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Reliability prediction and evaluation of communication base stations in earthquake prone areas

One of the primary tasks for effective disaster relief after a catastrophic earthquake is robust communication. In this paper, we propose a simple logistic method based on two-parameter sets of geology and building structure for the failure prediction of the base stations in post-earthquake. Using t...

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Autores principales: Li, Xueming, Wei, Yao, Ming, Zheng, Cong, Hao, Zheng, Xuanyu, Chang, Qihai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238392/
https://www.ncbi.nlm.nih.gov/pubmed/37268681
http://dx.doi.org/10.1038/s41598-023-35841-x
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author Li, Xueming
Wei, Yao
Ming, Zheng
Cong, Hao
Zheng, Xuanyu
Chang, Qihai
author_facet Li, Xueming
Wei, Yao
Ming, Zheng
Cong, Hao
Zheng, Xuanyu
Chang, Qihai
author_sort Li, Xueming
collection PubMed
description One of the primary tasks for effective disaster relief after a catastrophic earthquake is robust communication. In this paper, we propose a simple logistic method based on two-parameter sets of geology and building structure for the failure prediction of the base stations in post-earthquake. Using the post-earthquake base station data in Sichuan, China, the prediction results are 96.7% and 90% for the two-parameter sets and all parameter sets, respectively, and 93.3% for the neural network method sets. The results show that the two-parameter method outweighs the whole parameter set logistic method and the neural network prediction and can effectively improve the accuracy of the prediction. The weight parameters of two-parameter set by the actual field data significantly show that the failure of base stations after earthquake is mainly due to the geological differences where the base stations are located. It can be envisioned that if the geological distribution between the earthquake source and the base station is parameterized, the multi-parameter sets logistic method can not only effectively solve the failure prediction after earthquakes and the evaluation of communication base stations under complex conditions, but also provide site selection evaluation for the construction of civil buildings and power grid towers in earthquake-prone areas.
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spelling pubmed-102383922023-06-04 Reliability prediction and evaluation of communication base stations in earthquake prone areas Li, Xueming Wei, Yao Ming, Zheng Cong, Hao Zheng, Xuanyu Chang, Qihai Sci Rep Article One of the primary tasks for effective disaster relief after a catastrophic earthquake is robust communication. In this paper, we propose a simple logistic method based on two-parameter sets of geology and building structure for the failure prediction of the base stations in post-earthquake. Using the post-earthquake base station data in Sichuan, China, the prediction results are 96.7% and 90% for the two-parameter sets and all parameter sets, respectively, and 93.3% for the neural network method sets. The results show that the two-parameter method outweighs the whole parameter set logistic method and the neural network prediction and can effectively improve the accuracy of the prediction. The weight parameters of two-parameter set by the actual field data significantly show that the failure of base stations after earthquake is mainly due to the geological differences where the base stations are located. It can be envisioned that if the geological distribution between the earthquake source and the base station is parameterized, the multi-parameter sets logistic method can not only effectively solve the failure prediction after earthquakes and the evaluation of communication base stations under complex conditions, but also provide site selection evaluation for the construction of civil buildings and power grid towers in earthquake-prone areas. Nature Publishing Group UK 2023-06-02 /pmc/articles/PMC10238392/ /pubmed/37268681 http://dx.doi.org/10.1038/s41598-023-35841-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Li, Xueming
Wei, Yao
Ming, Zheng
Cong, Hao
Zheng, Xuanyu
Chang, Qihai
Reliability prediction and evaluation of communication base stations in earthquake prone areas
title Reliability prediction and evaluation of communication base stations in earthquake prone areas
title_full Reliability prediction and evaluation of communication base stations in earthquake prone areas
title_fullStr Reliability prediction and evaluation of communication base stations in earthquake prone areas
title_full_unstemmed Reliability prediction and evaluation of communication base stations in earthquake prone areas
title_short Reliability prediction and evaluation of communication base stations in earthquake prone areas
title_sort reliability prediction and evaluation of communication base stations in earthquake prone areas
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10238392/
https://www.ncbi.nlm.nih.gov/pubmed/37268681
http://dx.doi.org/10.1038/s41598-023-35841-x
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