Skip to main content

Advertisement

SpringerLink
Log in

Evaluation of a machine-based learning method to estimate the rate of nitrate penetration and groundwater contamination

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Groundwater is among the most important water resources around the globe. However, their contaminations are more difficult, costly, and time-consuming to detect and control than those of surface waters. Therefore, the preparation and deployment of vulnerability maps can help monitor and prevent contamination. In this study, the risk assessment of groundwater nitrate contamination was performed. For this purpose, nitrate concentration data in 130 wells in the Mashhad Plain were used to prepare the contamination mapping. Four machine learning models were used to assess the probability of groundwater contamination, including generalized linear model (GLM), boosted regression trees (BRT), and support vector machine (SVM). The validity of each model was assessed by the AUC characteristic curve. To mix the models, a minimum threshold of 80% was assumed. The results showed that the accuracy of the four models ranged from 80 to 90%, so all models were considered in the combination section. Considering the risk map of groundwater contamination due to vulnerability, contamination, and probability mapping, the western and southwestern areas of the aquifer are at high and very high risks of being exposed to nitrate contamination, which is consistent with the land use map of agricultural and urban areas. According to the results, the ensemble method can provide good efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abdullah T, Ali S, Al-Ansari N, Knutsson S (2018) Assessment of groundwater vulnerability to pollution using VLDA and COP models in Halabja Saidsadiq Iraq. J Civ Eng Architect 12(11):798–809

    Google Scholar 

  • Ahmadi J, Akhondi L, Abbasi H, Khashei-Siuki A, Alimadadi M (2013) Determination of aquifer vulnerability using DRASTIC model and a single parameter sensitivity analysis and acts and omissions case study: Salafchegan-Neyzar Plai. J Water Soil Conserv 20(3):1–25 (In Persian)

    Google Scholar 

  • Aller L, Bennett T, Lehr JH, Petty RJ, Hackett G (1987) DRASTIC: A standardized system for evaluating ground water pollution potential Using Hydrogeologic Settings, EPA 600/2-87-035. Ada, Oklahoma: U.S. Environmental Protection Agency

  • Arezoomand Omidi Langrudi M, Khashei Siuki A, Javadi S, Hashemi SR (2016) Evaluation of vulnerability of aquifers by improved fuzzy drastic method: case study: Aastane Kochesfahan plain in Iran. Ain Shams Engineering Journal 7(1):11–20

    Article  Google Scholar 

  • Asefi M, Radmanesh F, Zarei H (2014) Optimization of DRASTIC model for vulnerability assessment of groundwater resources using analytical hierarchy process (case study: Andimeshk Plain). Irrigation Sciences and Engineering 37(1):55–67 (In Persian)

    Google Scholar 

  • Brown CE (1998) Multiple discriminant analysis. In: Applied multivariate statistics in geohydrology and related sciences. Springer, Berlin, Heidelberg

  • Fijani E, Nadiri AA, Asghari Moghaddam A, Tsai FTC, Dixon B (2013) Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh– Bonab plain aquifer, Iran. J Hydrol 503:89–100

    Article  Google Scholar 

  • Gemitzi A, Petalas C, Tsihrintzis V, Pisinaras V (2006) Assessment of groundwater vulnerability to pollution: a combination of GIS, fuzzy logic and decision making techniques. Environ Geol 49(5):653–673

    Article  Google Scholar 

  • Godebo TR, Merola BR, Kravchenko J, Vengosh A, Mccornick P (2011) Groundwater geochemistry, quality and its impact on human health: field based dental fluorosis assessment in the Main Ethiopian Rift. 4th International Conference on Medical Geology-Italy

  • Gogu RC, Dassargues A (2000) Current trends and future challenges in groundwater vulnerability assessment using overlay and index methods. Environ Geol 39:549–559

    Article  Google Scholar 

  • Golkarian A, Naghibi SA, Kalantar B, Pradhan B (2018) Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS. Environ Monit Assess 190(3):149

    Article  Google Scholar 

  • Haycock N (1990) Handling excess nitrates. Nature 348:291

    Article  Google Scholar 

  • Jhariya DC, Kumar T, Pandey HK, Kumar S, Kumar D, Gautam AK, Baghel VS, Kishore N (2019) Assessment of groundwater vulnerability to pollution by modified DRASTIC model and analytic hierarchy process. Environ Earth Sci 78:610

    Article  Google Scholar 

  • Kapelj S, Loborec J, Kapelj J (2013) Assessment of aquifer intrinsic vulnerability by the SINTACS method. Geologia Croatica 66(2):119–128

    Article  Google Scholar 

  • Khashei Siuki A, Shahidi A, Amirabadizadeh A, Mjafarzadeh A (2018) Performance assessment of ann and svr for downscaling of daily rainfall in dry regions. Iran J Soil Water Res 49(4):781–793

  • Loveless SE, Lewis MA, Bloomfield JP, Stuart ME, Ward R, Davey I, Hart A A. (2019) Method for screening groundwater vulnerability from subsurface hydrocarbon extraction practices. J Env Management, 249

  • Matzeu A, Secci R, Uras G (2017) Methodological approach to assessment of groundwater contamination risk in an agricultural area. Agric Water Manag 184:46–58

    Article  Google Scholar 

  • Monserud RA, Leemans R (1992) Comparing global vegetation maps with the Kappa statistic. Ecol Model 62(4):275–293

    Article  Google Scholar 

  • Nerantzis K, Konstantinos S, Voudouris (2015) Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: modifying the drastic method using quantitative parameters. J Hydrol 525:13–25

    Article  Google Scholar 

  • Neshat A, Pradhan B (2015) An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment. Nat Hazards 76:543–563

    Article  Google Scholar 

  • Neshat A, Pradhan B, Pirasteh S, Shafri HZM (2014) Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area. Iran Environ Earth Sci 71(7):3119–3131

    Article  Google Scholar 

  • Neshat A, Pradhan B, Javadi S (2015) Risk assessment of groundwater pollution using Monte Carlo approach in an agricultural region: an example from Kerman Plain, Iran. Comput Environ Urban Syst 50:66–73

    Article  Google Scholar 

  • Nistor MM (2019) Vulnerability of groundwater resources under climate change in the Pannonian basin. Geo-Spatial Information Science 22(4):345–358

    Article  Google Scholar 

  • Oroji B (2019) Groundwater vulnerability assessment with using GIS in Hamadan–Bahar plain, Iran. Appl Water Sci 9:196

    Article  Google Scholar 

  • Rahman A (2008) A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India. Appl Geogr 28(1):32–53

    Article  Google Scholar 

  • Rahmati O, Melesse AM (2016) Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran. Sci Total Environ 568:1110–1123

    Article  Google Scholar 

  • Sajedi-Hosseini F, Malekian A, Choubin B, Rahmati O, Cipullo S, Coulon F, Pradhan B (2018) A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. Sci Total Environ 644:954–962

    Article  Google Scholar 

  • Sener E, Davraz A (2013) Assessment of groundwater vulnerability based on a modified DRASTIC model, GIS and an analytic hierarchy process (AHP) method: the case of Egirdir Lake Basin (Isparta, Turkey). Hydrogeol J 21(3):701–714

    Article  Google Scholar 

  • Sheykhi Narany T, Ramli MF, Aris AZ, Sulaiman WNA, Fakharian K (2014) Assessment of the potential contamination risk of nitrate in groundwater using indicator kriging (in Amol–Babol Plain, Iran). From Sources to Solution. Springer, Singapore: 273–277

  • Shrestha S, Semkuyu DJ, Pandey VP (2016) Assessment of groundwater vulnerability and risk to pollution in Kathmandu Valley, Nepal. Sci Total Environ 556:23–35

    Article  Google Scholar 

  • Stigter TY, Ribeiro L, Carvalho Dill AMM (2006) Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeol J 14:79–99

    Article  Google Scholar 

  • Van Beynen PE, Niedzielski MA, Bialkowska-Jelinska E, Alsharif K, Matusick J (2012) Comparative study of specific groundwater vulnerability of a karst aquifer in central Florida. Appl Geogr 32:868–877

    Article  Google Scholar 

  • Vias JM, Andreo B, Perles MJ, Carrasco F (2005) A comparative study of four schemes for groundwater vulnerability mapping in a diffuse flow carbonate aquifer under Mediterranean climatic conditions. J Environ Geol 47(4):586–595

    Article  Google Scholar 

  • Xie C, Luo C, Yu X (2011) Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies. Qual Quant 45(3):671–686

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Science and Water Engineering, University of Birjand, Birjand, Iran

    Amir Mohammad Rokhshad, Abbas Khashei Siuki & Mostafa Yaghoobzadeh

Authors
  1. Amir Mohammad Rokhshad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abbas Khashei Siuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mostafa Yaghoobzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abbas Khashei Siuki.

Additional information

Responsible Editor: Amjad Kallel

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rokhshad, A.M., Khashei Siuki, A. & Yaghoobzadeh, M. Evaluation of a machine-based learning method to estimate the rate of nitrate penetration and groundwater contamination. Arab J Geosci 14, 40 (2021). https://doi.org/10.1007/s12517-020-06257-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-020-06257-y

Keywords

Search

Navigation