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

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  • Mojtaba Kadkhodazadeh

    (Semnan University)

  • Saeed Farzin

    (Semnan University)

Abstract

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, SO4, HCO3, 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.

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  • Mojtaba Kadkhodazadeh & Saeed Farzin, 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3939-3968, September.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:12:d:10.1007_s11269-021-02913-4
    DOI: 10.1007/s11269-021-02913-4
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    3. Jingjing Xia & Jin Zeng, 2022. "Environmental Factors Assisted the Evaluation of Entropy Water Quality Indices with Efficient Machine Learning Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2045-2060, April.
    4. Mojtaba Kadkhodazadeh & Mahdi Valikhan Anaraki & Amirreza Morshed-Bozorgdel & Saeed Farzin, 2022. "A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods," Sustainability, MDPI, vol. 14(5), pages 1-37, February.
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    6. Zehai Gao & Yang Liu & Nan Li & Kangjie Ma, 2022. "An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2685-2702, June.
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