Application of Bayesian networks in a hierarchical structure for environmental risk assessment: a case study of the Gabric Dam, Iran

  • Bahram Malekmohammadi
  • Negar Tayebzadeh Moghadam


Environmental risk assessment (ERA) is a commonly used, effective tool applied to reduce adverse effects of environmental risk factors. In this study, ERA was investigated using the Bayesian network (BN) model based on a hierarchical structure of variables in an influence diagram (ID). ID facilitated ranking of the different alternatives under uncertainty that were then used to evaluate comparisons of the different risk factors. BN was used to present a new model for ERA applicable to complicated development projects such as dam construction. The methodology was applied to the Gabric Dam, in southern Iran. The main environmental risk factors in the region, presented by the Gabric Dam, were identified based on the Delphi technique and specific features of the study area. These included the following: flood, water pollution, earthquake, changes in land use, erosion and sedimentation, effects on the population, and ecosensitivity. These risk factors were then categorized based on results from the output decision node of the BN, including expected utility values for risk factors in the decision node. ERA was performed for the Gabric Dam using the analytical hierarchy process (AHP) method to compare results of BN modeling with those of conventional methods. Results determined that a BN-based hierarchical structure to ERA present acceptable and reasonable risk assessment prioritization in proposing suitable solutions to reduce environmental risks and can be used as a powerful decision support system for evaluating environmental risks.


Bayesian networks (BNs) Environmental risk assessment (ERA) Risk factors Risk ranking Influence diagram (ID) Gabric Dam, Iran 



The authors would like to thank the two reviewers for their constructive comments on correction and improvement of the manuscript. The authors would like to express their gratitude to the technical experts of the Regional Water Company of Hormozgan Province and Lar Consulting Engineers for providing data and technical assistance.


  1. Adnan, D. (2009). Modeling and reasoning with Bayesian networks. New York: Cambridge University Press.Google Scholar
  2. Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian networks and participatory modelling in water resource management. Environmental Modelling and Software, 22, 1075–1088.CrossRefGoogle Scholar
  3. Chen, S., Chen, B., & Fath, B. D. (2013). Ecological risk assessment on the system scale: a review of state-of-the-art models and future perspectives. Ecological Modelling, 250, 25–33.CrossRefGoogle Scholar
  4. Dalkey, N. C., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458–467.CrossRefGoogle Scholar
  5. Faridah-Hanum, I., Latiff, A., Hakeem, K. R., & Ozturk, M. (2014). Mangrove ecosystems of Asia: status, challenges and management strategies. New York: Springer.CrossRefGoogle Scholar
  6. Fenton, N. E., & Neil, M. (2012). Risk assessment and decision analysis with Bayesian networks. Boca Raton: CRC Press.Google Scholar
  7. Hamby, D. M. (1994). A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment, 32(2), 135–154.CrossRefGoogle Scholar
  8. Heller, S. (2006). Managing industrial risk having a tested and proven system to prevent and assess risk. Journal of Hazardous Materials, 130(1–2), 58–63.CrossRefGoogle Scholar
  9. Hormozgan regional water company. (2012). Specifications of dams province.Google Scholar
  10. Howes, A. L., Maron, M., & McAlpine, C. A. (2010). Bayesian networks and adaptive management of wildlife habitat. Conservation Biology, 24(4), 974–983.CrossRefGoogle Scholar
  11. Karami, A., & Johansson, R. (2014). Utilization of multi attribute decision making techniques to integrate automatic and manual ranking of options. Journal of Information Science and Engineering, 30(2), 519–534.Google Scholar
  12. Kazantzi, V., Gerogiannis, V. C., & Anthopoulos, L. (2013). Multi-criteria decision making for supplier selection in biomass supply networks for bioenergy production in outsourcing management for supply chain operations and logistics service (pp. 313–343). Hershey: IGI Global.Google Scholar
  13. Keshtkar, A. R., Salajegheh, A., Sadoddin, A., & Allan, M. G. (2013). Application of Bayesian networks for sustainability assessment in catchment modeling and management (case study: the Hablehrood river catchment). Ecological Modelling, 268, 48–54.CrossRefGoogle Scholar
  14. Korb, K. B., & Nicholson, A. E. (2004). Bayesian artificial intelligence. Boca Raton: Chapman and Hall/CRC Press.Google Scholar
  15. Landuyt, D., Broekx, S., D'hondt, R., Engelen, G., Aertsens, J., & Goethals, P. (2013). A review of Bayesian belief networks in ecosystem service modelling. Environmental Modelling and Software, 46, 1–11.CrossRefGoogle Scholar
  16. Lar Consulting Engineers. (2012). Updating report and completing studies of environmental impact assessment of the Gabric Dam. Tehran: Lar Consulting Engineers.Google Scholar
  17. Lein, J. K. (2002). Integrated environmental planning. Oxford, Madlen, Victoria, Berlin: Black Well Science Ltd..CrossRefGoogle Scholar
  18. Malekmohammadi, B., & Rahimi Blouchi, L. (2014). Ecological risk assessment of wetland ecosystems using multi criteria decision making and geographic information system. Ecological Indicators, 41, 133–144.CrossRefGoogle Scholar
  19. Malekmohammadi, B., Kerachian, R., & Zahraie, B. (2009). Developing monthly operating rules for a cascade system of reservoirs: application of Bayesian network. Environmental Modelling and Software, 24(12), 1420–1432.CrossRefGoogle Scholar
  20. McCann, R. K., Marcot, B. G., & Ellis, R. (2007). Bayesian belief networks: application in ecology and natural resource management. Canadian Journal of Forest Research, 36, 3053–3062.CrossRefGoogle Scholar
  21. Morales-Nápoles, O., Delgado-Hernández, D. J., De-León-Escobedo, D., & Arteaga-Arcos, J. C. (2014). A continuous Bayesian network for earth dams’ risk assessment: methodology and quantification. Structure and Infrastructure Engineering, 10(5), 589–603.CrossRefGoogle Scholar
  22. Nagarajan, R., Marco, S., & Sophie, L. (2013). Bayesian networks in R with applications in systems biology. Berlin: Springer.CrossRefGoogle Scholar
  23. Newton, A. C. (2010). Use of a Bayesian network for Red Listing under uncertainty. Environmental Modelling & Software, 25(1), 15–23.CrossRefGoogle Scholar
  24. Nyberg, J. B., Marcot, B. G., & Sulyma, R. (2006). Using Bayesian belief networks in adaptive management. Canadian Journal of Forest Research, 36(12), 3104–3116.CrossRefGoogle Scholar
  25. Pang, A. P., & Sun, T. (2014). Bayesian networks for environmental flow decision-making and an application in the Yellow River estuary, China. Hydrology and Earth System Sciences, 18(5), 1641–1651.CrossRefGoogle Scholar
  26. Peng, M., & Zhang, L. M. (2012). Analysis of human risks due to dam-break floods—part 1: a new model based on Bayesian networks. Natural Hazards, 64(1), 903–933.CrossRefGoogle Scholar
  27. Pollino, C. A., & Hart, B. T. (2008). Developing Bayesian network models within a risk assessment framework. Australia: International Congress on Environmental Modelling and Software.Google Scholar
  28. Pollino, C. A., White, A. K., & Hart, B. T. (2007a). Examination of conflicts and improved strategies for the management of an endangered eucalypt species using Bayesian networks. Ecological Modelling, 201, 37–59.CrossRefGoogle Scholar
  29. Pollino, C. A., Woodberry, O., & Nicholson, A. K. (2007b). Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental Modelling & Software, 22(8), 1140–1152.CrossRefGoogle Scholar
  30. Pourret, O., Naim, P., & Marcot, B. (2008). Bayesian networks: a practical guide to applications. Hoboken: John Wiley & Sons, Ltd.CrossRefGoogle Scholar
  31. Reckhow, K. H. (2010). Bayesian networks for the assessment of the effect of urbanization on stream macroinvertebrates. In: Proceedings of the 43rd Hawaii International Conference on System Sciences.Google Scholar
  32. Schubert, M., Hoj, N. P., Ragnoy, A., & Buvik, H. (2012). Risk assessment of road tunnels using Bayesian networks. Procedia - Social and Behavioral Sciences, 48, 2697–2706.CrossRefGoogle Scholar
  33. Shin, J., Ajmal, M., Yoo, J., & Kim, T. (2016). A Bayesian network-based probabilistic framework for drought forecasting and outlook. Advances in Meteorology, 2016, 9472605 10 pages.Google Scholar
  34. Smith, M. (2006). Dam risk analysis using Bayesian networks. Engineering Conferences International Proceedings Geohazards.Google Scholar
  35. Sujak, A., Kusz, A., Rymarz, & Kitowski, I. (2016). Environmental bioindication studies by Bayesian network with use of grey heron as model species. Environmental Modeling and Assessment, 22, 103–113. Scholar
  36. Sun, Z., & Müller, D. (2012). A framework for modeling payments for ecosystem services with agent based models, bayesian belief networks and opinion dynamics models. Environmental Modelling & Software, 45, 15–28.CrossRefGoogle Scholar
  37. Watthayu, W., & Peng, Y. (2004). A Bayesian network based framework for multi-criteria decision making, In: Proceedings of the 17 th International Conference on Multiple Criteria Decision Analysis. Whistler, British Columbia, Canada, pp. 6–11.Google Scholar
  38. Wu, W., Yang, C., Chang, J., Château, P., & Chang, Y. (2015). Risk assessment by integrating interpretive structural modeling and Bayesian network, case of offshore pipeline project. Reliability Engineering & System Safety, 142, 515–524.CrossRefGoogle Scholar
  39. Xu, Y., Zhang, L. M., & Jiac, J. S. (2011). Diagnosis of embankment dam distresses using Bayesian networks. Part II. Diagnosis of a specific distressed dam. Canadian Geotechnical Journal, 48(11), 1645–1657.CrossRefGoogle Scholar
  40. Zhang, L., Wu, X., Qin, Y., Skibniewski, M. J., & Liu, W. (2016). Towards a fuzzy Bayesian network based approach for safety risk analysis of tunnel-induced pipeline damage. Risk Analysis, 36(2), 278–301.CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Bahram Malekmohammadi
    • 1
  • Negar Tayebzadeh Moghadam
    • 1
  1. 1.Graduate Faculty of EnvironmentUniversity of TehranTehranIran

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