A novel method for modeling effluent quality index using Bayesian belief network

  • M. Falah Nezhad
  • M. AbbasiEmail author
  • S. Markarian
Original Paper


Reliable estimation of the effluent quality from a municipal wastewater treatment plant is important for safe discharge and reuse of the treated stream as well as control and monitoring of treatment processes. The quality index is a summative index that can be used for a rapid assessment of water and treated wastewater to rank the quality level. Since there is no quality index for different reuse options of reclaimed wastewater, this study aims to propose a quality index for the treated wastewater focusing on reusing purpose. The significant quality parameters associated with EQI were found using the Delphi method and weighted by analytic hierarchy process decision-making tool. Finally, the Bayesian network analysis was employed to estimate the probability of meeting legal reuse and disposal requirements for EQI based on data collected from south wastewater treatment plant in Tehran city, Iran. The results of Bayesian network analysis were compared with the aggregation method as a widely used method for estimating quality indices. Results revealed Bayesian model had great potential for effluent quality index modeling and significantly increased the precision and the accuracy of estimating the EQI formula. The suggested methodology can provide valuable support also to such practice.


Bayesian networks Effluent quality index Reuse Water quality 



This research used data from south Tehran wastewater treatment plant. We appreciate them for providing the required data for this research. We also thank the experts who participated in filling out our questionnaire and providing insight and expertise that greatly assisted the research.


  1. Aguilera P, Fernández A, Fernández R, Rumí R, Salmerón A (2011) Bayesian networks in environmental modelling. Environ Model Softw 26(12):1376–1388CrossRefGoogle Scholar
  2. Akash BA, Mamlook R, Mohsen MS (1999) Multi-criteria selection of electric power plants using analytical hierarchy process. Electr Power Syst Res 52(1):29–35CrossRefGoogle Scholar
  3. Al-Shujairi S (2013) Develop and apply water quality index to evaluate water quality of Tigris and Euphrates Rivers in Iraq. Int J Mod Eng Res (IJMER) 3(4):2119Google Scholar
  4. American Public Health Association, WPCF (1995) Standard methods for the examination of water and wastewater, 19th edn. APHA, Washington, DCGoogle Scholar
  5. Ames DP, Neilson BT, Stevens DK, Lall U (2005) Using Bayesian networks to model watershed management decisions: an East Canyon Creek case study. J Hydroinf 7(4):267–282CrossRefGoogle Scholar
  6. Borsuk M, Reckhow K (2000) Summary description of the Neuse estuary Bayesian ecological response network (Neu-BERN). WRRI/neuseltm.html. Accessed 26 Dec 2001
  7. Chan FT, Kumar N, Tiwari M, Lau HC, Choy K (2008) Global supplier selection: a fuzzy-AHP approach. Int J Prod Res 46(14):3825–3857CrossRefGoogle Scholar
  8. Chen C, Zhang G, Tarefder R, Ma J, Wei H, Guan H (2015) A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. Accid Anal Prev 80:76–88CrossRefGoogle Scholar
  9. Cheng C-H (1997) Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. Eur J Oper Res 96(2):343–350CrossRefGoogle Scholar
  10. Cowell RG, Dawid P, Lauritzen SL, Spiegelhalter DJ (2006) Probabilistic networks and expert systems: exact computational methods for Bayesian networks. Springer, BerlinGoogle Scholar
  11. Eberhardt III JS, Radano TA, Peterson BE (2016) Application of machine learned Bayesian networks to detection of anomalies in complex systems. Google PatentsGoogle Scholar
  12. Jensen FV (1996) An introduction to Bayesian networks. UCL Press, LondonGoogle Scholar
  13. Karbassi AR, Mir Mohammad Hosseini F, Baghvand A, Nazariha M (2011) Development of water quality index (WQI) for Gorganrood RIVER. Int J Environ Res 5(4):1041–1046Google Scholar
  14. Kaurish FW, Younos T (2007) Developing a standardized water quality index for evaluating surface water quality. JAWRA J Am Water Resour Assoc 43(2):533–545CrossRefGoogle Scholar
  15. Kuikka S, Varis O (1997) Uncertainties of climatic change impacts in Finnish watersheds: a Bayesian network analysis of expert knowledge. Boreal Environ Res 2(1):109–128Google Scholar
  16. Lee D, Bradshaw G (1998) Making monitoring work for managers: thoughts on a conceptual framework for improved monitoring within broad-scale ecosystem management efforts. Interior Columbia Basin Ecosystem Management ProjectGoogle Scholar
  17. Liou S-M, Lo S-L, Wang S-H (2004) A generalized water quality index for Taiwan. Environ Monit Assess 96(1):35–52CrossRefGoogle Scholar
  18. Mansouri SA, Lee H, Aluko O (2015) Multi-objective decision support to enhance environmental sustainability in maritime shipping: a review and future directions. Transp Res Part E Logist Transp Rev 78:3–18CrossRefGoogle Scholar
  19. Najafpoor AA, Vojoudi Z, Dehgani MH, Changani F, Alidadi H (2007) Quality assessment of the Kashaf river in North East of Iran in 1996–2005. J Appl Sci 7(2):253–257CrossRefGoogle Scholar
  20. Nezhad MF, Mehrdadi N, Torabian A, Behboudian S (2016) Artificial neural network modeling of the effluent quality index for municipal wastewater treatment plants using quality variables: south of Tehran wastewater treatment plant. J Water Supply Res Technol Aqua 65(1):18–27Google Scholar
  21. Pearl J (2011) Bayesian networks. Department of Statistics, UCLA, Los AngelesGoogle Scholar
  22. Pesce SF, Wunderlin DA (2000) Use of water quality indices to verify the impact of Córdoba City (Argentina) on Suquı́a River. Water Res 34(11):2915–2926CrossRefGoogle Scholar
  23. Rigosi A, Hanson P, Hamilton DP, Hipsey M, Rusak JA, Bois J, Sparber K, Chorus I, Watkinson AJ, Qin B (2015) Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems. Ecol Appl 25(1):186–199CrossRefGoogle Scholar
  24. Saaty TL (1980) The analytic hierarchy process: planning. McGraw-Hill, New York, p 287Google Scholar
  25. Saaty TL (1990) Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS Publications, PittsburghGoogle Scholar
  26. Shihab K, Al-Chalabi N (2014) An efficient method for assessing water quality based on Bayesian belief networks. Int J Soft Comput 5(2):21CrossRefGoogle Scholar
  27. Stow CA, Roessler C, Borsuk ME, Bowen JD, Reckhow KH (2003) Comparison of estuarine water quality models for total maximum daily load development in Neuse River Estuary. J Water Resour Plan Manag 129(4):307–314CrossRefGoogle Scholar
  28. Varis O (1995) Belief networks for modelling and assessment of environmental change. Environmetrics 6(5):439–444CrossRefGoogle Scholar
  29. Verlicchi P, Masotti L, Galletti A (2011) Wastewater polishing index: a tool for a rapid quality assessment of reclaimed wastewater. Environ Monit Assess 173(1):267–277CrossRefGoogle Scholar
  30. Wang F, Kang S, Du T, Li F, Qiu R (2011) Determination of comprehensive quality index for tomato and its response to different irrigation treatments. Agric Water Manag 98(8):1228–1238CrossRefGoogle Scholar
  31. Yagow G, Shanholtz V (1996) Procedures for indexing monthly NPS pollution loads from agricultural and urban fringe watersheds. In: Proceedings of watershed 96 conferenceGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2018

Authors and Affiliations

  1. 1.Faculty of EnvironmentUniversity of TehranTehranIran
  2. 2.Faculty of Civil, Water and Environmental EngineeringShahid Beheshti UniversityTehranIran

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