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Article

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

Department of Computer Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Korea
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Author to whom correspondence should be addressed.
Academic Editors: Baihua Li and Fei Chao
Sustainability 2021, 13(11), 5823; https://doi.org/10.3390/su13115823
Received: 14 April 2021 / Revised: 14 May 2021 / Accepted: 16 May 2021 / Published: 21 May 2021
(This article belongs to the Special Issue Machine Learning and AI Technology for Sustainability)
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. View Full-Text
Keywords: marine navigation safety; smart navigation; deep learning; fuzzy c-means clustering; seawater depth prediction marine navigation safety; smart navigation; deep learning; fuzzy c-means clustering; seawater depth prediction
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MDPI and ACS Style

Kamolov, A.A.; Park, S. Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data. Sustainability 2021, 13, 5823. https://doi.org/10.3390/su13115823

AMA Style

Kamolov AA, Park S. Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data. Sustainability. 2021; 13(11):5823. https://doi.org/10.3390/su13115823

Chicago/Turabian Style

Kamolov, Ahmadhon A., and Suhyun Park. 2021. "Prediction of Depth of Seawater Using Fuzzy C-Means Clustering Algorithm of Crowdsourced SONAR Data" Sustainability 13, no. 11: 5823. https://doi.org/10.3390/su13115823

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