A probabilistic water quality index for river water quality assessment: A case study

Mohammad Reza Nikoo, Reza Kerachian*, Siamak Malakpour-Estalaki, Seyyed Nasser Bashi-Azghadi, Mohammad Mahdi Azimi-Ghadikolaee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)


Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.

Original languageEnglish
Pages (from-to)465-478
Number of pages14
JournalEnvironmental Monitoring and Assessment
Issue number1-4
Publication statusPublished - Oct 2011


  • Bayesian networks (BNs)
  • Fuzzy inference system (FIS)
  • Probabilistic neural networks (PNNs)
  • Water quality index (WQI)
  • Water quality zoning

ASJC Scopus subject areas

  • Environmental Science(all)
  • Pollution
  • Management, Monitoring, Policy and Law


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