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Groundwater level estimation using improved deep learning and soft computing methods

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Abstract

Estimating groundwater level (GWL) is an important issue for planning and managing available water resources. This study uses monthly data from 86 observation wells from Mashhad Plain in Iran. A principled hierarchy method was used for the first time. In this regard, the K-means-GA method was used for clustering the considered wells. In each cluster, Principal Component Analysis (PCA) was employed to remove extra-loading observation wells. The presented study examines the accuracy of a new deep learning method, Long Short-Term Memory (LSTM), with Grey Wolf Optimization (GWO) (LSTM-GWO hybrid model) in modeling the GWL. The outcomes of the LSTM-GWO are compared with the enhanced artificial neural network (ANN), hybridized with GWO (ANN-GWO), and standalone ANN in the estimation of GWL. The results revealed that the LSTM-GWO method has a better ability to estimate GWL than the ANN-GWO and ANN methods. In the testing phase, by using the GWO the mean absolute average (MAE) of the ANN-GWO models decreased by at least 30% compared to the standalone ANN models. In addition, for ANN-GWO models the CA parameter which combines the root mean squared error (RMSE), MAE, and R2 decreased by at least 15% in the testing phase compared to the standalone ANN model. The ANN is the least accurate method to estimate monthly GWL. Hybrid model LSTM-GWO almost 23% improved the GWL estimations compared to previous research in terms of coefficient of determination, R2.

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Acknowledgements

Alban Kuriqi is grateful for the Foundation for Science and Technology's support through funding UIDB/04625/2020 from the research unit CERIS.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Faculty of Engineering, Kharazmi University, Tehran, Iran

    Amin Mirboluki & Mojtaba Mehraein

  2. Department of Civil Engineering, University of Applied Sciences, 23562, Lübeck, Germany

    Ozgur Kisi

  3. Department of Civil Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

  4. CERIS, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Alban Kuriqi

  5. Department of Civil Engineering, Tarbiat Modares University, Tehran, Iran

    Reza Barati

Authors
  1. Amin Mirboluki
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  2. Mojtaba Mehraein
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  4. Alban Kuriqi
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  5. Reza Barati
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Contributions

Amin Mirbolouki: Methodology, Software, Writing- Original draft preparation

Mojtaba Mehraein: Supervision, Writing- Original draft preparation

Ozgur Kisi: Reviewing and Editing

Alban Kuriqi: Reviewing and Editing

Reza Barati: data curation

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Correspondence to Mojtaba Mehraein.

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Communicated by: H. Babaie

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Mirboluki, A., Mehraein, M., Kisi, O. et al. Groundwater level estimation using improved deep learning and soft computing methods. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01300-y

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