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A Novel Intelligent Inversion Method of Hydrogeological Parameters Based on the Disturbance-Inspired Equilibrium Optimizer

Author

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  • W. Y. Wang

    (School of Qilu Transportation, Shandong University, Jinan 250061, China
    Geotechnical & Structural Engineering Research Center, Shandong University, Jinan 250061, China)

  • J. T. Kang

    (School of Qilu Transportation, Shandong University, Jinan 250061, China
    Geotechnical & Structural Engineering Research Center, Shandong University, Jinan 250061, China)

  • Kai Li

    (School of Qilu Transportation, Shandong University, Jinan 250061, China)

  • Y. H. Fan

    (Geotechnical & Structural Engineering Research Center, Shandong University, Jinan 250061, China)

  • P. Lin

    (Geotechnical & Structural Engineering Research Center, Shandong University, Jinan 250061, China)

Abstract

Accurate and quick acquisition of hydrogeological parameters is the critical issue for groundwater numerical simulation and sustainability of the water sources. A novel intelligent inversion method of hydrogeological parameter, based on the global optimization algorithm called the disturbance-inspired equilibrium optimizer (DIEO), is developed. Firstly, the mathematical model and the framework of DIEO are reported. Several types of mathematical benchmark functions are used to test the performance of the DIEO. Furthermore, the intelligent inversion of hydrogeological parameters of pumping tests is transformed into the global optimization problem, which can be solved by meta-heuristic algorithms. The objective function for hydrogeological parameter inversion is constructed, and the novel inversion method based on DIEO is finally proposed. To further validate the competitiveness and efficiency of the proposed intelligent inversion method, three types of case studies are carried out. The results show that the proposed intelligent inversion method is reliable for obtaining the hydrogeological parameters accurately and quickly, providing a reference for the inversion of parameters in other fields.

Suggested Citation

  • W. Y. Wang & J. T. Kang & Kai Li & Y. H. Fan & P. Lin, 2022. "A Novel Intelligent Inversion Method of Hydrogeological Parameters Based on the Disturbance-Inspired Equilibrium Optimizer," Sustainability, MDPI, vol. 14(6), pages 1-19, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3267-:d:768696
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    Citations

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    Cited by:

    1. Babak Mohammadi, 2022. "Application of Machine Learning and Remote Sensing in Hydrology," Sustainability, MDPI, vol. 14(13), pages 1-2, June.
    2. Zahra Dashti & Mohammad Nakhaei & Meysam Vadiati & Gholam Hossein Karami & Ozgur Kisi, 2023. "Estimation of Unconfined Aquifer Transmissivity Using a Comparative Study of Machine Learning Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4909-4931, September.

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