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Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters

Author

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  • Mojtaba Kadkhodazadeh

    (Semnan University)

  • Saeed Farzin

    (Semnan University)

Abstract

For the first time, a novel hybrid machine learning model named the least-squares support vector machine-arithmetic optimization algorithm (LSSVM-AOA) was proposed. The performance of LSSVM-AOA was checked on six benchmark data sets (BDSs) to showcase its applicability. After testing the performance of the novel hybrid machine learning model, its performance in electrical conductivity (EC) and total soluble solids (TDS) estimating was developed at six stations in the Karun river basin. For this purpose, effective parameters were selected by the principal component analysis (PCA) method. The results of the technique for order of preference by similarity to ideal solution (TOPSIS) method showed that the LSSVM-AOA has promising results in modeling BDSs and estimating water quality parameters (WQPs) in comparison with classical and hybrid algorithms (artificial neural network (ANN), adaptive neural fuzzy inference system (ANFIS), LSSVM, LSSVM-particle swarm optimization (LSSVM-PSO) and LSSVM-whale optimization algorithm (LSSVM-WOA)). The average values of correlation coefficient (R) in EC and TDS estimates were 0.969 and 0.950, respectively. Eventually, the Monte Carlo method (MCM) showed that the LSSVM-AOA has the lowest uncertainty among other algorithms. Graphical abstract

Suggested Citation

  • Mojtaba Kadkhodazadeh & Saeed Farzin, 2022. "Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3901-3927, August.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:10:d:10.1007_s11269-022-03238-6
    DOI: 10.1007/s11269-022-03238-6
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    References listed on IDEAS

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    1. Quoc Bao Pham & Tao-Chang Yang & Chen-Min Kuo & Hung-Wei Tseng & Pao-Shan Yu, 2021. "Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 847-868, February.
    2. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    3. 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.
    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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