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Progressive improvement of DRASTICA and SI models for groundwater vulnerability assessment based on evolutionary algorithms

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Abstract

Groundwater management is essential in water and environmental engineering from both quantity and quality aspects due to the growing urban population. Groundwater vulnerability evaluation models play a prominent role in groundwater resource management, such as the DRASTIC model that has been used successfully in numerous areas. Several studies have focused on improving this model by changing the initial parameters or the rates and weights. The presented study investigated results produced by the DRASTIC model by simultaneously exerting both modifications. For this purpose, two land use-based DRASTIC-derived models, DRASTICA and susceptibility index (SI), were implemented in the Shiraz plain, Iran, a semi-arid region and the primary resource of groundwater currently struggling with groundwater pollution. To develop the novel proposed framework for the progressive improvement of the mentioned rating-based techniques, three main calculation steps for rates and weights are presented: (1) original rates and weights; (2) modified rates by Wilcoxon tests and original weights; and (3) adjusted rates and optimized weights using the genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. To validate the results of this framework applied to the case study, the concentrations of three contamination pollutants, NO3, SO4, and toxic metals, were considered. The results indicated that the DRASTICA model yielded more accurate contamination concentrations for vulnerability evaluations than the SI model. Moreover, both models initially displayed well-matched results for the SO4 concentrations, specifically 0.7 for DRASTICA and 0.58 for SI, respectively. Comparatively, the DRASTICA model showed a higher correlation with NO3 concentrations (0.8) than the SI model (0.6) through improved steps. Furthermore, although both original models demonstrated less correlation with toxic metal concentrations (0.05) compared to SO4 and NO3 concentrations, the DRASTICA and SI models with modified rates and optimized weights exhibited enhanced correlation with toxic metals of about 0.7 and 0.2, respectively.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. This parameter is dimensionless that calculated based on Piscopo (Table 3).

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran

    Masoumeh Zare

  2. Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman

    Mohammad Reza Nikoo & Ghazi Ali Al-Rawas

  3. Department of Environmental Sciences, University of California, Riverside, USA

    Banafsheh Nematollahi

  4. Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia

    Amir H. Gandomi

  5. Department of Soils, Water, and Agricultural Engineering, Sultan Qaboos University, Muscat, Oman

    Malik Al-Wardy

Authors
  1. Masoumeh Zare
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  2. Mohammad Reza Nikoo
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  3. Banafsheh Nematollahi
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  4. Amir H. Gandomi
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  5. Malik Al-Wardy
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  6. Ghazi Ali Al-Rawas
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Contributions

Masoumeh Zare: methodology, software, investigation, writing—original draft, review and editing, visualization, resources. Mohammad Reza Nikoo: project administration, conceptualization, investigations, software, writing—review and editing, resources, supervision. Banafsheh Nematollahi: conceptualization, methodology, validation, writing—review and editing. Amir H. Gandomi: conceptualization, review and editing. Malik Al-Wardy: resources, review and editing. Ghazi Ali Al-Rawas: resources, review and editing.

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Correspondence to Mohammad Reza Nikoo.

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Zare, M., Nikoo, M.R., Nematollahi, B. et al. Progressive improvement of DRASTICA and SI models for groundwater vulnerability assessment based on evolutionary algorithms. Environ Sci Pollut Res 29, 55845–55865 (2022). https://doi.org/10.1007/s11356-022-19620-1

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