Skip to main content
SpringerLink
Log in

Applying the Back-Propagation Neural Network model and fuzzy classification to evaluate the trophic status of a reservoir system

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

Abstract

The trophic state index, and in particular, the Carlson Trophic State Index (CTSI), is critical for evaluating reservoir water quality. Despite its common use in evaluating static water quality, the reliability of the CTSI may decrease when water turbidity is high. Therefore, this study examines the reliability of the CTSI and uses the Back-Propagation Neural Network (BPNN) model to create a new trophic state index. Fuzzy theory, rather than binary logic, is implemented to classify the trophic status into its three grades. The results show that compared to the CTSI with traditional classification, the new index with fuzzy classification can improve trophic status evaluation with high water turbidity. A reliable trophic state index can correctly describe reservoir water quality and allow relevant agencies to address proper water quality management strategies for a reservoir system.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Biggs, B. J. (2000). Eutrophication of streams and rivers: dissolved nutrient-chlorophyll relationships for benthic algae. Journal of the North American Benthological Society, 19(1), 17–31.

    Article  Google Scholar 

  • Brezonik, P. L. (1984). Trophic state indices: rationale for multivariate approaches. Lake and Reservoir Management, 1(1), 441–445.

    Article  Google Scholar 

  • Carlson, R. E. (1977). A trophic state index for lakes. Limnology and Oceanography, 22(2), 361–369.

    Article  CAS  Google Scholar 

  • Chang, S. P., & Chuang, S. M. (2001). Eutrophication study of twenty reservoirs in Taiwan. Water Science & Technology, 44(6), 19–26.

    CAS  Google Scholar 

  • Chang, C. L. & Lin, Y. T. (2014). A water quality monitoring network design using fuzzy theory and multiple criteria analysis. Environmental Monitoring & Assessment, 186(10), 6459–6469.

  • Chiu, S. W. (2003). Grey relational analysis for the synthetic evaluation of reservoir water quality: case studies of Feitsui and Shihmen Reservoir. National Central University, thesis. (In Chinese).

  • Icaga, Y. (2007). Fuzzy evaluation of water quality classification. Ecological Indicators, 7(3), 710–718.

    Article  Google Scholar 

  • Justić, D. (1991). A simple oxygen index for trophic state description. Marine Pollution Bulletin, 22(4), 201–204.

    Article  Google Scholar 

  • Karul, C., Soyupak, S., Cilesiz, A. F., Akbay, N., & Germen, E. (2000). Case studies on the use of neural networks in eutrophication modeling. Ecological Modelling, 134, 145–152.

    Article  CAS  Google Scholar 

  • Kuo, J. T., Wang, Y. Y., & Lung, W. S. (2006). A hybrid neural–genetic algorithm for reservoir water quality management. Water Research, 40(7), 1367–1376.

    Article  CAS  Google Scholar 

  • Kuo, J. T., Hsieh, M. H., Lung, W. S., & She, N. (2007). Using artificial neural network for reservoir eutrophication prediction. Ecological Modelling, 200(1), 171–177.

    Article  Google Scholar 

  • Lambou, V. W., Taylor, W. D., Hern, S. C., & Williams, L. R. (1983). Comparisons of trophic state measurements. Water Research, 17(11), 1619–1626.

    Article  CAS  Google Scholar 

  • Liou, Y. T., & Lo, S. L. (2005). A fuzzy index model for trophic status evaluation of reservoir waters. Water Research, 39(7), 1415–1423.

    Article  CAS  Google Scholar 

  • Lu, R. S., & Lo, S. L. (2002). Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. Water Research, 36(9), 2265–2274.

    Article  CAS  Google Scholar 

  • Lu, R. S., Lo, S. L., & Hu, J. Y. (1999). Analysis of reservoir water quality using fuzzy synthetic evaluation. Stochastic Environmental Research and Risk Assessment, 13(5), 327–336.

    Article  Google Scholar 

  • Palani, S., Liong, S. Y., & Tkalich, P. (2008). An ANN application for water quality forecasting. Marine Pollution Bulletin, 56, 1586–1597.

    Article  CAS  Google Scholar 

  • Sheela, A. M., Letha, J., & Joseph, S. (2011a). Environmental status of a tropical lake system. Environmental Monitoring and Assessment, 180(1–4), 427–449.

    Article  CAS  Google Scholar 

  • Sheela, A. M., Letha, J., Joseph, S., Ramachandran, K. K., & Sanalkumar, S. P. (2011b). Trophic state index of a lake system using IRS (P6-LISS III) satellite imagery. Environmental Monitoring and Assessment, 177(1–4), 575–592.

    Article  CAS  Google Scholar 

  • Sigua, G. C., Williams, M. J., Coleman, S. W., & Starks, R. (2006). Nitrogen and phosphorus status of soils and trophic state of lakes associated with forage-based beef cattle operations in Florida. Journal of Environmental Quality, 35(1), 240–252.

    Article  CAS  Google Scholar 

  • Wu, C. C. (2010). Discussion on the parameters for trophic status evaluation. Taiwan Water Corporation, project report. (in Chinese).

Download references

Acknowledgments

The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 100-2221-E-035-072-MY3.

Author information

Authors and Affiliations

  1. Department of Water Resources Engineering and Conservation, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, Taichung, 40724, Taiwan

    C. L. Chang & H. C. Liu

Authors
  1. C. L. Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. C. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. L. Chang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, C.L., Liu, H.C. Applying the Back-Propagation Neural Network model and fuzzy classification to evaluate the trophic status of a reservoir system. Environ Monit Assess 187, 567 (2015). https://doi.org/10.1007/s10661-015-4513-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-015-4513-7

Keywords

Search

Navigation