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

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      Author(s)
      Safari, M. J. S.
      Rahimzadeh Arashloo, Shervin
      Vaheddoost, B.
      Date
      2022-08
      Source Title
      Environmental Modelling & Software
      Print ISSN
      1364-8152
      Electronic ISSN
      1873-6726
      Publisher
      Elsevier
      Volume
      154
      Pages
      105425-1 - 105425-11
      Language
      English
      Type
      Article
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      Abstract
      Fast 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.
      Keywords
      Fast multi-output relevance vector regression
      Groundwater
      Lake urmia
      Lake water depth
      Multi-output regression
      Support vector regression
      Permalink
      http://hdl.handle.net/11693/111645
      Published Version (Please cite this version)
      https://doi.org/10.1016/j.envsoft.2022.105425
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      • Department of Computer Engineering 1561
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