Skip to main content
SpringerLink
Log in

Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models

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

Abstract

This paper describes the application of artificial neural network models for the prediction of biological oxygen demand (BOD) levels in the Danube River. Eighteen regularly monitored water quality parameters at 17 stations on the river stretch passing through Serbia were used as input variables. The optimization of the model was performed in three consecutive steps: firstly, the spatial influence of a monitoring station was examined; secondly, the monitoring period necessary to reach satisfactory performance was determined; and lastly, correlation analysis was applied to evaluate the relationship among water quality parameters. Root-mean-square error (RMSE) was used to evaluate model performance in the first two steps, whereas in the last step, multiple statistical indicators of performance were utilized. As a result, two optimized models were developed, a general regression neural network model (labeled GRNN-1) that covers the monitoring stations from the Danube inflow to the city of Novi Sad and a GRNN model (labeled GRNN-2) that covers the stations from the city of Novi Sad to the border with Romania. Both models demonstrated good agreement between the predicted and actually observed BOD values.

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
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Antanasijević, D., Pocajt, V., Povrenović, D., Perić-Grujić, A., & Ristić, M. (2013). Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environmental Science and Pollution Research, 20, 9006–9013.

    Article  Google Scholar 

  • Antanasijević, D. Z., Ristić, M. Đ., Perić-Grujić, A. A., & Pocajt, V. V. (2014). Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis. International Journal of Greenhouse Gas Control, 20, 244–253.

    Article  Google Scholar 

  • Awchi, T. (2014). River discharges forecasting in northern Iraq using different ANN techniques. Water Resources Management, 28, 801–814.

    Article  Google Scholar 

  • Basant, N., Gupta, S., Malik, A., & Singh, K. P. (2010). Linear and nonlinear modeling for simultaneous prediction of dissolved oxygen and biochemical oxygen demand of the surface water—a case study. Chemometrics and Intelligent Laboratory Systems, 104, 172–180.

    Article  CAS  Google Scholar 

  • Dehghani, M., Saghafian, B., Nasiri, S. F., Farokhnia, A., & Noori, R. (2014). Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte-Carlo simulation. International Journal of Climatology, 34, 1169–1180.

    Article  Google Scholar 

  • Dogan, E., Sengorur, B., & Koklu, R. (2009). Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. Journal of Environmental Management, 90, 1229–1235.

    Article  CAS  Google Scholar 

  • EEA (2015). European Environment Agency: oxygen consuming substances in rivers—CSI 019. http://www.eea.europa.eu/data-and-maps/indicators/oxygen-consuming-substances-in-rivers/oxygen-consuming-substances-in-rivers-7. Accessed 22 March 2015.

  • El-Shafie, A., Najah, A., Mosad, A. H., & Jahanbani, H. (2014). Optimized neural network prediction model for potential evapotranspiration utilizing ensemble procedure. Water Resources Management, 28, 947–967.

    Article  Google Scholar 

  • Emamgholizadeh, S., Kashi, H., Marofpoor, I., & Zalaghi, E. (2014). Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. International journal of Environmental Science and Technology, 11, 645–656.

    Article  CAS  Google Scholar 

  • Hadzima-Nyarko, M., Rabi, A., & Šperac, M. (2014). Implementation of artificial neural networks in modeling the water-air temperature relationship of the River Drava. Water Resources Management, 28, 1379–1394.

    Article  Google Scholar 

  • Hanna, A. M., Ural, D., & Saygili, G. (2007). Neural network model for liquefaction potential in soil deposits using Turkey and Taiwan earthquake data. Soil Dynamics and Earthquake Engineering, 27, 521–540.

    Article  Google Scholar 

  • Heddam, S. (2014). Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at middle Haddam station, USA. Environmental Monitoring and Assessment, 186, 7837–7848.

    Article  CAS  Google Scholar 

  • ICPDR (2014). International Commission for the Protection of the Danube River: water quality in the Danube River Basin—2012, Transnational Monitoring Network-Yearbook 2012. https://www.icpdr.org/main/publications/tnmn-yearbooks. Accessed 24 February 2015.

  • ICPDR (2015). International Commission for the Protection of the Danube River: the Danube River Basin facts and figures. http://www.icpdr.org/main/danube-basin/river-basin. Accessed 24 February 2015.

  • Iglesias, C., Martínez Torres, J., García Nieto, P. J., Alonso Fernández, J. R., Díaz Muñiz, C., Piñeiro, J. I., & Taboada, J. (2014). Turbidity prediction in a river basin by using artificial neural networks: a case study in northern Spain. Water Resources Management, 28, 319–331.

    Article  Google Scholar 

  • Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Sulaiman, W. N. A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, 67, 251–264.

    Article  Google Scholar 

  • Melesse, A. M., Ahmad, S., McClain, M. E., Wang, X., & Lim, Y. H. (2011). Suspended sediment load prediction of river systems: an artificial neural network approach. Agricultural Water Management, 98, 855–866.

    Article  Google Scholar 

  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE/American Society of Agricultural and Biological Engineers, 50, 885–900.

    Google Scholar 

  • Noori, R., Karbassi, A., Ashrafi, K., Ardestani, M., Mehrdadi, N., & Nabi Bidhendi, G.-R. (2012). Active and online prediction of BOD5 in river systems using reduced-order support vector machine. Environmental Earth Sciences, 67, 141–149.

    Article  CAS  Google Scholar 

  • OGRS (2012). Official Gazette of the Republic of Serbia no. 50/2012: regulation on limit values of pollutants in surface waters, groundwaters and sediments.

  • Prathumratana, L., Sthiannopkao, S., & Woong Kim, K. (2008). The relationship of climatic and hydrological parameters to surface water quality in the lower Mekong River. Environment International, 34, 860–866.

    Article  CAS  Google Scholar 

  • Ranković, V., Radulović, J., Radojević, I., Ostojić, A., & Čomić, L. (2010). Neural network modeling of dissolved oxygen in the Gruža Reservoir, Serbia. Ecological Modelling, 221, 1239–1244.

    Article  Google Scholar 

  • Rene, E. R., & Saidutta, M. B. (2008). Prediction of water quality indices by regression analysis and artificial neural networks. International Journal of Environmental Research, 2, 183–188.

    CAS  Google Scholar 

  • RHMSS (2012). Republic Hydrometeorological Service of Serbia: hydrological yearbook—surface waters, 2011. http://www.hidmet.gov.rs/ciril/hidrologija/povrsinske_godisnjaci.php. Accessed 24 February, 2015.

  • Şengorur, B., Dogan, E., Koklu, R., & Samandar, A. (2006). Dissolved oxygen estimation using artificial neural network for water quality control. Fresenius Environmental Bulletin, 15, 1064–1067.

    Google Scholar 

  • SEPA (2012). Serbian Environmental Protection Agency: Results of analysing surface and groundwater quality in 2011. http://www.sepa.gov.rs/index.php?menu=5000&id=13&akcija=showExternal. Accessed 12 December 2014.

  • Šiljić, A., Antanasijević, D., Perić-Grujić, A., Ristić, M., & Pocajt, V. (2015). Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations. Environmental Science and Pollution Research, 22, 4230–4241.

    Article  Google Scholar 

  • Singh, J., Knapp, H.V., Demissie, M. (2004). Hydrologic modeling of the Iroquois River watershed using HSPF and SWAT. ISWS CR 2004–08. Champaign, IL.: Illinois State Water Survey. http://www.isws.illinois.edu/pubdoc/CR/ISWSCR2004-08.pdf. Accessed 22 March 2015.

  • Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the riverwater quality—a case study. Ecological Modelling, 220, 888–895.

    Article  CAS  Google Scholar 

  • Soyupak, S., Karaer, F., Hasan Gürbüz, H., Kivrak, E., Sentürk, E., & Yazici, A. (2003). A neural network-based approach for calculating dissolved oxygen profiles in reservoirs. Neural Computing Applications, 12, 166–172.

    Article  Google Scholar 

  • Specht, D. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2, 568–576.

    Article  CAS  Google Scholar 

  • Verma, A. K., & Singh, T. N. (2013). Prediction of water quality from simple field parameters. Environmental Earth Sciences, 69, 821–829.

    Article  CAS  Google Scholar 

  • Wen, X., Fang, J., Diao, M., & Zhang, C. (2013). Artificial neural network modeling of dissolved oxygen in the Heihe River, northwestern China. Environmental Monitoring and Assessment, 185, 4361–4371.

    Article  CAS  Google Scholar 

  • Willmott, C. J., Robeson, S. M., & Matsuura, K. (2012). Short communication—a refined index of model performance. International Journal of Climatology, 32, 2088–2094.

    Article  Google Scholar 

  • Zhao, Y., Nan, J., Cui, F., & Guo, L. (2007). Water quality forecast through application of BP neural network at Yuqiao reservoir. Journal of Zhejiang University Science A, 8, 1482–1487.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors are grateful to the Ministry of Education, Science, and Technological Development of the Republic of Serbia, Project No. 172007, for the financial support.

Author information

Authors and Affiliations

  1. Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, Belgrade, 11120, Serbia

    Aleksandra N. Šiljić Tomić, Mirjana Đ. Ristić, Aleksandra A. Perić-Grujić & Viktor V. Pocajt

  2. Innovation Center of the Faculty of Technology and Metallurgy, Karnegijeva 4, Belgrade, 11120, Serbia

    Davor Z. Antanasijević

Authors
  1. Aleksandra N. Šiljić Tomić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davor Z. Antanasijević
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mirjana Đ. Ristić
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aleksandra A. Perić-Grujić
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Viktor V. Pocajt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Davor Z. Antanasijević.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Šiljić Tomić, A.N., Antanasijević, D.Z., Ristić, M.Đ. et al. Modeling the BOD of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models. Environ Monit Assess 188, 300 (2016). https://doi.org/10.1007/s10661-016-5308-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-016-5308-1

Keywords

Search

Navigation