Impact wave predictions by a Fuzzy ARTMAP neural network
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.oceaneng.2020.107165 http://hdl.handle.net/11449/201108 |
Resumo: | Impact waves caused by landslides, rock blocks, and avalanches in lakes and dam reservoirs have provoked several disasters since the dawn of time; further, they have caused considerable number of deaths and property loss. Understanding the behavior and characteristics of these waves, principally their height, propagation velocity, and energy, are key for determining engineering parameters, and therefore developing alert systems and evacuation plans. Several studies have investigated impact waves using experimental, mathematical, and numerical models; however, few have used artificial neural networks. Considering the learning and prediction characteristics of neural networks, this work explores their application in the prediction of impact waves. The main objective is to verify the capability of a fuzzy ARTMAP neural network in predicting the evolution of the maximum wave height, one of the main parameters of impact waves. The experimental data used in this paper are from the work developed by Huber (1980) when studying the impact waves generated by dropping deformable material (granular) in a channel. This study uses different forms of normalization, as well as different training parameters. The Fuzzy ARTMAP neural network predicts adequately the evolution of the maximum wave heights, becoming useful for this kind of application. |
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Repositório Institucional da UNESP |
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Impact wave predictions by a Fuzzy ARTMAP neural networkFuzzy ARTMAPImpact wavesIntelligent systemsImpact waves caused by landslides, rock blocks, and avalanches in lakes and dam reservoirs have provoked several disasters since the dawn of time; further, they have caused considerable number of deaths and property loss. Understanding the behavior and characteristics of these waves, principally their height, propagation velocity, and energy, are key for determining engineering parameters, and therefore developing alert systems and evacuation plans. Several studies have investigated impact waves using experimental, mathematical, and numerical models; however, few have used artificial neural networks. Considering the learning and prediction characteristics of neural networks, this work explores their application in the prediction of impact waves. The main objective is to verify the capability of a fuzzy ARTMAP neural network in predicting the evolution of the maximum wave height, one of the main parameters of impact waves. The experimental data used in this paper are from the work developed by Huber (1980) when studying the impact waves generated by dropping deformable material (granular) in a channel. This study uses different forms of normalization, as well as different training parameters. The Fuzzy ARTMAP neural network predicts adequately the evolution of the maximum wave heights, becoming useful for this kind of application.Universidade Estadual PaulistaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Faculty of Engineering of Ilha Solteira - UNESP, Ilha SolteiraDepartment of Electrical Engineering Campus III, Ilha SolteiraDepartment of Mathematics, Rio de Janeiro Street, 266, Ilha SolteiraDepartment of Civil Engineering, Alameda Bahia, 550, Ilha SolteiraFederal Institute of Education Science and Technology of the State of São Paulo, Alameda Tucuruí, 164, Ilha SolteiraFaculty of Engineering of Ilha Solteira - UNESP, Ilha SolteiraCAPES: 001Universidade Estadual Paulista (Unesp)Department of Electrical EngineeringScience and Technology of the State of São PauloOliveira, P. B.A. [UNESP]Lotufo, A. D.P. [UNESP]Lopes, M. L.M. [UNESP]Maciel, G. F. [UNESP]2020-12-12T02:24:15Z2020-12-12T02:24:15Z2020-04-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.oceaneng.2020.107165Ocean Engineering, v. 202.0029-8018http://hdl.handle.net/11449/20110810.1016/j.oceaneng.2020.1071652-s2.0-85080137485Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengOcean Engineeringinfo:eu-repo/semantics/openAccess2021-10-23T16:01:12Zoai:repositorio.unesp.br:11449/201108Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T16:01:12Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Impact wave predictions by a Fuzzy ARTMAP neural network |
title |
Impact wave predictions by a Fuzzy ARTMAP neural network |
spellingShingle |
Impact wave predictions by a Fuzzy ARTMAP neural network Oliveira, P. B.A. [UNESP] Fuzzy ARTMAP Impact waves Intelligent systems |
title_short |
Impact wave predictions by a Fuzzy ARTMAP neural network |
title_full |
Impact wave predictions by a Fuzzy ARTMAP neural network |
title_fullStr |
Impact wave predictions by a Fuzzy ARTMAP neural network |
title_full_unstemmed |
Impact wave predictions by a Fuzzy ARTMAP neural network |
title_sort |
Impact wave predictions by a Fuzzy ARTMAP neural network |
author |
Oliveira, P. B.A. [UNESP] |
author_facet |
Oliveira, P. B.A. [UNESP] Lotufo, A. D.P. [UNESP] Lopes, M. L.M. [UNESP] Maciel, G. F. [UNESP] |
author_role |
author |
author2 |
Lotufo, A. D.P. [UNESP] Lopes, M. L.M. [UNESP] Maciel, G. F. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Department of Electrical Engineering Science and Technology of the State of São Paulo |
dc.contributor.author.fl_str_mv |
Oliveira, P. B.A. [UNESP] Lotufo, A. D.P. [UNESP] Lopes, M. L.M. [UNESP] Maciel, G. F. [UNESP] |
dc.subject.por.fl_str_mv |
Fuzzy ARTMAP Impact waves Intelligent systems |
topic |
Fuzzy ARTMAP Impact waves Intelligent systems |
description |
Impact waves caused by landslides, rock blocks, and avalanches in lakes and dam reservoirs have provoked several disasters since the dawn of time; further, they have caused considerable number of deaths and property loss. Understanding the behavior and characteristics of these waves, principally their height, propagation velocity, and energy, are key for determining engineering parameters, and therefore developing alert systems and evacuation plans. Several studies have investigated impact waves using experimental, mathematical, and numerical models; however, few have used artificial neural networks. Considering the learning and prediction characteristics of neural networks, this work explores their application in the prediction of impact waves. The main objective is to verify the capability of a fuzzy ARTMAP neural network in predicting the evolution of the maximum wave height, one of the main parameters of impact waves. The experimental data used in this paper are from the work developed by Huber (1980) when studying the impact waves generated by dropping deformable material (granular) in a channel. This study uses different forms of normalization, as well as different training parameters. The Fuzzy ARTMAP neural network predicts adequately the evolution of the maximum wave heights, becoming useful for this kind of application. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:24:15Z 2020-12-12T02:24:15Z 2020-04-15 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.oceaneng.2020.107165 Ocean Engineering, v. 202. 0029-8018 http://hdl.handle.net/11449/201108 10.1016/j.oceaneng.2020.107165 2-s2.0-85080137485 |
url |
http://dx.doi.org/10.1016/j.oceaneng.2020.107165 http://hdl.handle.net/11449/201108 |
identifier_str_mv |
Ocean Engineering, v. 202. 0029-8018 10.1016/j.oceaneng.2020.107165 2-s2.0-85080137485 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ocean Engineering |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965530397868032 |