Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models

  • Hossein Mojaddadi Rizeei
  • Omer Saud Azeez
  • Biswajeet PradhanEmail author
  • Hayder Hassan Khamees


Groundwater hazard assessments involve many activities dealing with the impacts of pollution on groundwater, such as human health studies and environment modelling. Nitrate contamination is considered a hazard to human health, environment and ecosystem. In groundwater management, the hazard should be assessed before any action can be taken, particularly for groundwater pollution and water quality. Thus, pollution due to the presence of nitrate poses considerable hazard to drinking water, and excessive nutrient loads deteriorate the ecosystem. The parametric IPNOA model is one of the well-known methods used for evaluating nitrate content. However, it cannot predict the effect of soil and land use/land cover (LULC) types on calculations relying on parametric well samples. Therefore, in this study, the parametric model was trained and integrated with the multivariate data-driven model with different levels of information to assess groundwater nitrate contamination in Saladin, Iraq. The IPNOA model was developed with 185 different well samples and contributing parameters. Then, the IPNOA model was integrated with the logistic regression (LR) model to predict the nitrate contamination levels. Geographic information system techniques were also used to assess the spatial prediction of nitrate contamination. High-resolution SPOT-5 satellite images with 5 m spatial resolution were processed by object-based image analysis and support vector machine algorithm to extract LULC. Mapping of potential areas of nitrate contamination was examined using receiver operating characteristic assessment. Results indicated that the optimised LR-IPNOA model was more accurate in determining and analysing the nitrate hazard concentration than the standalone IPNOA model. This method can be easily replicated in other areas that have similar climatic condition. Therefore, stakeholders in planning and environmental decision makers could benefit immensely from the proposed method of this research, which can be potentially used for a sustainable management of urban, industrialised and agricultural sectors.


Nitrate contamination IPNOA GIS Logistic regression Groundwater hazard assessment 


  1. Aal-shamkhi, A. D. S., Mojaddadi, H., Pradhan, B., & Abdullahi, S. (2017). Extraction and modeling of urban sprawl development in Karbala City using VHR satellite imagery. In Spatial Modeling and Assessment of Urban Form (pp. 281–296): Springer.Google Scholar
  2. Abdula, R. A. (2016). Stratigraphy and lithology of Naokelekan Formation in Iraqi Kurdistan-review. The International Journal of Engineering and Science (IJES), 5(8), 45–52.Google Scholar
  3. Abdulkareem, J. H., Sulaiman, W. N. A., Pradhan, B., & Jamil, N. R. (2018a). Long-term hydrologic impact assessment of non-point source pollution measured through land use/land cover (LULC) changes in a tropical complex catchment. Earth Systems and Environment, 2(1), 67–84. Scholar
  4. Abdulkareem, J. H., Pradhan, B., Sulaiman, W. N. A., & Jamil, N. R. (2018b). Quantification of runoff as influenced by morphometric characteristics in a rural complex catchment. Earth Systems and Environment, 2(1), 145–162. Scholar
  5. Abdullahi, S., Pradhan, B., & Mojaddadi, H. (2017). City compactness: assessing the influence of the growth of residential land use. Journal of Urban Technology. 1–26.Google Scholar
  6. AL-Dulaimi, G. A., & Younes, M. K. (2017). Assessment of potable water quality in Baghdad City, Iraq. Air, Soil and Water Research, Scholar
  7. Althuwaynee, O. F., Pradhan, B., & Lee, S. (2012). Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences, 44, 120–135.CrossRefGoogle Scholar
  8. Althuwaynee, O. F., Pradhan, B., Park, H. J., & Lee, J. H. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena, 114, 21–36.CrossRefGoogle Scholar
  9. Alwathaf, Y., & El Mansouri, B. (2011). Assessment of aquifer vulnerability based on GIS and ARCGIS methods: a case study of the Sana’a Basin (Yemen). Journal of Water Resource and Protection, 3(12), 845–855.CrossRefGoogle Scholar
  10. Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65, 15–31.CrossRefGoogle Scholar
  11. Bai, S., Wang, J., Zhang, Z., & Cheng, C. (2012). Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China. Catena, 99, 18–25.CrossRefGoogle Scholar
  12. Benediktsson, J. O. N. A., Swain, P. H., & Ersoy, O. K. (1990). Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 28, 540–552.CrossRefGoogle Scholar
  13. Bone, J., Head, M., Jones, D. T., Barraclough, D., Archer, M., Scheib, C., … Voulvoulis, N. (2010). From chemical risk assessment to environmental quality management: the challenge for soil protection. In: ACS Publications.Google Scholar
  14. Boy Roura, M. (2013). Nitrate groundwater pollution and aquifer vulnerability: the case of the Osona region.Google Scholar
  15. Bui, D. T., Bui, Q.-T., Nguyen, Q.-P., Pradhan, B., Nampak, H., & Trinh, P. T. (2017a). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology, 233, 32–44.CrossRefGoogle Scholar
  16. Bui, D. T., Tuan, T. A., Hoang, N. D., Thanh, N. Q., Nguyen, D. B., Van Liem, N., & Pradhan, B. (2017b). Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides, 14(2), 447–458.CrossRefGoogle Scholar
  17. Cantor, K. P. (1997). Drinking water and cancer. Cancer Causes & Control, 8(3), 292–308.CrossRefGoogle Scholar
  18. Capri, E., Civita, M., Corniello, A., Cusimano, G., De Maio, M., Ducci, D., et al. (2009). Assessment of nitrate contamination risk: the Italian experience. Journal of Geochemical Exploration, 102(2), 71–86.CrossRefGoogle Scholar
  19. Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., Duan, Z., & Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147–160.CrossRefGoogle Scholar
  20. DeSimone, L. A., & Howes, B. L. (1998). Nitrogen transport and transformations in a shallow aquifer receiving wastewater discharge: a mass balance approach. Water Resources Research, 34(2), 271–285.CrossRefGoogle Scholar
  21. Ettazarini, S. (2007). Groundwater potentiality index: a strategically conceived tool for water research in fractured aquifers. Environmental Geology, 52(3), 477–487.CrossRefGoogle Scholar
  22. Ghiglieri, G., Barbieri, G., Vernier, A., Carletti, A., Demurtas, N., Pinna, R., & Pittalis, D. (2009). Potential risks of nitrate pollution in aquifers from agricultural practices in the Nurra region, northwestern Sardinia, Italy. Journal of Hydrology, 379(3–4), 339–350.CrossRefGoogle Scholar
  23. Golkarian, A., Naghibi, S. A., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS. Envrionmental Monitoring & Assessment, 190, 149. Scholar
  24. Green, C. T., Fisher, L. H., & Bekins, B. A. (2008). Nitrogen fluxes through unsaturated zones in five agricultural settings across the United States. Journal of Environmental Quality, 37(3), 1073–1085.CrossRefGoogle Scholar
  25. Gross, E. L. (2008). Ground water susceptibility to elevated nitrate concentrations in South Middleton Township, Cumberland County, Pennsylvania. Shippensburg University of Pennsylvania.Google Scholar
  26. Gumma, M. K., & Pavelic, P. (2013). Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling. Environmental Monitoring and Assessment, 185(4), 3561–3579.CrossRefGoogle Scholar
  27. Gupta, M., & Srivastava, P. K. (2010). Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water International, 35(2), 233–245.CrossRefGoogle Scholar
  28. Into, H. D. I. G. (2011). Nitrate in Drinking Water.Google Scholar
  29. Kordestani, M. D., Naghibi, S. A., Hashemi, H., Ahmadi, K., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeology Journal.
  30. Kowal, A., & Polik, A. (1987). Nitrates in groundwater. In Environmental Technology (pp. 604–609): Springer.Google Scholar
  31. Lake, I. R., Lovett, A. A., Hiscock, K. M., Betson, M., Foley, A., Sünnenberg, G., et al. (2003). Evaluating factors influencing groundwater vulnerability to nitrate pollution: developing the potential of GIS. Journal of Environmental Management, 68(3), 315–328.CrossRefGoogle Scholar
  32. Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50(6), 847–855.CrossRefGoogle Scholar
  33. Liao, L., Green, C. T., Bekins, B. A., & Böhlke, J. (2012). Factors controlling nitrate fluxes in groundwater in agricultural areas. Water Resources Research, 48(6).Google Scholar
  34. Mezaal, M. R., Pradhan, B., Shafri, H., Mojaddadi, H., & Yusoff, Z. (2017). Optimized hierarchical rule-based classification for differentiating shallow and deep-seated landslide using high-resolution LiDAR data. Paper presented at the Global Civil Engineering Conference.Google Scholar
  35. Min, J. H., Yun, S. T., Kim, K., Kim, H. S., & Kim, D. J. (2003). Geologic controls on the chemical behaviour of nitrate in riverside alluvial aquifers, Korea. Hydrological Processes, 17(6), 1197–1211.CrossRefGoogle Scholar
  36. Mishra, N., Khare, D., Gupta, K., & Shukla, R. (2014). Impact of land use change on groundwater—a review. Adv Water Resour Protect, 2, 28–41.Google Scholar
  37. Mojaddadi, H., Habibnejad, M., Solaimani, K., Ahmadi, M., & Hadian-Amri, M. (2009). An investigation of efficiency of outlet runoff assessment. Journal of Applied Sciences, 9(1), 105–112.CrossRefGoogle Scholar
  38. Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. H. B. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 1–23.Google Scholar
  39. Nampak, H., Pradhan, B., & Manap, M. A. (2014). Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, 283–300.CrossRefGoogle Scholar
  40. Neshat, A., & Pradhan, B. (2015). An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment. Natural Hazards, 76(1), 543–563.CrossRefGoogle Scholar
  41. Neshat, A., Pradhan, B., Pirasteh, S., & Shafri, H. Z. M. (2014). Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran. Environmental Earth Sciences, 71(7), 3119–3131.CrossRefGoogle Scholar
  42. 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. Computers, Environment and Urban Systems, 50, 66–73.CrossRefGoogle Scholar
  43. Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences, 37(9), 1264–1276.CrossRefGoogle Scholar
  44. Padovani, L., & Trevisan, M. (2002). I nitrati di origine agricola nelle acque sotterranee: un indice parametrico per l'individuazione di aree vulnerabili: Pitagora.Google Scholar
  45. Pradhan, B. (2009). Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Open Geosciences, 1(1), 120–129.CrossRefGoogle Scholar
  46. Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350–365.CrossRefGoogle Scholar
  47. Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747–759.CrossRefGoogle Scholar
  48. Raaz Maheshwari, B. R., Yadav, A. S. R. K., & Sharma, S. (2012). Nitrate ion contaminated groundwater: its health hazards, preventive & denitrification measures. Bull Env Pharmacol Life Scien Volume, 1, 26–33.Google Scholar
  49. Re, V., Sacchi, E., Kammoun, S., Tringali, C., Trabelsi, R., Zouari, K., & Daniele, S. (2017). Integrated socio-hydrogeological approach to tackle nitrate contamination in groundwater resources. The case of Grombalia Basin (Tunisia). Science of the Total Environment, 593, 664–676.CrossRefGoogle Scholar
  50. Rizeei, H. M., Pradhan, B., & Saharkhiz, M. A. (2018a). Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region. Arabian Journal of Geosciences, 11(3), 53.CrossRefGoogle Scholar
  51. Rizeei, H. M., Shafri, H. Z., Mohamoud, M. A., Pradhan, B., & Kalantar, B. (2018b). Oil palm counting and age estimation from WorldView-3 imagery and LiDAR data using an integrated OBIA height model and regression analysis. Journal of Sensors, 2018.Google Scholar
  52. Sacco, D., Zavattaro, L., & Grignani, C. (2006). Regional-scale predictions of agricultural n losses in an area with a high livestock density. Italian Journal of Agronomy, 1(4), 689–704.CrossRefGoogle Scholar
  53. Sacco, D., Offi, M., De Maio, M., & Grignani, C. (2007). Groundwater nitrate contamination risk assessment: a comparison of parametric systems and simulation modelling. American Journal of Environmental Sciences, 3, 117–125.CrossRefGoogle Scholar
  54. Sener, E., Davraz, A., & Ozcelik, M. (2005). An integration of GIS and remote sensing in groundwater investigations: a case study in Burdur, Turkey. Hydrogeology Journal, 13(5–6), 826–834.CrossRefGoogle Scholar
  55. Shaban, A., Khawlie, M., & Abdallah, C. (2006). Use of remote sensing and GIS to determine recharge potential zones: the case of Occidental Lebanon. Hydrogeology Journal, 14(4), 433–443.CrossRefGoogle Scholar
  56. Shahid, S., Nath, S., & Roy, J. (2000). Groundwater potential modelling in a soft rock area using a GIS. International Journal of Remote Sensing, 21(9), 1919–1924.CrossRefGoogle Scholar
  57. Shahid, S., Nath, S. K., & Maksud Kamal, A. (2002). GIS integration of remote sensing and topographic data using fuzzy logic for ground water assessment in Midnapur District, India. Geocarto International, 17(3), 69–74.CrossRefGoogle Scholar
  58. Spalding, R. F., & Exner, M. E. (1993). Occurrence of nitrate in groundwater—a review. Journal of Environmental Quality, 22(3), 392–402.CrossRefGoogle Scholar
  59. Süzen, M. L., & Doyuran, V. (2004). A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environmental Geology, 45, 665–679.CrossRefGoogle Scholar
  60. Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79.CrossRefGoogle Scholar
  61. Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343.CrossRefGoogle Scholar
  62. Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91–101.CrossRefGoogle Scholar
  63. Umar, Z., Pradhan, B., Ahmad, A., Jebur, M. N., & Tehrany, M. S. (2014). Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena, 118, 124–135.CrossRefGoogle Scholar
  64. Vendrusculo, L., Magalhaes, P., & Vieira, S. (2002). Strategies for soil sampling and multi-layers maps generation through geostatic tools: development of a computational system. Paper presented at the World Congress of Computers in Agriculture and Natural Resources, Proceedings of the 2002 Conference.Google Scholar
  65. Vikas, C., Kushwaha, R., Ahmad, W., Prasannakumar, V., Dhanya, P., & Reghunath, R. (2015). Hydrochemical appraisal and geochemical evolution of groundwater with special reference to nitrate contamination in aquifers of a semi-arid terrain of NW India. Water Quality, Exposure and Health, 7(3), 347–361.CrossRefGoogle Scholar
  66. Wick, K., Heumesser, C., & Schmid, E. (2009). Agriculture and nitrate contamination in Austrian groundwater: an empirical analysis. Rollen der Landwirtschaft in benachteiligten Regionen, 19.Google Scholar
  67. Zhao, Y., De Maio, M., & Suozzi, E. (2013). Assessment of groundwater potential risk by agricultural activities, in North Italy. International Journal of Environmental Science and Development, 4(3), 286.CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and ITUniversity of Technology SydneySydneyAustralia
  2. 2.Department of Civil Engineering, Faculty of EngineeringUniversity Putra MalaysiaSeri KembanganMalaysia

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