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Susceptibility mapping of groundwater salinity using machine learning models

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Abstract

Increasing groundwater salinity has recently raised severe environmental and health concerns around the world. Advancement of the novel methods for spatial salinity modeling and prediction would be essential for effective management of the resources and planning mitigation policies. The current research presents the application of machine learning (ML) models in groundwater salinity mapping based on the dichotomous predictions. The groundwater salinity is predicted using the essential factors (i.e., identified by the simulated annealing feature selection methodology) through k-fold cross-validation methodology. Six ML models, namely, flexible discriminant analysis (FDA), mixture discriminant analysis (MAD), boosted regression tree (BRT), multivariate adaptive regression spline (MARS), random forest (RF), support vector machine (SVM), were employed to groundwater salinity mapping. The results of the modeling indicated that the SVM model had superior performance than other models. Variables of soil order, groundwater withdrawal, precipitation, land use, and elevation had the most contribute to groundwater salinity mapping. Results highlighted that the southern parts of the region and some parts in the north, northeast, and west have a high groundwater salinity, in which these areas are mostly matched with soil order of Entisols, bareland areas, and low elevations.

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Authors and Affiliations

  1. Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Amirhosein Mosavi

  2. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Amirhosein Mosavi

  3. Reclamation of Arid and Mountainous Regions Department, Faculty of Natural Resources, University of Tehran, Karaj, Iran

    Farzaneh Sajedi Hosseini

  4. Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran

    Bahram Choubin

  5. Department of Irrigation, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

    Fereshteh Taromideh

  6. Faculty of Geography, University of Tehran, Tehran, Iran

    Marzieh Ghodsi

  7. Department of Water Sciences and Engineering, Imam Khomeini International University, Qazvin, Iran

    Bijan Nazari

  8. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

    Adrienn A. Dineva

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  1. Amirhosein Mosavi
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  3. Bahram Choubin
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  4. Fereshteh Taromideh
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  5. Marzieh Ghodsi
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Contributions

Conceptualization, AM and BC; data preparation, FSH; formal analysis, FSH, BC and AM; investigation, MG, AAD, and BN; methodology, BC, and FSH; project administration, AAD and BC; supervision, AM and BC; validation, FT; visualization, BN and AAD; writing—original draft, MG, BN, and FT; writing—review and editing, AAD.

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Correspondence to Bahram Choubin or Adrienn A. Dineva.

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Mosavi, A., Sajedi Hosseini, F., Choubin, B. et al. Susceptibility mapping of groundwater salinity using machine learning models. Environ Sci Pollut Res 28, 10804–10817 (2021). https://doi.org/10.1007/s11356-020-11319-5

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