Surface Water Sodium (na+) Concentration Prediction Using Hybrid Weighted Exponential Regression Model with Gradient-Based Optimization.

Undeniably, there is a link between water resources and people’s lives and, consequently, economic development, which makes them vital in health and the environment. Proper water quality forecasting time series has a crucial role in giving on-time warnings for water pollution and supporting the decision-making of water resource management. The principal aim of this study is to develop a novel and cutting-edge ensemble data intelligence model named the weighted exponential regression and hybridized by gradient-based optimization (WER-GBO). Indeed, this is to reach more meticulous sodium (Na+) prediction monthly at Maroon River in the southwest of Iran. This developed model has advantages over other previous methodologies thanks to the following merits: (i) it can improve the performance and ability by mixing the outputs of four distinct data intelligence (DI) models, i.e., adaptive neuro-fuzzy inference system (ANFIS), least square support vector regression (LSSVM), Bayesian linear regression (BLR), and response surface regression (RSR); (ii) the proposed model can employ a Cauchy weighted function combined with an exponential-based regression model being optimized by GBO algorithm. To evaluate the performance of these models, diverse statistical indices and graphical assessment including error distributions, box plots, scatter-plots with confidence bounds and Taylor diagrams were conducted. According to obtained statistical metrics and verified validation procedures, the proposed WER-GBO resulted in promising accuracy compared to other models. Furthermore, the outcomes revealed the WER-GBO (R = 0.9712, RMSE = 0.639, and KGE = 0.948) reached more accurate and reliable results than other methods such as the ANFIS, LSSVM, BLR, and RSR for Na prediction in this study. Hence, the WER-GBO model can be considered a constructive technique to forecast the water quality parameters.