Fast multi-output relevance vector regression for joint groundwater and lake water depth modeling. (August 2022) - Safari, Mir Jafar Sadegh; Rahimzadeh Arashloo, Shervin; Vaheddoost, Babak
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.