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

• Published in: Water resources management Vol. 35; no. 12; pp. 3939 - 3968
• Main Authors: Kadkhodazadeh, Mojtaba, Farzin, Saeed
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2021; Springer Nature B.V
• Subjects: absorption; administrative management; Algorithms; Anions; Artificial neural networks; Atmospheric Sciences; Benchmarks; Calcium ions; Cations; Civil Engineering; data collection; Datasets; Dissolved solids; Earth and Environmental Science; Earth Sciences; Electrical conductivity; Electrical resistivity; Environment; Fuzzy logic; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology/Water Resources; Mathematical models; Model accuracy; Modelling; Neural networks; Parameters; Performance evaluation; Quality assessment; River basins; Sodium; Sulphates; Support vector machines; Time lag; Timing; Total dissolved solids; water; Water quality; Water quality assessments; watersheds; Benchmark datasets; Novel hybrid model; GBO; LSSVM; Karun river; Water quality parameters
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-021-02913-4

In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for the assessment of water quality (WQ) 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 WQ parameters EC and TDS consists of Ca +2 , Cl −1 , Mg +2 , Na +1 , SO 4 , HCO 3 , sodium absorption ratio (SAR), sum cation (Sum.C), sum anion (Sum.A), pH, and Q. The modeling 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 Armand and Gotvand stations, Sum.C, Sum.A, and Cl −1 parameters have the greatest impact on modeling EC and TDS parameters. Then, EC and TDS modeling was performed based on the best input combination and the best algorithm in different time delays. The highest accuracy of modeling EC and TDS parameters in Gotvand station was and C1 time delay.