Fast multi-output relevance vector regression for joint groundwater and lake water depth modeling. (August 2022)

• Record Type: Journal Article
• Authors: Safari, Mir Jafar Sadegh; Rahimzadeh Arashloo, Shervin; Vaheddoost, Babak
• Is Part Of: Environmental modelling & software. Volume 154(2022)
• Publication Date: 2022-08
• Subjects: Fast multi-output relevance vector regression -- Groundwater -- Lake urmia -- Lake water depth -- Multi-output regression -- Support vector regression; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105425
• Languages: English
• ISSNs: 1364-8152

Abstract

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 ( R 2 ) 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. Highlights: Fast multi-output relevance vector regression (FMRVR) is recommended for modeling. FMRVR is applied for simultaneous estimation of groundwater and lake water depth. Hydro-meteorological parameters are incorporated into the model structure. FMRVR outperforms support vector regression and multi-linear regression models. Groundwater and lake water depth parameters tend to be correlated variables.