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A hybrid intelligent model for spatial analysis of groundwater potential around Urmia Lake, Iran

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Abstract

The importance of groundwater potential mapping (GWPM) is eminent for the proper management of underpinning resources. This research applies artificial intelligence-based GWPM to the surroundings of Urmia Lake (Iran) which, unfortunately, has been facing severe drying in recent years. An artificial neural network (ANN) is assisted by an optimization algorithm, namely the water-cycle algorithm (WCA) to spatially analyze the relationship between environmental factors and the presence of springs. The proposed model goes through an iterative course to find the optimal contribution of the environment to the GWP within the study area. The results indicated the promising capability of the WCA-ANN for analyzing the spring patterns and also producing reliable GWPMs. Moreover, the WCA outperformed three benchmark algorithms called political optimizer (PO), equilibrium optimization (EO), and electrostatic discharge algorithm (ESDA). Area under the curve values of 0.761, 0.757, 0.801, and 0.767, as well as mean absolute errors of 0.205, 0.203, 0.186, and 0.201, respectively, have been obtained for the PO-ANN, EO-ANN, WCA-ANN, and ESDA-ANN. These outcomes indicate the greater competency of the proposed algorithm. The WCA-ANN and ESDA-ANN algorithms are both therefore recommended as capable methodologies for GWPM. Besides, the maps suggested in this study may help overcoming the Urmia Lake crisis by identifying wealthy groundwater resources in the nearby.

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Authors and Affiliations

  1. Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Gorgan, Golestan, Iran

    Omid Asadi Nalivan

  2. Department of Civil and Environmental Engineering, Politecnico di Milano, Lecco Campus, Lecco, Italy

    Seyed Ali Mousavi Tayebi & Mohammad Mehrabi

  3. Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran

    Hoda Ghasemieh

  4. Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Milan, Italy

    Marco Scaioni

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  1. Omid Asadi Nalivan
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Contributions

Omid Asadi Nalivan: Conceptualization, Data curation, Methodology, Writing- Original draft preparation. Seyed Ali Mousavi Tayebi: Visualization, Investigation, Writing- Reviewing and Editing. Mohammad Mehrabi: Software, Visualization, Writing- Original draft preparation. Hoda Ghasemieh: Writing- Reviewing and Editing. Marco Scaioni: Writing- Reviewing and Editing.

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Correspondence to Mohammad Mehrabi.

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Asadi Nalivan, O., Mousavi Tayebi, S.A., Mehrabi, M. et al. A hybrid intelligent model for spatial analysis of groundwater potential around Urmia Lake, Iran. Stoch Environ Res Risk Assess 37, 1821–1838 (2023). https://doi.org/10.1007/s00477-022-02368-y

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  • DOI: https://doi.org/10.1007/s00477-022-02368-y

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