Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis
Multicollinearity and difficulty of interpreting the coefficients of dam regression models pose two problems: (1) selection of informative variables for analysing dam deformation behaviour, and (2) mitigation of the multicollinearity among the variables. Resolving these two problems necessitates the...
Gespeichert in:
| Veröffentlicht in: | Engineering applications of artificial intelligence 2012-04, Vol.25 (3), p.468-475 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 475 |
|---|---|
| container_issue | 3 |
| container_start_page | 468 |
| container_title | Engineering applications of artificial intelligence |
| container_volume | 25 |
| creator | Xu, Chang Yue, Dongjie Deng, Chengfa |
| description | Multicollinearity and difficulty of interpreting the coefficients of dam regression models pose two problems: (1) selection of informative variables for analysing dam deformation behaviour, and (2) mitigation of the multicollinearity among the variables. Resolving these two problems necessitates the application of genetic algorithm-based partial least square (GA-PLS) and statistically inspired modification of PLS algorithm (SIMPLS). A SIMPLS regression with the predictor variables selected by GA-PLS (hybrid GA/SIMPLS regression) is put forward to interpret the results obtained from periodic monitoring surveys of hydraulic structures. The hybrid model is employed for analysing the crack behaviour of an earth-rock dam in China. The results show the proposed model is superior to an ordinary SIMPLS and stepwise regression, especially when multicollinearity and influential outliers exist among the variables. |
| doi_str_mv | 10.1016/j.engappai.2011.09.020 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1019632482</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0952197611001734</els_id><sourcerecordid>1019632482</sourcerecordid><originalsourceid>FETCH-LOGICAL-c345t-ddf5e1e80d446765bacbe23492d4ca0e6d5fc5ce63a870a399438f40b787ee273</originalsourceid><addsrcrecordid>eNqFkMtOwzAQRS0EEqXwCyhLNkn9ihPvqCpoKwWBVFhbjj2pXOVR7LRS_x5XhTWrWcy5VzMHoUeCM4KJmO0y6Ld6v9cuo5iQDMsMU3yFJqQsWCoKIa_RBMucpkQW4hbdhbDDGLOSiwmqVqfaO5ss57PN-u2j2iQ6JLodwfd6dEdIPGw9hOCGPukGC23i-sTqLrHQDL6LTFzoXren4MI9uml0G-Dhd07R1-vL52KVVu_L9WJepYbxfEytbXIgUGLLeTwvr7WpgTIuqeVGYxA2b0xuQDBdFlgzKTkrG47roiwAaMGm6OnSu_fD9wHCqDoXDLSt7mE4BBW1SMEoL2lExQU1fgjBQ6P23nXanyJ05oTaqT996qxPYamivhh8vgQhPnJ04FUwDnoD1nkwo7KD-6_iB1IYfFc</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1019632482</pqid></control><display><type>article</type><title>Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis</title><source>ScienceDirect Journals (5 years ago - present)</source><creator>Xu, Chang ; Yue, Dongjie ; Deng, Chengfa</creator><creatorcontrib>Xu, Chang ; Yue, Dongjie ; Deng, Chengfa</creatorcontrib><description>Multicollinearity and difficulty of interpreting the coefficients of dam regression models pose two problems: (1) selection of informative variables for analysing dam deformation behaviour, and (2) mitigation of the multicollinearity among the variables. Resolving these two problems necessitates the application of genetic algorithm-based partial least square (GA-PLS) and statistically inspired modification of PLS algorithm (SIMPLS). A SIMPLS regression with the predictor variables selected by GA-PLS (hybrid GA/SIMPLS regression) is put forward to interpret the results obtained from periodic monitoring surveys of hydraulic structures. The hybrid model is employed for analysing the crack behaviour of an earth-rock dam in China. The results show the proposed model is superior to an ordinary SIMPLS and stepwise regression, especially when multicollinearity and influential outliers exist among the variables.</description><identifier>ISSN: 0952-1976</identifier><identifier>EISSN: 1873-6769</identifier><identifier>DOI: 10.1016/j.engappai.2011.09.020</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Algorithms ; Crack ; Dam ; Deformation ; GA-PLS ; Genetic algorithms ; Genetics ; Least squares method ; Mathematical models ; Monitoring ; Multicollinearity ; Regression ; SIMPLS ; Stepwise</subject><ispartof>Engineering applications of artificial intelligence, 2012-04, Vol.25 (3), p.468-475</ispartof><rights>2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c345t-ddf5e1e80d446765bacbe23492d4ca0e6d5fc5ce63a870a399438f40b787ee273</citedby><cites>FETCH-LOGICAL-c345t-ddf5e1e80d446765bacbe23492d4ca0e6d5fc5ce63a870a399438f40b787ee273</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.engappai.2011.09.020$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,46000</link.rule.ids></links><search><creatorcontrib>Xu, Chang</creatorcontrib><creatorcontrib>Yue, Dongjie</creatorcontrib><creatorcontrib>Deng, Chengfa</creatorcontrib><title>Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis</title><title>Engineering applications of artificial intelligence</title><description>Multicollinearity and difficulty of interpreting the coefficients of dam regression models pose two problems: (1) selection of informative variables for analysing dam deformation behaviour, and (2) mitigation of the multicollinearity among the variables. Resolving these two problems necessitates the application of genetic algorithm-based partial least square (GA-PLS) and statistically inspired modification of PLS algorithm (SIMPLS). A SIMPLS regression with the predictor variables selected by GA-PLS (hybrid GA/SIMPLS regression) is put forward to interpret the results obtained from periodic monitoring surveys of hydraulic structures. The hybrid model is employed for analysing the crack behaviour of an earth-rock dam in China. The results show the proposed model is superior to an ordinary SIMPLS and stepwise regression, especially when multicollinearity and influential outliers exist among the variables.</description><subject>Algorithms</subject><subject>Crack</subject><subject>Dam</subject><subject>Deformation</subject><subject>GA-PLS</subject><subject>Genetic algorithms</subject><subject>Genetics</subject><subject>Least squares method</subject><subject>Mathematical models</subject><subject>Monitoring</subject><subject>Multicollinearity</subject><subject>Regression</subject><subject>SIMPLS</subject><subject>Stepwise</subject><issn>0952-1976</issn><issn>1873-6769</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNqFkMtOwzAQRS0EEqXwCyhLNkn9ihPvqCpoKwWBVFhbjj2pXOVR7LRS_x5XhTWrWcy5VzMHoUeCM4KJmO0y6Ld6v9cuo5iQDMsMU3yFJqQsWCoKIa_RBMucpkQW4hbdhbDDGLOSiwmqVqfaO5ss57PN-u2j2iQ6JLodwfd6dEdIPGw9hOCGPukGC23i-sTqLrHQDL6LTFzoXren4MI9uml0G-Dhd07R1-vL52KVVu_L9WJepYbxfEytbXIgUGLLeTwvr7WpgTIuqeVGYxA2b0xuQDBdFlgzKTkrG47roiwAaMGm6OnSu_fD9wHCqDoXDLSt7mE4BBW1SMEoL2lExQU1fgjBQ6P23nXanyJ05oTaqT996qxPYamivhh8vgQhPnJ04FUwDnoD1nkwo7KD-6_iB1IYfFc</recordid><startdate>201204</startdate><enddate>201204</enddate><creator>Xu, Chang</creator><creator>Yue, Dongjie</creator><creator>Deng, Chengfa</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201204</creationdate><title>Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis</title><author>Xu, Chang ; Yue, Dongjie ; Deng, Chengfa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c345t-ddf5e1e80d446765bacbe23492d4ca0e6d5fc5ce63a870a399438f40b787ee273</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithms</topic><topic>Crack</topic><topic>Dam</topic><topic>Deformation</topic><topic>GA-PLS</topic><topic>Genetic algorithms</topic><topic>Genetics</topic><topic>Least squares method</topic><topic>Mathematical models</topic><topic>Monitoring</topic><topic>Multicollinearity</topic><topic>Regression</topic><topic>SIMPLS</topic><topic>Stepwise</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Chang</creatorcontrib><creatorcontrib>Yue, Dongjie</creatorcontrib><creatorcontrib>Deng, Chengfa</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Engineering applications of artificial intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Chang</au><au>Yue, Dongjie</au><au>Deng, Chengfa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis</atitle><jtitle>Engineering applications of artificial intelligence</jtitle><date>2012-04</date><risdate>2012</risdate><volume>25</volume><issue>3</issue><spage>468</spage><epage>475</epage><pages>468-475</pages><issn>0952-1976</issn><eissn>1873-6769</eissn><abstract>Multicollinearity and difficulty of interpreting the coefficients of dam regression models pose two problems: (1) selection of informative variables for analysing dam deformation behaviour, and (2) mitigation of the multicollinearity among the variables. Resolving these two problems necessitates the application of genetic algorithm-based partial least square (GA-PLS) and statistically inspired modification of PLS algorithm (SIMPLS). A SIMPLS regression with the predictor variables selected by GA-PLS (hybrid GA/SIMPLS regression) is put forward to interpret the results obtained from periodic monitoring surveys of hydraulic structures. The hybrid model is employed for analysing the crack behaviour of an earth-rock dam in China. The results show the proposed model is superior to an ordinary SIMPLS and stepwise regression, especially when multicollinearity and influential outliers exist among the variables.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.engappai.2011.09.020</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0952-1976 |
| ispartof | Engineering applications of artificial intelligence, 2012-04, Vol.25 (3), p.468-475 |
| issn | 0952-1976 1873-6769 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1019632482 |
| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Algorithms Crack Dam Deformation GA-PLS Genetic algorithms Genetics Least squares method Mathematical models Monitoring Multicollinearity Regression SIMPLS Stepwise |
| title | Hybrid GA/SIMPLS as alternative regression model in dam deformation analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T16%3A52%3A32IST&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=Hybrid%20GA/SIMPLS%20as%20alternative%20regression%20model%20in%20dam%20deformation%20analysis&rft.jtitle=Engineering%20applications%20of%20artificial%20intelligence&rft.au=Xu,%20Chang&rft.date=2012-04&rft.volume=25&rft.issue=3&rft.spage=468&rft.epage=475&rft.pages=468-475&rft.issn=0952-1976&rft.eissn=1873-6769&rft_id=info:doi/10.1016/j.engappai.2011.09.020&rft_dat=%3Cproquest_cross%3E1019632482%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=1019632482&rft_id=info:pmid/&rft_els_id=S0952197611001734&rfr_iscdi=true |