Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)

In the present study we compare performances of the prediction of hourly tidal level variations at Puerto Belgrano, a coastal site in the Bahia Blanca Estuary (Argentina), by means of the MOHID model, which is a numerical model designed for coastal and estuarine shallow water applications, and of an...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Acta geophysica 2013-12, Vol.61 (6), p.1522-1537
Hauptverfasser: Pierini, Jorge O., Lovallo, Michele, Telesca, Luciano, Gómez, Eduardo A.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1537
container_issue 6
container_start_page 1522
container_title Acta geophysica
container_volume 61
creator Pierini, Jorge O.
Lovallo, Michele
Telesca, Luciano
Gómez, Eduardo A.
description In the present study we compare performances of the prediction of hourly tidal level variations at Puerto Belgrano, a coastal site in the Bahia Blanca Estuary (Argentina), by means of the MOHID model, which is a numerical model designed for coastal and estuarine shallow water applications, and of an artificial neural network (ANN). It was shown that the ANN model is able to predict the hourly tidal levels over long term duration with at least seven days of observations and with a better performance in respect to the numerical model. Our findings can be useful to implement ANN-based tools for future studies of the hydrodynamics of Bahía Blanca estuary.
doi_str_mv 10.2478/s11600-012-0093-x
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1464554964</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3082000991</sourcerecordid><originalsourceid>FETCH-LOGICAL-a429t-79bc7d14c2d2ef314c33a0aff917d7e078812e1e020fbfeb1757acef5f5807953</originalsourceid><addsrcrecordid>eNqNUctu1TAQjRBIlMIHsLPEpkgEbMeOkx29VWkrVWoXsI7mJuPUJbEvttPH9_CjzCVdICQkVjOaOWfOzJyieCv4R6lM8ykJUXNeciFLztuqfHhWHIim1aVRWj9_ymtt5MviVUq3nNeKsAfFzwt_hym7EbLzI9tFHFyfXfBsh9GGOIPvkQXLwDOI2VnXO5iYxyX-Dvk-xO_UHBgwv8wYXU_1OQw47Vn5Bll2A5WSGz0FyOx6wZgD2-A0RvDhA9vAjQO2mUgK2GnKC8RHdnQcR_S0FLx_XbywMCV88xQPi29fTr-enJeXV2cXJ8eXJSjZ5tK0294MQvVykGgrSqoKOFjbCjMY5KZphESBXHK7tbgVRhvo0WqrG25aXR0WR-vcXQw_FvpKN7vU40SLYVhSJ1RNz1RtrQj67i_obVgiHbhHVXVtjFCcUGJF9TGkFNF2u-hmuq4TvNvb1q22dWRFt7eteyDO55VzD1PGOOAYl0dK_hD4F7cWtdBS0gi5jkgk58f_4la_AMGysc4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1436677140</pqid></control><display><type>article</type><title>Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)</title><source>SpringerLink Journals - AutoHoldings</source><creator>Pierini, Jorge O. ; Lovallo, Michele ; Telesca, Luciano ; Gómez, Eduardo A.</creator><creatorcontrib>Pierini, Jorge O. ; Lovallo, Michele ; Telesca, Luciano ; Gómez, Eduardo A.</creatorcontrib><description>In the present study we compare performances of the prediction of hourly tidal level variations at Puerto Belgrano, a coastal site in the Bahia Blanca Estuary (Argentina), by means of the MOHID model, which is a numerical model designed for coastal and estuarine shallow water applications, and of an artificial neural network (ANN). It was shown that the ANN model is able to predict the hourly tidal levels over long term duration with at least seven days of observations and with a better performance in respect to the numerical model. Our findings can be useful to implement ANN-based tools for future studies of the hydrodynamics of Bahía Blanca estuary.</description><identifier>ISSN: 1895-6572</identifier><identifier>EISSN: 1895-7455</identifier><identifier>DOI: 10.2478/s11600-012-0093-x</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Artificial neural networks ; Coastal ; Computational fluid dynamics ; Earth and Environmental Science ; Earth Sciences ; Estuaries ; Fluid dynamics ; Geophysics ; Geophysics/Geodesy ; Geotechnical Engineering &amp; Applied Earth Sciences ; hydrodinamic model ; Hydrodynamics ; Learning theory ; Mathematical models ; Neural networks ; Research Article ; Shallow water ; Structural Geology ; Tidal waves ; tides</subject><ispartof>Acta geophysica, 2013-12, Vol.61 (6), p.1522-1537</ispartof><rights>Versita Warsaw and Springer-Verlag Wien 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a429t-79bc7d14c2d2ef314c33a0aff917d7e078812e1e020fbfeb1757acef5f5807953</citedby><cites>FETCH-LOGICAL-a429t-79bc7d14c2d2ef314c33a0aff917d7e078812e1e020fbfeb1757acef5f5807953</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.2478/s11600-012-0093-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.2478/s11600-012-0093-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids></links><search><creatorcontrib>Pierini, Jorge O.</creatorcontrib><creatorcontrib>Lovallo, Michele</creatorcontrib><creatorcontrib>Telesca, Luciano</creatorcontrib><creatorcontrib>Gómez, Eduardo A.</creatorcontrib><title>Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)</title><title>Acta geophysica</title><addtitle>Acta Geophys</addtitle><description>In the present study we compare performances of the prediction of hourly tidal level variations at Puerto Belgrano, a coastal site in the Bahia Blanca Estuary (Argentina), by means of the MOHID model, which is a numerical model designed for coastal and estuarine shallow water applications, and of an artificial neural network (ANN). It was shown that the ANN model is able to predict the hourly tidal levels over long term duration with at least seven days of observations and with a better performance in respect to the numerical model. Our findings can be useful to implement ANN-based tools for future studies of the hydrodynamics of Bahía Blanca estuary.</description><subject>Artificial neural networks</subject><subject>Coastal</subject><subject>Computational fluid dynamics</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Estuaries</subject><subject>Fluid dynamics</subject><subject>Geophysics</subject><subject>Geophysics/Geodesy</subject><subject>Geotechnical Engineering &amp; Applied Earth Sciences</subject><subject>hydrodinamic model</subject><subject>Hydrodynamics</subject><subject>Learning theory</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Research Article</subject><subject>Shallow water</subject><subject>Structural Geology</subject><subject>Tidal waves</subject><subject>tides</subject><issn>1895-6572</issn><issn>1895-7455</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNUctu1TAQjRBIlMIHsLPEpkgEbMeOkx29VWkrVWoXsI7mJuPUJbEvttPH9_CjzCVdICQkVjOaOWfOzJyieCv4R6lM8ykJUXNeciFLztuqfHhWHIim1aVRWj9_ymtt5MviVUq3nNeKsAfFzwt_hym7EbLzI9tFHFyfXfBsh9GGOIPvkQXLwDOI2VnXO5iYxyX-Dvk-xO_UHBgwv8wYXU_1OQw47Vn5Bll2A5WSGz0FyOx6wZgD2-A0RvDhA9vAjQO2mUgK2GnKC8RHdnQcR_S0FLx_XbywMCV88xQPi29fTr-enJeXV2cXJ8eXJSjZ5tK0294MQvVykGgrSqoKOFjbCjMY5KZphESBXHK7tbgVRhvo0WqrG25aXR0WR-vcXQw_FvpKN7vU40SLYVhSJ1RNz1RtrQj67i_obVgiHbhHVXVtjFCcUGJF9TGkFNF2u-hmuq4TvNvb1q22dWRFt7eteyDO55VzD1PGOOAYl0dK_hD4F7cWtdBS0gi5jkgk58f_4la_AMGysc4</recordid><startdate>20131201</startdate><enddate>20131201</enddate><creator>Pierini, Jorge O.</creator><creator>Lovallo, Michele</creator><creator>Telesca, Luciano</creator><creator>Gómez, Eduardo A.</creator><general>Springer Vienna</general><general>Versita</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>7SC</scope><scope>7SM</scope><scope>JQ2</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20131201</creationdate><title>Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)</title><author>Pierini, Jorge O. ; Lovallo, Michele ; Telesca, Luciano ; Gómez, Eduardo A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a429t-79bc7d14c2d2ef314c33a0aff917d7e078812e1e020fbfeb1757acef5f5807953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Artificial neural networks</topic><topic>Coastal</topic><topic>Computational fluid dynamics</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Estuaries</topic><topic>Fluid dynamics</topic><topic>Geophysics</topic><topic>Geophysics/Geodesy</topic><topic>Geotechnical Engineering &amp; Applied Earth Sciences</topic><topic>hydrodinamic model</topic><topic>Hydrodynamics</topic><topic>Learning theory</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Research Article</topic><topic>Shallow water</topic><topic>Structural Geology</topic><topic>Tidal waves</topic><topic>tides</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pierini, Jorge O.</creatorcontrib><creatorcontrib>Lovallo, Michele</creatorcontrib><creatorcontrib>Telesca, Luciano</creatorcontrib><creatorcontrib>Gómez, Eduardo A.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>Computer and Information Systems Abstracts</collection><collection>Earthquake Engineering Abstracts</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Acta geophysica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pierini, Jorge O.</au><au>Lovallo, Michele</au><au>Telesca, Luciano</au><au>Gómez, Eduardo A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)</atitle><jtitle>Acta geophysica</jtitle><stitle>Acta Geophys</stitle><date>2013-12-01</date><risdate>2013</risdate><volume>61</volume><issue>6</issue><spage>1522</spage><epage>1537</epage><pages>1522-1537</pages><issn>1895-6572</issn><eissn>1895-7455</eissn><abstract>In the present study we compare performances of the prediction of hourly tidal level variations at Puerto Belgrano, a coastal site in the Bahia Blanca Estuary (Argentina), by means of the MOHID model, which is a numerical model designed for coastal and estuarine shallow water applications, and of an artificial neural network (ANN). It was shown that the ANN model is able to predict the hourly tidal levels over long term duration with at least seven days of observations and with a better performance in respect to the numerical model. Our findings can be useful to implement ANN-based tools for future studies of the hydrodynamics of Bahía Blanca estuary.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.2478/s11600-012-0093-x</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1895-6572
ispartof Acta geophysica, 2013-12, Vol.61 (6), p.1522-1537
issn 1895-6572
1895-7455
language eng
recordid cdi_proquest_miscellaneous_1464554964
source SpringerLink Journals - AutoHoldings
subjects Artificial neural networks
Coastal
Computational fluid dynamics
Earth and Environmental Science
Earth Sciences
Estuaries
Fluid dynamics
Geophysics
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
hydrodinamic model
Hydrodynamics
Learning theory
Mathematical models
Neural networks
Research Article
Shallow water
Structural Geology
Tidal waves
tides
title Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina)
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-09T21%3A14%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Investigating%20prediction%20performance%20of%20an%20artificial%20neural%20network%20and%20a%20numerical%20model%20of%20the%20tidal%20signal%20at%20Puerto%20Belgrano,%20Bahia%20Blanca%20Estuary%20(Argentina)&rft.jtitle=Acta%20geophysica&rft.au=Pierini,%20Jorge%20O.&rft.date=2013-12-01&rft.volume=61&rft.issue=6&rft.spage=1522&rft.epage=1537&rft.pages=1522-1537&rft.issn=1895-6572&rft.eissn=1895-7455&rft_id=info:doi/10.2478/s11600-012-0093-x&rft_dat=%3Cproquest_cross%3E3082000991%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1436677140&rft_id=info:pmid/&rfr_iscdi=true