Water quality is one of the most important factors contributing to a healthy life; meanwhile, total dissolved solids (TDS) and electrical conductivity (EC) are the most important parameters in water quality, and many water developing plans have been implemented for the recognition of these factors. The accurate prediction of water quality parameters (WQPs) is an essential requisite for water quality management, human health, public consumption, and domestic uses. Using three novel data preprocessing algorithms (DPAs), including empirical mode decomposition (EMD), ensemble EMD (EEMD), and variational mode decomposition (VMD) to estimate two important WQPs, TDS and EC, differentiates this study from the existing literature. The acceptability and reliability of the proposed models (e.g., model tree (MT), EMD-MT, EEMD-MT, and VMD-MT) were evaluated using five performance metrics and visual plots. A comparison of the performances of standalone and hybrid models indicated that DPAs can enhance the performance of standalone MT model for both TDS and EC estimations. For instance, the VMD-MT model (root-mean-square error (RMSE) = 24.41 mg/l, ratio of RMSE to SD (RSD) = 0.231, and Nash–Sutcliffe efficiency (Ens) = 0.94 (Garmrood) and RMSE = 31.85 mg/l, RSD = 0.133, and Ens = 0.98 (Varand)) outperformed other hybrid models and original MT models for TDS estimations. Regarding the EC estimation results, as for R2, VMD could enhance the accuracy of prediction for the MT model for Garmrood and Varand stations by 10.2 and 7.6%, respectively.
Two important water quality parameters, TDS and EC, were modeled in this study.
Three data preprocessing algorithms were used to address the nonstationary of the dataset.
To validate proposed models, a classification-based MT was used as the benchmark model.
The VMD-MT proves to be an effective tool to provide strong technical support for WQPs.