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Application of particle swarm optimization in optimal placement of tsunami sensors
Rapid detection and early warning systems demonstrate crucial significance in tsunami risk reduction measures. So far, several tsunami observation networks have been deployed in tsunamigenic regions to issue effective local response. However, guidance on where to station these sensors are limited. I...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924491/ https://www.ncbi.nlm.nih.gov/pubmed/33816981 http://dx.doi.org/10.7717/peerj-cs.333 |
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author | Ferrolino, Angelie Mendoza, Renier Magdalena, Ikha Lope, Jose Ernie |
author_facet | Ferrolino, Angelie Mendoza, Renier Magdalena, Ikha Lope, Jose Ernie |
author_sort | Ferrolino, Angelie |
collection | PubMed |
description | Rapid detection and early warning systems demonstrate crucial significance in tsunami risk reduction measures. So far, several tsunami observation networks have been deployed in tsunamigenic regions to issue effective local response. However, guidance on where to station these sensors are limited. In this article, we address the problem of determining the placement of tsunami sensors with the least possible tsunami detection time. We use the solutions of the 2D nonlinear shallow water equations to compute the wave travel time. The optimization problem is solved by implementing the particle swarm optimization algorithm. We apply our model to a simple test problem with varying depths. We also use our proposed method to determine the placement of sensors for early tsunami detection in Cotabato Trench, Philippines. |
format | Online Article Text |
id | pubmed-7924491 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79244912021-04-02 Application of particle swarm optimization in optimal placement of tsunami sensors Ferrolino, Angelie Mendoza, Renier Magdalena, Ikha Lope, Jose Ernie PeerJ Comput Sci Optimization Theory and Computation Rapid detection and early warning systems demonstrate crucial significance in tsunami risk reduction measures. So far, several tsunami observation networks have been deployed in tsunamigenic regions to issue effective local response. However, guidance on where to station these sensors are limited. In this article, we address the problem of determining the placement of tsunami sensors with the least possible tsunami detection time. We use the solutions of the 2D nonlinear shallow water equations to compute the wave travel time. The optimization problem is solved by implementing the particle swarm optimization algorithm. We apply our model to a simple test problem with varying depths. We also use our proposed method to determine the placement of sensors for early tsunami detection in Cotabato Trench, Philippines. PeerJ Inc. 2020-12-18 /pmc/articles/PMC7924491/ /pubmed/33816981 http://dx.doi.org/10.7717/peerj-cs.333 Text en © 2020 Ferrolino et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Optimization Theory and Computation Ferrolino, Angelie Mendoza, Renier Magdalena, Ikha Lope, Jose Ernie Application of particle swarm optimization in optimal placement of tsunami sensors |
title | Application of particle swarm optimization in optimal placement of tsunami sensors |
title_full | Application of particle swarm optimization in optimal placement of tsunami sensors |
title_fullStr | Application of particle swarm optimization in optimal placement of tsunami sensors |
title_full_unstemmed | Application of particle swarm optimization in optimal placement of tsunami sensors |
title_short | Application of particle swarm optimization in optimal placement of tsunami sensors |
title_sort | application of particle swarm optimization in optimal placement of tsunami sensors |
topic | Optimization Theory and Computation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924491/ https://www.ncbi.nlm.nih.gov/pubmed/33816981 http://dx.doi.org/10.7717/peerj-cs.333 |
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