Skip to main content
SpringerLink
Log in

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

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

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.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abbasi, S. A. (2002). Water quality indices, state of the art report (pp. 73). Scientific Contribution No. INCOH/SAR-25/2002. Roorkee: INCOH, National Institute of Hydrology.

  • Almeida, C. A., Quintar, S., Gonzalez, P., & Mallea, M. A. (2007). Influence of urbanization and tourist activities on the water quality of the Potrero de los Funes River (San Luis-Argentina). Environmental Monitoring and Assessment, 133, 459–465.

    Article  CAS  Google Scholar 

  • Bashi-Azghadi, S. N., Kerachian, R., Bazargan-Lari, M. R., & Solouki, K. (2010). Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN. Expert Systems with Applications, 37, 7154–7161.

    Article  Google Scholar 

  • Batchelor, C., & Cain, J. (1999). Application of belief networks to water management, studies. Agricultural Water Management, 4(1), 51–57.

    Article  Google Scholar 

  • Brown, R. M., McClleland, N. I., Deininger, R. A., & Tozer, R. (1970). A water quality index—Do we dare? Water and Sewage Works, 117(10), 339–343.

    Google Scholar 

  • Borsuk, M., Reichert, P., & Burkhardt-Holm, P. (2002). A Bayesian belief network for modelling brown trout (Salmo trutta) populations in Switzerland. In A. Rizzoli & A. Jakeman (Eds.), Integrated assessment and decision support, proceedings of first biennial meeting of IEMSS, 24–27 June, Lugano, CH.

  • Buntine, W. (1996). A guide to the literature on learning probabilistic network from data. IEEE Transactions on Knowledge and Data Engineering, 8(2), 195–210.

    Article  Google Scholar 

  • Canadian Council of Ministers of the Environment (CCME) (2001). Canadian water quality index 1.0 technical report and user’s manual. Canadian Environmental Quality Guidelines Water Quality Index Technical Subcommittee. Gatineau, QC, Canada.

  • Canadian Council of Ministers of the Environment (CCME) (2004). An assessment of the application and testing of the water quality index of the Canadian Council of Ministers of the Environment for selected water bodies in Atlantic Canada. National indicators and reporting office. Available at: http://www.ec.gc.ca/soer-ree/ [Accessed August 2010].

  • Cordoba, E. B., Martinez, A. C., & Ferrer, E. V. (2010). Water quality indicators: Comparison of a probabilistic index and a general quality index. The case of the Confederacion Hidrografica del Jucar (Spain). Ecological Indicators, 10, 1049–1054.

    Article  Google Scholar 

  • Cude, C. (2001). Oregon water quality index: A tool for evaluating water quality management effectiveness. Journal of the American Water Resources Association, 37, 125–137.

    Article  CAS  Google Scholar 

  • Debels, P., Figueroa, R., Urrutia, R., Barra, R., & Niell, X. (2005). Evaluation of water quality in the Chilla’n River (Central Chile) using physicochemical parameters and a modified water quality index. Environmental Monitoring and Assessment, 110, 301–322.

    Article  CAS  Google Scholar 

  • Horton, R. K. (1965). An index number system for rating water quality. J Water Poll Cont Fed, 37(3), 300–306.

    Google Scholar 

  • Hülya, B. (2009). Utilization of the water quality index method as a classification tool. Environmental Monitoring and Assessment. doi:10.1007/s10661-009-1035-1.

    Google Scholar 

  • Kannel, P. R., Lee, S., Lee, Y. S., Kanel, S. R., & Khan, S. P. (2007). Application of water quality indices and dissolved oxygen as indicators for river water classification and urban impact assessment. Environmental Monitoring and Assessment, 132, 93–110.

    Article  CAS  Google Scholar 

  • Karamouz, M., Zahraie, B., & Kerachian, R. (2003). Development of a master plan for water pollution control using MCDM techniques: A case study. Water International, IWRA, 28(4), 478–490.

    Article  Google Scholar 

  • Karamouz, M., Tabari, M. M. R., & Kerachian, R. (2007). Application of generic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources. Water International, IWRA, 32(1), 163–176.

    Article  Google Scholar 

  • Liou, S., Lo, S., & Wang, S. (2004). A generalized water quality index for Taiwan. Environmental Monitoring and Assessment, 96, 35–52.

    Article  CAS  Google Scholar 

  • Mahjoobi, J., Etemad-Shahidi, A., & Kazeminezhad, M. H. (2008). Hindcasting of wave parameters using different soft computing methods. Applied Ocean Research, 30, 28–36.

    Article  Google Scholar 

  • Mahjouri, N., & Kerachian, R. (2010). Revising river water quality monitoring networks using discrete entropy theory: The Jajrood river experience. Environmental Monitoring and Assessment, Springer. doi:10.1007/s10661-010-1512-6.

    Google Scholar 

  • Malekmohamadi, I., Bazargan-Lari, M. R., Kerachian, R., Nikoo, M. R. & Fallahnia, M. (2010). Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction. Ocean Engineering, Elsevier. doi:10.1016/j.oceaneng.2010.11.020.

    Google Scholar 

  • Malekmohammadi, B., Kerachian, R., & Zahraie, B. (2009). Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks. Environmental Modelling & Software, 24(12), 1420–1432.

    Article  Google Scholar 

  • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.

    Article  Google Scholar 

  • Marvin, C., Grapentine, L., & Painter, S. (2004). Application of a sediment quality index to the lower Laurentian Great Lakes. Environmental Monitoring and Assessment, 91(1), 1–16.

    Article  CAS  Google Scholar 

  • McCab, B., AbouRizk, S. M., & Goebel, R. (1998). Belief networks for construction performance diagnostics. Journal of Computing in Civil Engineering, 12(2), 93–100.

    Article  Google Scholar 

  • Mesbah, S. M., Kerachian, R., & Nikoo, M. R. (2009). Developing real time operating rules for trading discharge permits in rivers: Application of Bayesian Networks. Environmental Modelling & Software, 24(2), 238–246.

    Article  Google Scholar 

  • Mitchell, M. K., & Stapp, W. B. (2000). Field manual for water quality monitoring (12th edn.). Dubuque: Kendall/Hunt.

    Google Scholar 

  • National Sanitation Foundation (NSF) (2005). Available at: http://www.nsf.org [Accessed August 2010].

  • Ocampo-Duque, W., Ferré-Huguet, N., & Schuhmacher, M. (2006). Assessing water quality in rivers with fuzzy inference systems: A case study. Environment International, 32, 733–742.

    Article  CAS  Google Scholar 

  • Rose-Pehrsson, S. L., Shaffer, R. E., Hart, S. J., Williams, F. W., Gottuk, D. T., Strehlen, B. D., et al. (2000). Multi-criteria fire detection systems using a probabilistic neural network. Sensors and Actuators B, 69, 325–335.

    Article  Google Scholar 

  • Ross T. J. (2004). Fuzzy logic with engineering applications (650 pp.). Wiley.

  • Said, A., Stevens, D., & Selke, G. (2004). An innovative index for evaluating water quality in streams. Environmental Management, 34, 406–414.

    Article  Google Scholar 

  • Semenchenko, V. P., Razlutskii, V. I., & Moroz, M. D. (2009). Comparative analysis of two approaches to the assessment of water quality by biological indices: Case study of Dnieper Tributaries. Water Resources, 36(5), 437–442.

    Article  CAS  Google Scholar 

  • Specht, D. (1990). Probabilistic neural networks. Neural Networks, 3, 109–118.

    Article  Google Scholar 

  • Terrado, M., Borrell, E., Campos, S. D., Barcelo’, D., & Tauler, R. (2010). Surface-water-quality indices for the analysis of data generated by automated sampling networks. Trends in Analytical Chemistry, 29(1), 40–52. doi:10.1016/j.trac.2009.10.001.

    Article  CAS  Google Scholar 

  • Tran, H. D., Perera, B. J. C., & Ng, A. W. M. (2009). Predicting structural deterioration condition of individual storm-water pipes using probabilistic neural networks and multiple logistic regression models. Journal of Water Resources Planning and Management, 135(6), 553–557.

    Article  Google Scholar 

  • Ubeyli, E. D. (2008). Implementing eigenvector methods/probabilistic neural networks for analysis of EEG signals. Neural Networks, 21, 1410–1417.

    Article  Google Scholar 

  • Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics, 3(1), 28–44.

    Article  Google Scholar 

  • Zanaganeh, M., Mousavi, S. J., & Etemad Shahidi, A. F. (2009). A hybrid genetic algorithm—Adaptive network-based fuzzy inference system in prediction of wave parameters. Engineering Applications of Artificial Intelligence, 22, 1194–1202.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Civil Engineering, University of Tehran, Tehran, Iran

    Mohammad Reza Nikoo & Siamak Malakpour-Estalaki

  2. Center of Excellence for Engineering and Management of Civil Infrastructures, School of Civil Engineering, University of Tehran, Tehran, Iran

    Reza Kerachian

  3. School of Civil Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Narmak, Tehran, Iran

    Seyyed Nasser Bashi-Azghadi

  4. Water Engineering Department, Power and Water University of Technology, Tehran, Iran

    Mohammad Mahdi Azimi-Ghadikolaee

Authors
  1. Mohammad Reza Nikoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reza Kerachian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siamak Malakpour-Estalaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seyyed Nasser Bashi-Azghadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Mahdi Azimi-Ghadikolaee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Kerachian.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nikoo, M.R., Kerachian, R., Malakpour-Estalaki, S. et al. A probabilistic water quality index for river water quality assessment: a case study. Environ Monit Assess 181, 465–478 (2011). https://doi.org/10.1007/s10661-010-1842-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-010-1842-4

Keywords

Search

Navigation