A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters

Abstract In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for assessment of water quality parameters. For this purpose, three stations including Ahvaz, Armand, and Gotvand in the Karun river basin have been selected to model electrical conductivity (EC), and total dissolved solids (TDS). First, to prove the superiority of the LSSVM-GBO algorithm, the performance is evaluated with three benchmark datasets (Housing, LVST, Servo). Then, the results of the new hybrid algorithm were compared with those of artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and LSSVM algorithms. Input combination for assessment of water quality parameters EC and TDS consists of Ca+ 2, Cl-1, Mg+ 2, Na+ 1, SO4, HCO3, sodium absorption ratio (SAR), sum cation (Sum.C), sum anion (Sum.A), PH, and Q. The modelling results based on evaluation criteria showed the significant performance of LSSVM-GBO among all benchmark datasets and algorithms. Other results showed that in Ahvaz station, Sum.C, Sum.A, and Na+1 parameters, and in Gotvand and Armand stations, Sum.C, Sum.A, and Cl-1 parameters have the greatest impact on modelling EC and TDS parameters. In the next step, EC and TDS modelling was performed based on the best input combination and the best algorithm in different time delays. Based on the results, the highest accuracy of modelling EC and TDS parameters in Gotvand station was [0] month time delays.