Fast multi-output relevance vector regression for joint groundwater and lake water depth modeling

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buir.contributor.authorRahimzadeh Arashloo, Shervin
buir.contributor.orcidRahimzadeh Arashloo, Shervin|0000-0003-0189-4774
dc.citation.epage105425-11en_US
dc.citation.spage105425-1en_US
dc.citation.volumeNumber154en_US
dc.contributor.authorSafari, M. J. S.
dc.contributor.authorRahimzadeh Arashloo, Shervin
dc.contributor.authorVaheddoost, B.
dc.date.accessioned2023-02-23T14:02:29Z
dc.date.available2023-02-23T14:02:29Z
dc.date.issued2022-08
dc.description.abstractFast multi-output relevance vector regression (FMRVR) algorithm is developed for simultaneous estimation of groundwater and lake water depth for the first time in this study. The FMRVR is a multi-output regression analysis technique which can simultaneously predict multiple outputs for a multi-dimensional input. The data used in this study is collected from 34 stations located in the lake Urmia basin over a 40-year time period. The performance of the FMRVR model is examined in contrast to the support vector regression (SVR) and multi-linear regression (MLR) benchmarks. Results reveal that FMRVR is able to generate more accurate estimation for groundwater and lake water depth with coefficient of determination (R2) of 0.856 and 0.992 and root mean square error (RMSE) of 0.857 and 0.083, respectively. The outperformance of FMRVR can be linked to its capability for a joint estimation of multiple relevant outputs by taking into account possible correlations among the outputs.en_US
dc.description.provenanceSubmitted by Bilge Kat (bilgekat@bilkent.edu.tr) on 2023-02-23T14:02:29Z No. of bitstreams: 1 Fast_multi-output_relevance_vector_regression_for_joint_groundwater_and_lake_water_depth_modeling.pdf: 3601916 bytes, checksum: df49fa68e90211e1112deabb22a89b1d (MD5)en
dc.description.provenanceMade available in DSpace on 2023-02-23T14:02:29Z (GMT). No. of bitstreams: 1 Fast_multi-output_relevance_vector_regression_for_joint_groundwater_and_lake_water_depth_modeling.pdf: 3601916 bytes, checksum: df49fa68e90211e1112deabb22a89b1d (MD5) Previous issue date: 2022-08en
dc.identifier.doi10.1016/j.envsoft.2022.105425en_US
dc.identifier.eissn1873-6726
dc.identifier.issn1364-8152
dc.identifier.urihttp://hdl.handle.net/11693/111645
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttps://doi.org/10.1016/j.envsoft.2022.105425en_US
dc.source.titleEnvironmental Modelling & Softwareen_US
dc.subjectFast multi-output relevance vector regressionen_US
dc.subjectGroundwateren_US
dc.subjectLake urmiaen_US
dc.subjectLake water depthen_US
dc.subjectMulti-output regressionen_US
dc.subjectSupport vector regressionen_US
dc.titleFast multi-output relevance vector regression for joint groundwater and lake water depth modelingen_US
dc.typeArticleen_US

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