra,” Buffer Information, vol. 5, no. 2, pp. 19–25, 2019. doi: 10.25134/buffer.v5i2.2184
  10. E. S. Ortigossa, F. Dias, J. Ueyama, and L. G. Nonato, “Using digital image processing to estimate the depth of urban streams,” in Conference on Graphics, Patterns and Images, Salvador, Brazil, Aug. 2015, pp. 1-6. doi: 10.13140/RG.2.1.4331.4408
  11. Y. U. Jaehyoung and H. Hernsoo, “Remote detection and monitoring of a water level using narrow band channel,” Journal of Information Science and Engineering, vol. 26, no. 1, pp. 71–82, 2010
  12. O. R. Vincent and O. Folorunso, “A descriptive algorithm for sobel image edge detection,” in the Informing Science & IT Education Conference, Macon, USA, Jun. 2009, pp. 97-107. doi: 10.28945/3351
  13. W. Piao, Y. Yuan, and H. Lin, “A digital image denoising algorithm based on gaussian filtering and bilateral filtering,” in International Conference on Wireless Communication and Sensor Network, Wuhan, China, Dec. 2017, pp. 1-8. doi: 10.1051/itmconf/20181701006
  14. G. N. Chaple, R. D. Daruwala and M. S. Gofane, "Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA," in the International Conference on Technologies for Sustainable Development, Mumbai, India, Feb. 2015, pp. 1-4, doi: 10.1109/ICTSD.2015.7095920
  15. H. Sakaino, "Camera-vision-based water level estimation," IEEE Sensors Journal, vol. 16, no. 21, pp. 7564-7565, 2016. doi: 10.1109/JSEN.2016.2603524
  16. S. Paris, P. Kornprobst, J. Tumblin, and F. Durand. “Bilateral filtering: theory and applications.” Foundations and Trends in Computer Graphics and Vision, vol. 4, no. 1, pp. 1–73, 2009. doi: 10.1561/0600000020
  17. M. Manashti, M. Javad, and M. A. Azimi, “Water level measurement using image processing,” The Second International Conference on Agriculture and Natural Resource, Kermanshah, Iran, Dec. 2013, pp. 393–95. doi: 10.13140/2.1.3089.2320
  18. T. Chai, and R. R. Draxler. “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature.” Geoscientific Model Development, vol. 7, no. 3, pp. 1247–50, 2014. doi: 10.5194/gmd-7-1247-2014
  19. O. Prabhune, P. Sabale, D. N. Sonawane, and C.L. Prabhune. “Image processing and matrices,” in the International Conference on Data Management, Analytics and Innovation, Pune, India, Feb. 2017, pp. 166–71

Last update:

No citation recorded.

Last update: 2024-10-04 09:20:37

No citation recorded.