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Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches

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Abstract

Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian approaches such as Bayesian generalized linear model (BGLM), Bayesian regularized neural network (BRNN), Bayesian additive regression tree (BART), and Bayesian ridge regression (BRR) were used to model groundwater nitrate contamination in a semiarid region Marvdasht watershed, Fars province, Iran. Eleven groundwater (GW) nitrate conditioning factors have been taken as input parameters for predictive modeling. The results showed that the Bayesian models used in this study were all competent to model groundwater nitrate and the BART model with R2 = 0.83 was more efficient than the other models. The result of variable importance showed that potassium (K) has the highest importance in the models followed by rainfall, altitude, groundwater depth, and distance from the residential area. The results of the study can support the decision-making process to control and reduce the sources of nitrate pollution.

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Availability of data and materials

The data that support the findings of this study are available from the first author [Quoc Bao Pham, phambaoquoc@tdmu.edu.vn], upon reasonable request.

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Authors and Affiliations

  1. UNESCO Chair on Aflaj Studies, Archaeohydrology, University of Nizwa, Nizwa, Oman

    Khalifa M. Alkindi

  2. Department of Geography, Chandidas Mahavidyalaya, Birbhum, WB, 731215, India

    Kaustuv Mukherjee

  3. University Center for Research & Development (UCRD), Chandigarh University, Mohali, 140413, Punjab, India

    Manish Pandey

  4. Department of Civil Engineering, University Institute of Engineering, Chandigarh University, Mohali, 140413, Punjab, India

    Manish Pandey

  5. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 10025, Delhi, India

    Aman Arora

  6. Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, 14115-111, Tehran, Iran

    Saeid Janizadeh

  7. Institute of Applied Technology, Thu Dau Mot University, Binh Duong Province, Vietnam

    Quoc Bao Pham

  8. Ho Chi Minh City University of Technology (HUTECH) 475A, Dien Bien Phu, Ward 25, Binh Thanh District, Ho Chi Minh City, Vietnam

    Duong Tran Anh

  9. Department of Forestry, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, 14115-111, Tehran, Iran

    Kourosh Ahmadi

Authors
  1. Khalifa M. Alkindi
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  2. Kaustuv Mukherjee
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  3. Manish Pandey
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  4. Aman Arora
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  5. Saeid Janizadeh
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  6. Quoc Bao Pham
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  7. Duong Tran Anh
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  8. Kourosh Ahmadi
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Contributions

Khalifa M. Alkindi, Kaustuv Mukherjee: conceptualization, writing—original draft, software, formal analysis, visualization. Manish Pandey, Aman Arora, Saeid Janizadeh: formal analysis, writing—original draft, visualization. Quoc Bao Pham, Kourosh Ahmadi: data curation, writing, review and editing. Duong Tran Anh: supervision, writing, review, editing.

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Correspondence to Duong Tran Anh.

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Alkindi, K.M., Mukherjee, K., Pandey, M. et al. Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches. Environ Sci Pollut Res 29, 20421–20436 (2022). https://doi.org/10.1007/s11356-021-17224-9

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