An empirical comparison of machine learning techniques for dam behaviour modelling

Predictive models are essential in dam safety assessment. Both deterministic and statistical models applied in the day-to-day practice have demonstrated to be useful, although they show relevant limitations at the same time. On another note, powerful learning algorithms have been developed in the fi...

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Autores: Salazar González, Fernando, Toledo Municio, Miguel Ángel, Oñate Ibáñez de Navarra, Eugenio|||0000-0002-0804-7095, Morán Moya, Rafael
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/76195
Acceso en línea:https://hdl.handle.net/2117/76195
https://dx.doi.org/10.1016/j.strusafe.2015.05.001
Access Level:acceso abierto
Palabra clave:Dam safety
Neural networks (Computer science)
Machine learning
Dam monitoring
Boosted regression trees
Neural networks
Random forests
MARS
Support vector machines
Leakage flow
Preses (Enginyeria) -- Mesures de seguretat
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i preses
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oai_identifier_str oai:upcommons.upc.edu:2117/76195
network_acronym_str ES
network_name_str España
repository_id_str
spelling An empirical comparison of machine learning techniques for dam behaviour modellingSalazar González, FernandoToledo Municio, Miguel ÁngelOñate Ibáñez de Navarra, Eugenio|||0000-0002-0804-7095Morán Moya, RafaelDam safetyNeural networks (Computer science)Machine learningDam monitoringDam safetyMachine learningBoosted regression treesNeural networksRandom forestsMARSSupport vector machinesLeakage flowPreses (Enginyeria) -- Mesures de seguretatAprenentatge automàticÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticÀrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i presesPredictive models are essential in dam safety assessment. Both deterministic and statistical models applied in the day-to-day practice have demonstrated to be useful, although they show relevant limitations at the same time. On another note, powerful learning algorithms have been developed in the field of machine learning (ML), which have been applied to solve practical problems. The work aims at testing the prediction capability of some state-of-the-art algorithms to model dam behaviour, in terms of displacements and leakage. Models based on random forests (RF), boosted regression trees (BRT), neural networks (NN), support vector machines (SVM) and multivariate adaptive regression splines (MARS) are fitted to predict 14 target variables. Prediction accuracy is compared with the conventional statistical model, which shows poorer performance on average. BRT models stand out as the most accurate overall, followed by NN and RF. It was also verified that the model fit can be improved by removing the records of the first years of dam functioning from the training set.Peer Reviewed20152015-09-0120152015-07-17journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/76195https://dx.doi.org/10.1016/j.strusafe.2015.05.001reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/761952026-05-27T15:37:01Z
dc.title.none.fl_str_mv An empirical comparison of machine learning techniques for dam behaviour modelling
title An empirical comparison of machine learning techniques for dam behaviour modelling
spellingShingle An empirical comparison of machine learning techniques for dam behaviour modelling
Salazar González, Fernando
Dam safety
Neural networks (Computer science)
Machine learning
Dam monitoring
Dam safety
Machine learning
Boosted regression trees
Neural networks
Random forests
MARS
Support vector machines
Leakage flow
Preses (Enginyeria) -- Mesures de seguretat
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i preses
title_short An empirical comparison of machine learning techniques for dam behaviour modelling
title_full An empirical comparison of machine learning techniques for dam behaviour modelling
title_fullStr An empirical comparison of machine learning techniques for dam behaviour modelling
title_full_unstemmed An empirical comparison of machine learning techniques for dam behaviour modelling
title_sort An empirical comparison of machine learning techniques for dam behaviour modelling
dc.creator.none.fl_str_mv Salazar González, Fernando
Toledo Municio, Miguel Ángel
Oñate Ibáñez de Navarra, Eugenio|||0000-0002-0804-7095
Morán Moya, Rafael
author Salazar González, Fernando
author_facet Salazar González, Fernando
Toledo Municio, Miguel Ángel
Oñate Ibáñez de Navarra, Eugenio|||0000-0002-0804-7095
Morán Moya, Rafael
author_role author
author2 Toledo Municio, Miguel Ángel
Oñate Ibáñez de Navarra, Eugenio|||0000-0002-0804-7095
Morán Moya, Rafael
author2_role author
author
author
dc.subject.none.fl_str_mv Dam safety
Neural networks (Computer science)
Machine learning
Dam monitoring
Dam safety
Machine learning
Boosted regression trees
Neural networks
Random forests
MARS
Support vector machines
Leakage flow
Preses (Enginyeria) -- Mesures de seguretat
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i preses
topic Dam safety
Neural networks (Computer science)
Machine learning
Dam monitoring
Dam safety
Machine learning
Boosted regression trees
Neural networks
Random forests
MARS
Support vector machines
Leakage flow
Preses (Enginyeria) -- Mesures de seguretat
Aprenentatge automàtic
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Embassaments i preses
description Predictive models are essential in dam safety assessment. Both deterministic and statistical models applied in the day-to-day practice have demonstrated to be useful, although they show relevant limitations at the same time. On another note, powerful learning algorithms have been developed in the field of machine learning (ML), which have been applied to solve practical problems. The work aims at testing the prediction capability of some state-of-the-art algorithms to model dam behaviour, in terms of displacements and leakage. Models based on random forests (RF), boosted regression trees (BRT), neural networks (NN), support vector machines (SVM) and multivariate adaptive regression splines (MARS) are fitted to predict 14 target variables. Prediction accuracy is compared with the conventional statistical model, which shows poorer performance on average. BRT models stand out as the most accurate overall, followed by NN and RF. It was also verified that the model fit can be improved by removing the records of the first years of dam functioning from the training set.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-09-01
2015
2015-07-17
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/76195
https://dx.doi.org/10.1016/j.strusafe.2015.05.001
url https://hdl.handle.net/2117/76195
https://dx.doi.org/10.1016/j.strusafe.2015.05.001
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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