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Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches

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Abstract

Growing population and rapid urbanization are among the major causes of ground water level (GWL) depletion. Modeling GWL is considered as tough task as the GWL variation depends on various complex hydrological and meteorological variables. However, few methodologies have been proposed in literature for modeling GWL. The present research offers a summary of the most common methodologies in GWL forecasting using artificial intelligence (AI), as well as bibliographic assessments of the authors' knowledge and an overview and comparison of the findings. The characteristics and capabilities of modeling methods and the consideration of input data types and time steps have been reviewed in 40 studies published from 2010 to 2020. The reviewed studies succeeded in modeling and predicting the GWL in various regions using the methods proposed by the authors. Trial and error method in certain phases of AI modeling was helpful for testing in special applications for GWL modeling. The reviewed papers provided several partial and overall findings that may provide relevant recommendations to investigators who would like to conduct similar work in GWL modeling. In this report, a variety of new concepts for designing novel approaches and enhancing modeling efficiency are also discussed in the relevant field of analysis. Analyzing modeling methods used in all the reviewed studies it was estimated that the machine learning methods are efficient enough for modeling GWL.

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Acknowledgements

The authors would like to acknowledge the Innovation & Research Management Center (iRMC) of Universiti Tenaga Nasional for its technical and financial support provided under grant code RJO10517844/088 by the Innovation & Research Management Center (iRMC), Universiti Tenaga Nasional.

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Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor, Malaysia

    Ahmedbahaaaldin Ibrahem Ahmed Osman & Ali Najah Ahmed

  2. Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, Malaysia

    Yuk Feng Huang

  3. Department of Civil Engineering, Faculty of Engineering, Universiti Malaya (UM), 50603, Kuala Lumpur, Malaysia

    Pavitra Kumar & Ahmed El-Shafie

  4. Department of Civil Engineering, College of Engineering, Qassim University, Unaizah, Saudi Arabia

    Ahmed H. Birima

  5. National Water and Energy Center, United Arab Emirates University, P.O. Box 15551, Al Ain, UAE

    Mohsen Sherif, Ahmed Sefelnasr & Abdel Azim Ebraheemand

  6. Civil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, 15551, Al Ain, UAE

    Mohsen Sherif

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  1. Ahmedbahaaaldin Ibrahem Ahmed Osman
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Osman, A.I.A., Ahmed, A.N., Huang, Y.F. et al. Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches. Arch Computat Methods Eng 29, 3843–3859 (2022). https://doi.org/10.1007/s11831-022-09715-w

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