Article
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Preserved in Portico This version is not peer-reviewed
Optimal Rescue Ship Locations Using Image Processing and Clustering
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Version 1
: Received: 15 October 2018 / Approved: 15 October 2018 / Online: 15 October 2018 (16:53:32 CEST)
A peer-reviewed article of this Preprint also exists.
Jung, C.-Y.; Yoo, S.-L. Optimal Rescue Ship Locations Using Image Processing and Clustering. Symmetry 2019, 11, 32. Jung, C.-Y.; Yoo, S.-L. Optimal Rescue Ship Locations Using Image Processing and Clustering. Symmetry 2019, 11, 32.
Abstract
Currently, maritime traffic is increasing with economic growth in several regions worldwide. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features, such as islands, are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest distance between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance from rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.
Keywords
clustering-based optimization; location optimization; flood-filling algorithm; marine accident; rescue ship; shortest distance
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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