Log in

Risk assessment of groundwater pollution with a new methodological framework: application of Dempster–Shafer theory and GIS

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

Managing natural groundwater resources is challenged by nitrate pollution resulting from agricultural activities. This issue is emerging as an important environmental concern that needs to be addressed through effective groundwater management. Groundwater assessment is an important aspect of groundwater management, particularly in arid and semi-arid regions. This study focused on the Kerman Plain, which is exposed to intensive agricultural activities and land exploitation that result in intense land pollution. The effects of nitrate pollution may be controlled by applying specific measures. Dempster–Shafer theory (DST) was applied in this study to develop a new methodology for assessing pollution risk. Applying this theory as a pioneering approach to assessing groundwater pollution risk is the novel component of this research. This approach provides a major advantage by dealing with varying levels of precision related to information. The spatial association between DRASTIC parameters including D (depth of water), R (net recharge), A (aquifer media), S (soil media), T (topography), I (impact of vadose zone) and C (hydraulic conductivity) and underground nitrate occurrence was evaluated by applying bivariate DST to assign mass functions. Dempster’s rule of combination using GIS was then applied to determine a series of combined mass functions for multiple hydrogeological data layers. The uncertainty of system responses was directly addressed by the proposed methodology. Finally, the modified DRASTIC map with the highest validity and accuracy was selected and combined with the damage map. The comparison between nitrate distribution and vulnerability and the risk maps exhibit high similarity between different vulnerability degrees and nitrate concentrations. Long-term planning of preventive measures and associated developments can be aided by the regions with low and very low risks located in the northeast, northwest, and central regions.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Aller L, Bennet T, Lehr JH, Petty RJ, Hackett G (1987) DRASTIC: a standardized system for evaluating groundwater pollution potential using hydrogeological settings

  • An P, Moon WM, Bonham-Carter GF (1992) On knowledge-based approach of integrating remote sensing, geophysical and geological information. In: Geoscience and Remote Sensing Symposium, pp 34–38

  • Anane M, Bouziri L, Limam A, Jellali S (2012) Ranking suitable sites for irrigation with reclaimed water in the Nabeul-Hammamet region (Tunisia) using GIS and AHP-multicriteria decision analysis. Resour Conserv Recy 65:36–46

    Article  Google Scholar 

  • Antonakos AK, Lambrakis NJ (2007) Development and testing of three hybrid methods for the assessment of aquifer vulnerability to nitrates, based on the drastic model, an example from NE Korinthia, Greece. J Hydrol 333:288–304. doi:10.1016/j.jhydrol.2006.08.014

    Article  Google Scholar 

  • Assaf H, Saadeh M (2009) Geostatistical assessment of groundwater nitrate contamination with reflection on DRASTIC vulnerability assessment: the case of the upper litani basin, Lebanon. Water Resour Manage 23:775–796. doi:10.1007/s11269-008-9299-8

    Article  Google Scholar 

  • Baalousha H (2010) Assessment of a groundwater quality monitoring network using vulnerability map** and geostatistics: a case study from Heretaunga Plains, New Zealand. Agric Water Manage 97:240–246. doi:10.1016/j.agwat.2009.09.013

    Article  Google Scholar 

  • Babiker IS, Mohamed MAA, Hiyama T, Kato K (2005) A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara Heights, Gifu Prefecture, Central Japan. Sci Total Environ 345:127–140. doi:10.1016/j.scitotenv.2004.11.005

    Article  Google Scholar 

  • Baraldi P, Zio E (2010) A comparison between probabilistic and dempster-shafer theory approaches to model uncertainty analysis in the performance assessment of radioactive waste repositories. Risk Anal 30:1139–1156. doi:10.1111/j.1539-6924.2010.01416.x

    Article  Google Scholar 

  • Bayat B, Nasseri M, Zahraie B (2014) Identification of long-term annual pattern of meteorological drought based on spatiotemporal methods: evaluation of different geostatistical approaches. Nat Hazards 76:515–541. doi:10.1007/s11069-014-1499-3

    Article  Google Scholar 

  • Carranza EJM (2011) Analysis and map** of geochemical anomalies using logratio-transformed stream sediment data with censored values. J Geochem Explor 110:167–185. doi:10.1016/j.gexplo.2011.05.007

    Article  Google Scholar 

  • Carranza EJM, Castro OT (2006) Predicting Lahar-Inundation Zones: case study in West Mount Pinatubo, Philippines. Nat Hazards 37:331–372. doi:10.1007/s11069-005-6141-y

    Article  Google Scholar 

  • Carranza EJM, Hale M (2002) Spatial association of mineral occurrences and curvilinear geological features. Math Geo l34: 203–221

  • Carranza EJM, Sadeghi M (2010) Predictive map** of prospectivity and quantitative estimation of undiscovered VMS deposits in Skellefte district (Sweden). Ore Geol Rev 38:219–241. doi:10.1016/j.oregeorev.2010.02.003

    Article  Google Scholar 

  • Carranza EJM, Hale M, Faassen C (2008) Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity map**. Ore Geol Rev 33:536–558. doi:10.1016/j.oregeorev.2007.07.001

    Article  Google Scholar 

  • Cayuela L, Golicher JD, Rey JS, Benayas JMR (2006) Classification of a complex landscape using Dempster-Shafer theory of evidence. Int J Remote Sens 27:1951–1971. doi:10.1080/01431160500181788

    Article  Google Scholar 

  • Chen SK, Jang CS, Peng YH (2013) Develo** a probability-based model of aquifer vulnerability in an agricultural region. J Hydrol 486:494–504. doi:10.1016/j.jhydrol.2013.02.019

    Article  Google Scholar 

  • Chica-Olmo M, Luque-Espinar JA, Rodriguez-Galiano V, Pardo-Igúzquiza E, Chica-Rivas L (2014) Categorical Indicator Kriging for assessing the risk of groundwater nitrate pollution: the case of Vega de Granada aquifer (SE Spain). Sci Total Environ 470–471:229–239. doi:10.1016/j.scitotenv.2013.09.077

    Article  Google Scholar 

  • Chowdhury S, Champagne P, McLellan PJ (2009) Uncertainty characterization approaches for risk assessment of DBPs in drinking water: a review. J Environ Manag 90:1680–1691. doi:10.1016/j.jenvman.2008.12.014

    Article  Google Scholar 

  • Cucchi F, Franceschini G, Zini L, Aurighi M (2008) Intrinsic vulnerability assessment of Sette Comuni Plateau aquifer (Veneto Region, Italy). J Environ Manage 88:984–994. doi:10.1016/j.jenvman.2007.05.007

    Article  Google Scholar 

  • Dempster AP (1967) Upper and lower probabilities induced by a multivalued map**. Ann Math Stat 38:325–339

    Article  Google Scholar 

  • Dimitriou E, Karaouzas I, Sarantakos K, Zacharias I, Bogdanos K, Diapoulis A (2008) Groundwater risk assessment at a heavily industrialised catchment and the associated impacts on a peri-urban wetland. J Environ Manag 88:526–538. doi:10.1016/j.jenvman.2007.03.019

    Article  Google Scholar 

  • Feizizadeh B, Blaschke T (2012) GIS-multicriteria decision analysis for landslide susceptibility map**: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65:2105–2128. doi:10.1007/s11069-012-0463-3

    Article  Google Scholar 

  • 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. doi:10.1016/j.jhydrol.2013.08.038

    Article  Google Scholar 

  • Ghosh S, Carranza EJM (2010) Spatial analysis of mutual fault/fracture and slope controls on rocksliding in Darjeeling Himalaya, India. Geomorphology 122:1–24. doi:10.1016/j.geomorph.2010.05.008

    Article  Google Scholar 

  • Gorsevski PV, Jankowski P, Gessler PE (2005) Spatial prediction of landslide hazard using Fuzzy k-means and Dempster–Shafer theory. Trans GIS 9:455–474. doi:10.1111/j.1467-9671.2005.00229.x

    Article  Google Scholar 

  • Helton JC (2008) Uncertainty and sensitivity analysis for models of complex systems computational methods in transport: verification and validation. Springer, Heidelberg, pp 207–228. doi:10.1007/978-3-540-77362-7_9

  • Javadi S, Kavehkar N, Mohammadi K, Khodadadi A, Kahawita R (2011) Calibrating DRASTIC using field measurements, sensitivity analysis and statistical methods to assess groundwater vulnerability. Water Int 36:719–732. doi:10.1080/02508060.2011.610921

    Article  Google Scholar 

  • Jebur MN, Pradhan B, Tehrany MS (2015) Manifestation of LiDAR-derived parameters in the spatial prediction of landslides using novel ensemble evidential belief functions and support vector machine models in GIS. IEEE J Sel Top Appl 8:674–690. doi:10.1109/jstars.2014.2341276

    Google Scholar 

  • Leone A, Ripa MN, Uricchio V, Deák J, Vargay Z (2009) Vulnerability and risk evaluation of agricultural nitrogen pollution for Hungary’s main aquifer using DRASTIC and GLEAMS models. J Environ Manage 90:2969–2978. doi:10.1016/j.jenvman.2007.08.009

    Article  Google Scholar 

  • Malpica JA, Alonso MC, Sanz MA (2007) Dempster-Shafer theory in geographic information systems, a survey. Expert Syst Appl 32:47–55

    Article  Google Scholar 

  • Manos B, Papathanasiou J, Bournaris T, Voudouris K (2010) A multicriteria model for planning agricultural regions within a context of groundwater rational management. J Environ Manag 91:1593–1600

    Article  Google Scholar 

  • Mishra U, Lal R, Liu D, Van Meirvenne M (2010) Predicting the spatial variation of the soil organic carbon pool at a regional scale. Soil Sci Soc Am J 74:906. doi:10.2136/sssaj2009.0158

    Article  Google Scholar 

  • Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility map** at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236

    Article  Google Scholar 

  • Moon WM (1990) Integration of geophysical and geological data using evidential belief function. IEEE T Geosci Remote 28:711–720

    Article  Google Scholar 

  • Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300. doi:10.1016/j.jhydrol.2014.02.053

    Article  Google Scholar 

  • Napolitano P, Fabbri AG (1996) Single-parameter sensitivity analysis for aquifer vulnerability assessment using DRASTIC and SINTACS. In: IAHS publications-series of proceedings and reports-intern Association Hydrological Sciences 235: 559–566

  • Neshat A, Pradhan B (2014) An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment. Nat Hazards 76:543–563. doi:10.1007/s11069-014-1503-y

    Article  Google Scholar 

  • Neshat A, Pradhan B, Pirasteh S, Shafri HZM (2013) Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran. Environ Earth Sci 71:3119–3131. doi:10.1007/s12665-013-2690-7

    Article  Google Scholar 

  • Neshat A, Pradhan B, Dadras M (2014) Groundwater vulnerability assessment using an improved DRASTIC method in GIS. Resour Conserv Recy 86:74–86. doi:10.1016/j.resconrec.2014.02.008

    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. doi:10.1016/j.compenvurbsys.2014.11.004

    Article  Google Scholar 

  • Pacheco FAL, Sanches Fernandes LF (2013) The multivariate statistical structure of DRASTIC model. J Hydrol 476:442–459. doi:10.1016/j.jhydrol.2012.11.020

    Article  Google Scholar 

  • Pavlis M, Cummins E (2014) Assessing the vulnerability of groundwater to pollution in Ireland based on the COST-620 Pan-European approach. J Environ Manag 133:162–173. doi:10.1016/j.jenvman.2013.11.044

    Article  Google Scholar 

  • Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility map** at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73:1019–1042. doi:10.1007/s11069-014-1128-1

    Article  Google Scholar 

  • Saidi S, Bouri S, Ben Dhia H, Anselme B (2011) Assessment of groundwater risk using intrinsic vulnerability and hazard map**: application to Souassi aquifer, Tunisian Sahel. Agric Water Manag 98:1671–1682. doi:10.1016/j.agwat.2011.06.005

    Article  Google Scholar 

  • Sentz K, Ferson S (2002) Combination of Evidence in Dempster-Shafer Theory. doi:10.2172/800792

    Article  Google Scholar 

  • Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton

    Google Scholar 

  • Sorichetta A, Masetti M, Ballabio C, Sterlacchini S, Beretta GP (2011) Reliability of groundwater vulnerability maps obtained through statistical methods. J Environ Manag 92:1215–1224. doi:10.1016/j.jenvman.2010.12.009

    Article  Google Scholar 

  • Stigter TY, Ribeiro L, Carvalho Dill AMM (2006) Application of a groundwater quality index as an assessment and communication tool in agro-environmental policies—two Portuguese case studies. J Hydrol 327:578–591. doi:10.1016/j.jhydrol.2005.12.001

    Article  Google Scholar 

  • Tesoriero AJ, Duff JH, Wolock DM, Spahr NE, Almendinger JE (2009) Identifying pathways and processes affecting nitrate and orthophosphate inputs to streams in agricultural watersheds. J Environ Qual 38:1892–1900. doi:10.2134/jeq2008.0484

    Article  Google Scholar 

  • Thiam AK (2005) An evidential reasoning approach to land degradation evaluation: dempster-shafer theory of evidence. Trans GIS 9:507–520. doi:10.1111/j.1467-9671.2005.00232.x

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. CATENA 96:28–40. doi:10.1016/j.catena.2012.04.001

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2013) Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh Province, Vietnam. Nat Hazards 66(2):707–730. doi:10.1007/s11069-012-0510-0

    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. doi:10.1016/j.apgeog.2011.09.005

    Article  Google Scholar 

  • Wang J, He J, Chen H (2012) Assessment of groundwater contamination risk using hazard quantification, a modified DRASTIC model and groundwater value, Bei**g Plain, China. Sci Total Environ 432:216–226. doi:10.1016/j.scitotenv.2012.06.005

    Article  Google Scholar 

  • Wang K, Zhang C, Li W (2013) Predictive map** of soil total nitrogen at a regional scale: a comparison between geographically weighted regression and cokriging. Appl Geogr 42:73–85. doi:10.1016/j.apgeog.2013.04.002

    Article  Google Scholar 

  • Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:80–83

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Aminreza Neshat or Biswajeet Pradhan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neshat, A., Pradhan, B. Risk assessment of groundwater pollution with a new methodological framework: application of Dempster–Shafer theory and GIS. Nat Hazards 78, 1565–1585 (2015). https://doi.org/10.1007/s11069-015-1788-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-015-1788-5

Keywords

Navigation