Assessment of Groundwater Hardness in Khorramabad Plain Using Hybrid Models Based on Metaheuristic Algorithms

In this study, a hybrid Support Vector Regression (SVR) model, combined with optimization algorithms including Wavelet, Firefly, and Grey Wolf, was employed to estimate groundwater hardness at the Naservand well in the Khorramabad Plain of Lorestan Province. For modeling, monthly quality data of the well during the water year (1382-1402) were used. This included parameters such as bicarbonate (HCO3), chloride (Cl), sulfate (SO4), magnesium (Mg), calcium (Ca), and total hardness (TH), all measured in ppm, which were used as inputs. Groundwater hardness was selected as the model output. To evaluate the performance of the models, assessment metrics such as the correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency coefficient (NSE) were used. Time series, box plots, and Taylor diagrams were also used for analyzing the model results. The results indicated that hybrid scenarios in the studied models improve the model’s performance. Comparing results showed that the Support Vector Regression-Wavelet model performed better than the Support Vector Regression-Grey Wolf model, with the Support Vector Regression-Wavelet model achieving a correlation coefficient of 0.917, the lowest RMSE of 0.190 ppm, the lowest MAE of 0.115 ppm, and the highest NSE of 0.920 in the validation stage. Overall, the results indicate that using intelligent models based on the Support Vector Regression approach can be an effective approach for managing groundwater quality.