IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i10d10.1007_s11269-022-03238-6.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03238-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-022-03238-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefan Tsokov & Milena Lazarova & Adelina Aleksieva-Petrova, 2022. "A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction," Sustainability, MDPI, vol. 14(9), pages 1-38, April.
    2. 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.
    3. 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.
    4. Sufyan Ghani & Sunita Kumari, 2022. "Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(3), pages 2995-3029, April.
    5. Neda Khanmohammadi & Hossein Rezaie & Javad Behmanesh, 2022. "Investigation of Drought Trend on the Basis of the Best Obtained Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1355-1375, March.
    6. Mona Nemati & Mahmoud Mohammad Rezapour Tabari & Seyed Abbas Hosseini & Saman Javadi, 2021. "A Novel Approach Using Hybrid Fuzzy Vertex Method-MATLAB Framework Based on GMS Model for Quantifying Predictive Uncertainty Associated with Groundwater Flow and Transport Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4189-4215, September.
    7. Srishti Gaur & Arnab Bandyopadhyay & Rajendra Singh, 2021. "From Changing Environment to Changing Extremes: Exploring the Future Streamflow and Associated Uncertainties Through Integrated Modelling System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1889-1911, April.
    8. 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.
    9. Mustafa Al-Mukhtar & Aman Srivastava & Leena Khadke & Tariq Al-Musawi & Ahmed Elbeltagi, 2024. "Prediction of Irrigation Water Quality Indices Using Random Committee, Discretization Regression, REPTree, and Additive Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 343-368, January.
    10. Kiyoumars Roushangar & Roghayeh Ghasempour & Farhad Alizadeh, 2022. "Uncertainty Assessment of the Integrated Hybrid Data Processing Techniques for Short to Long Term Drought Forecasting in Different Climate Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 273-296, January.
    11. Jongsung Kim & Myungjin Lee & Heechan Han & Donghyun Kim & Yunghye Bae & Hung Soo Kim, 2022. "Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
    12. Di Nunno, Fabio & Granata, Francesco, 2023. "Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms," Agricultural Water Management, Elsevier, vol. 280(C).
    13. Mojtaba Poursaeid & Amir Houssain Poursaeid & Saeid Shabanlou, 2022. "A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1499-1519, March.
    14. Hadeel E. Khairan & Salah L. Zubaidi & Syed Fawad Raza & Maysoun Hameed & Nadhir Al-Ansari & Hussein Mohammed Ridha, 2023. "Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating," Sustainability, MDPI, vol. 15(19), pages 1-22, September.
    15. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:36:y:2022:i:10:d:10.1007_s11269-022-03238-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.