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Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data

Author

Listed:
  • Ahmadhon Akbarkhonovich Kamolov

    (Department of Computer Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Korea)

  • Suhyun Park

    (Department of Computer Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Korea)

Abstract

Implementing AI in all fields is a solution to the complications that can be troublesome to solve for human beings and will be the key point of the advancement of those spheres. In the marine world, specialists also encounter some problems that can be revealed through addressing AI and machine learning algorithms. One of these challenges is determining the depth of the seabed with high precision. The depth of the seabed is utterly significant in the procedure of ships at sea occupying a safe route. Thus, it is considerably crucial that the ships do not sit in shallow water. In this article, we have addressed the fuzzy c-means (FCM) clustering algorithm, which is one of the vigorous unsupervised learning methods under machine learning to solve the mentioned problems. In the case study, crowdsourced data have been trained, which are gathered from vessels that have installed sound navigation and ranging (SONAR) sensors. The data for the training were collected from ships sailing in the south part of South Korea. In the training section, we segregated the training zone into the diminutive size areas (blocks). The data assembled in blocks had been trained in FCM. As a result, we have received data separated into clusters that can be supportive to differentiate data. The results of the effort show that FCM can be implemented and obtain accurate results on crowdsourced bathymetry.

Suggested Citation

  • Ahmadhon Akbarkhonovich Kamolov & Suhyun Park, 2021. "Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5823-:d:559847
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    References listed on IDEAS

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    1. Dong Yang & Lingxiao Wu & Shuaian Wang & Haiying Jia & Kevin X. Li, 2019. "How big data enriches maritime research – a critical review of Automatic Identification System (AIS) data applications," Transport Reviews, Taylor & Francis Journals, vol. 39(6), pages 755-773, November.
    2. Geertsma, R.D. & Negenborn, R.R. & Visser, K. & Hopman, J.J., 2017. "Design and control of hybrid power and propulsion systems for smart ships: A review of developments," Applied Energy, Elsevier, vol. 194(C), pages 30-54.
    3. Jiyang Tian & Chuanzhe Li & Jia Liu & Fuliang Yu & Shuanghu Cheng & Nana Zhao & Wan Zurina Wan Jaafar, 2016. "Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test," Sustainability, MDPI, vol. 8(11), pages 1-17, October.
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    Cited by:

    1. Mohammed Abdulhakim Al-Absi & Ahmed Abdulhakim Al-Absi & Mangal Sain & Hoonjae Lee, 2021. "Moving Ad Hoc Networks—A Comparative Study," Sustainability, MDPI, vol. 13(11), pages 1-31, May.
    2. Isa Ebtehaj & Keyvan Soltani & Afshin Amiri & Marzban Faramarzi & Chandra A. Madramootoo & Hossein Bonakdari, 2021. "Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning," Sustainability, MDPI, vol. 13(14), pages 1-23, July.

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