Skip to main content

Advertisement

SpringerLink
Log in

Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin, northeastern China, from 2011 to 2015

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

Abstract

The analysis of a large number of multidimensional surface water monitoring data for extracting potential information plays an important role in water quality management. In this study, growing hierarchical self-organizing map (GHSOM) was applied to a water quality assessment of the Songhua River Basin in China using 22 water quality parameters monitored monthly from 13 monitoring sites from 2011 to 2015 (14,782 observations). The spatial and temporal features and correlation between the water quality parameters were explored, and the major contaminants were identified. The results showed that the downstream of the Second Songhua River had the worst water quality of the Songhua River Basin. The upstream and midstream of Nenjiang River and the Second Songhua River had the best. The major contaminants of the Songhua River were chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total phosphorus (TP), and fecal coliform (FC). In the Songhua River, the water pollution at downstream has been gradually eased in years. However, FC and biochemical oxygen demand (BOD5) showed growth over time. The component planes showed that three sets of parameters had positive correlations with each other. GHSOM was found to have advantages over self-organizing maps and hierarchical clustering analysis as follows: (1) automatically generating the necessary neurons, (2) intuitively exhibiting the hierarchical inheritance relationship between the original data, and (3) depicting the boundaries of the classification much more clearly. Therefore, the application of GHSOM in water quality assessments, especially with large amounts of monitoring data, enables the extraction of more information and provides strong support for water quality management.

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
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Alahakoon, D., Halgamuge, S. K., & Srinivasan, B. (2000). Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks, 11(3), 601–614.

    Article  CAS  Google Scholar 

  • Almeida, S. F., Elias, C., Ferreira, J., Tornés, E., Puccinelli, C., Delmas, F., et al. (2014). Water quality assessment of rivers using diatom metrics across Mediterranean Europe: a methods intercalibration exercise. Science of the Total Environment, 476, 768–776.

    Article  Google Scholar 

  • Alvarez-Guerra, M., González-Piñuela, C., Andrés, A., Galán, B., & Viguri, J. R. (2008). Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality. Environment International, 34(6), 782–790.

    Article  Google Scholar 

  • Anny, F., Kabir, M., & Bodrud-Doza, M. (2017). Assessment of surface water pollution in urban and industrial areas of Savar Upazila, Bangladesh. Pollution, 3(2), 243–259.

    Google Scholar 

  • Aksela, K., Aksela, M., & Vahala, R. (2009). Leakage detection in a real distribution network using a SOM. Urban Water Journal, 6(4), 279–289.

    Article  Google Scholar 

  • Bizzi, S., Harrison, R. F., & Lerner, D. N. (2009). The Growing Hierarchical Self-Organizing Map (GHSOM) for analysing multi-dimensional stream habitat datasets. In 18th World IMACS/MODSIM Congress.

  • Cao, H., & Xu, D. (2014). Spatial-temporal variation of land-use in Songhua River Basin. Chinese Agricultural Science Bulletin, 30(8), 144–149.

    Google Scholar 

  • Céréghino, R., & Park, Y. S. (2009). Review of the self-organizing map (SOM) approach in water resources: commentary. Environmental Modelling & Software, 24(8), 945–947.

    Article  Google Scholar 

  • Chan, A., & Pampalk, E. (2002). Growing hierarchical self organising map (ghsom) toolbox: visualisations and enhancements. In Neural Information Processing, 2002. ICONIP'02. Proceedings of the 9th International Conference on (Vol. 5, pp. 2537–2541). IEEE.

  • Costa, J. A. F., & de Andrade Netto, M. L. (1999). Automatic data classification by a hierarchy of self-organizing maps. In Systems, Man, and Cybernetics, 1999. IEEE SMC'99 Conference Proceedings. 1999 I.E. International Conference on (Vol. 5, pp. 419–424). IEEE.

  • Daou, C., Nabbout, R., & Kassouf, A. (2016). Spatial and temporal assessment of surface water quality in the Arka River, Akkar, Lebanon. Environmental Monitoring and Assessment, 188(12), 684.

    Article  Google Scholar 

  • De la Hoz, E., de la Hoz, E., Ortiz, A., Ortega, J., & Martínez-Álvarez, A. (2014). Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps. Knowledge-Based Systems, 71, 322–338.

    Article  Google Scholar 

  • Dittenbach, M., Merkl, D., & Rauber, A. (2000). The growing hierarchical self-organizing map. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Vol. 6, pp. 15–19). IEEE.

  • Fritzke, B. (1994). Growing cell structures—a self-organizing network for unsupervised and supervised learning. Neural Networks, 7(9), 1441–1460.

    Article  Google Scholar 

  • Gamble, A., & Babbar-Sebens, M. (2012). On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River Basin, Indiana, USA. Environmental Monitoring and Assessment, 184(2), 845–875.

    Article  Google Scholar 

  • Gao, D., Li, Z., Wen, Z., & Ren, N. (2014). Occurrence and fate of phthalate esters in full-scale domestic wastewater treatment plants and their impact on receiving waters along the Songhua River in China. Chemosphere, 95, 24–32.

    Article  CAS  Google Scholar 

  • González, S. O., Almeida, C. A., Calderón, M., Mallea, M. A., & González, P. (2014). Assessment of the water self-purification capacity on a river affected by organic pollution: application of chemometrics in spatial and temporal variations. Environmental Science and Pollution Research, 21(18), 10583–10593.

    Article  Google Scholar 

  • Griffiths, J. A., Chan, F. K. S., Zhu, F., Wang, V., & Higgitt, D. L. (2017). Reach-scale variation surface water quality in a reticular canal system in the lower Yangtze River Delta region, China. Journal of Environmental Management, 196, 80–90.

    Article  CAS  Google Scholar 

  • Güler, C., Thyne, G. D., McCray, J. E., & Turner, K. A. (2002). Evaluation of graphical and multivariate statistical methods for classification of water chemistry data. Hydrogeology Journal, 10(4), 455–474.

    Article  Google Scholar 

  • Hentati, A., Kawamura, A., Amaguchi, H., & Iseri, Y. (2010). Evaluation of sedimentation vulnerability at small hillside reservoirs in the semi-arid region of Tunisia using the Self-Organizing Map. Geomorphology, 122(1), 56–64.

    Article  Google Scholar 

  • Hu, J., Liu, C., Guo, Q., Yang, J., Okoli, C. P., Lang, Y., et al. (2017). Characteristics, source, and potential ecological risk assessment of polycyclic aromatic hydrocarbons (PAHs) in the Songhua River Basin, Northeast China. Environmental Science and Pollution Research, 1–13.

  • Ippoliti, D., & Zhou, X. (2012). A-GHSOM: An adaptive growing hierarchical self organizing map for network anomaly detection. Journal of Parallel and Distributed Computing, 72(12), 1576–1590.

    Article  Google Scholar 

  • Janahiraman, T. V., & Kong, W. (2011). SOM based segmentation method to identify water region in LANDSAT images. International Journal of Electronics, Computer and Communications Technologies, 2(1), 13–18.

  • Jin, Y. H., Kawamura, A., Park, S. C., Nakagawa, N., Amaguchi, H., & Olsson, J. (2011). Spatiotemporal classification of environmental monitoring data in the Yeongsan River basin, Korea, using self-organizing maps. Journal of Environmental Monitoring, 13(10), 2886–2894.

    Article  CAS  Google Scholar 

  • Juahir, H., Zain, S. M., Yusoff, M. K., Hanidza, T. I. T., Armi, A. S. M., Toriman, M. E., & Mokhtar, M. (2011). Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques. Environmental Monitoring and Assessment, 173(1), 625–641.

    Article  Google Scholar 

  • Kalteh, A. M., Hjorth, P., & Berndtsson, R. (2008). Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application. Environmental Modelling & Software, 23(7), 835–845.

    Article  Google Scholar 

  • Kohonen, T. (1981). Automatic formation of topological maps of patterns in a self-organizing system. In Processing 2nd Scandinavian Conference on Image Analysis (pp. 214–220). Oja, E., Simula, O. (eds.).

  • Koklu, R., Sengorur, B., & Topal, B. (2010). Water quality assessment using multivariate statistical methods—a case study: Melen River System (Turkey). Water Resources Management, 24(5), 959–978.

    Article  Google Scholar 

  • Liu, H., Wang, J., & Zheng, C. (2004). Growing hierarchical self-organizing map models for mental task classification. Shengwu Wuli Xuebao, 21(6), 443–448.

    CAS  Google Scholar 

  • Liu, Y., Weisberg, R. H., & He, R. (2006). Sea surface temperature patterns on the West Florida Shelf using growing hierarchical self-organizing maps. Journal of Atmospheric and Oceanic Technology, 23(2), 325–338.

    Article  Google Scholar 

  • Matharage, S., & Alahakoon, D. (2014). Growing self organising map based exploratory analysis of text data. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 8(4), 639–646.

    Google Scholar 

  • Palomo, E. J., North, J., Elizondo, D., Luque, R. M., & Watson, T. (2012). Application of growing hierarchical SOM for visualisation of network forensics traffic data. Neural Networks, 32, 275–284.

    Article  CAS  Google Scholar 

  • Rauber, A., Merkl, D., & Dittenbach, M. (2002). The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks, 13(6), 1331–1341.

    Article  CAS  Google Scholar 

  • Sarnovsky, M., & Ulbrik, Z. (2013). Cloud-based clustering of text documents using the GHSOM algorithm on the GridGain platform. In Applied Computational Intelligence and Informatics (SACI), 2013 I.E. 8th International Symposium on (pp. 309–313). IEEE.

  • Sengorur, B., Koklu, R., & Ates, A. (2015). Water quality assessment using artificial intelligence techniques: SOM and ANN—a case study of Melen River Turkey. Water Quality, Exposure and Health, 7(4), 469–490.

    Article  Google Scholar 

  • Shen, Y., Cao, H., Tang, M., & Deng, H. (2017). The human threat to river ecosystems at the watershed scale: an ecological security assessment of the Songhua River Basin, Northeast China. Water, 9(3), 219.

    Article  Google Scholar 

  • Shukla, A. K., Ojha, C. S. P., & Garg, R. D. (2017). Application of overall index of pollution (OIP) for the assessment of the surface water quality in the Upper Ganga River Basin, India. In Development of Water Resources in India (pp. 135-149). Springer, Cham.

  • Shrestha, S., & Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: s case study of the Fuji river basin, Japan. Environmental Modelling & Software, 22(4), 464–475.

    Article  Google Scholar 

  • Simeonov, V., Stratis, J. A., Samara, C., Zachariadis, G., Voutsa, D., Anthemidis, A., Sofoniou, M., & Kouimtzis, T. (2003). Assessment of the surface water quality in Northern Greece. Water Research, 37(17), 4119–4124.

    Article  CAS  Google Scholar 

  • Singh, K. P., Malik, A., & Sinha, S. (2005). Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques—a case study. Analytica Chimica Acta, 538(1), 355–374.

    Article  CAS  Google Scholar 

  • Templ, M., Filzmoser, P., & Reimann, C. (2008). Cluster analysis applied to regional geochemical data: problems and possibilities. Applied Geochemistry, 23(8), 2198–2213.

    Article  CAS  Google Scholar 

  • Tsui, I. F., & Wu, C. R. (2012). Variability analysis of Kuroshio intrusion through Luzon Strait using growing hierarchical self-organizing map. Ocean Dynamics, 62(8), 1187–1194.

    Article  Google Scholar 

  • Tyagi, S., Sharma, B., Singh, P., & Dobhal, R. (2013). Water quality assessment in terms of water quality index. American Journal of Water Resources, 1(3), 34–38.

    Google Scholar 

  • Vega, M., Pardo, R., Barrado, E., & Debán, L. (1998). Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Research, 32(12), 3581–3592.

    Article  CAS  Google Scholar 

  • Voutilainen, A., Rahkola-Sorsa, M., Parviainen, J., Huttunen, M. J., & Viljanen, M. (2012). Analysing a large dataset on long-term monitoring of water quality and plankton with the SOM clustering. Knowledge and Management of Aquatic Ecosystems, (406, 406), 04.

  • Wahed, M. S. A., Mohamed, E. A., Wolkersdorfer, C., El-Sayed, M. I., M’nif, A., & Sillanpää, M. (2015). Assessment of water quality in surface waters of the Fayoum watershed, Egypt. Environmental Earth Sciences, 74(2), 1765–1783.

    Article  Google Scholar 

  • Wang, C., Feng, Y., Sun, Q., Zhao, S., Gao, P., & Li, B. L. (2012a). A multimedia fate model to evaluate the fate of PAHs in Songhua River, China. Environmental Pollution, 164, 81–88.

    Article  CAS  Google Scholar 

  • Wang, C., Feng, Y., Zhao, S., & Li, B. L. (2012b). A dynamic contaminant fate model of organic compound: a case study of Nitrobenzene pollution in Songhua River, China. Chemosphere, 88(1), 69–76.

    Article  CAS  Google Scholar 

  • Wei, C., Gao, C., Han, D., Zhao, W., Lin, Q., & Wang, G. (2017). Spatial and temporal variations of water quality in Songhua River from 2006 to 2015: implication for regional ecological health and food safety. Sustainability, 9(9), 1502.

    Article  Google Scholar 

  • Wu, C. R., Hsin, Y. C., Chiang, T. L., Lin, Y. F., & Tsui, I. (2014). Seasonal and interannual changes of the Kuroshio intrusion onto the East China Sea Shelf. Journal of Geophysical Research: Oceans, 119(8), 5039–5051.

    Google Scholar 

  • Wu, M. L., Wang, Y. S., & Gu, J. D. (2015). Assessment for water quality by artificial neural network in Daya Bay, South China Sea. Ecotoxicology, 24(7–8), 1632–1642.

    Article  CAS  Google Scholar 

  • Wu, Z., & Yen, G. G. (2003). A SOM projection technique with the growing structure for visualizing high-dimensional data. International Journal of Neural Systems, 13(05), 353–365.

    Article  Google Scholar 

  • Yidana, S. M., Ophori, D., & Banoeng-Yakubo, B. (2008). A multivariate statistical analysis of surface water chemistry data—the Ankobra Basin, Ghana. Journal of Environmental Management, 86(1), 80–87.

    Article  CAS  Google Scholar 

  • Yin, H. L., & Xu, Z. X. (2008). Comparative study on typical river comprehensive water quality assessment methods [J]. Resources and Environment in the Yangtze Basin, 17(5), 729–733.

    CAS  Google Scholar 

  • Zou, Z. H., Yi, Y., & Sun, J. N. (2006). Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Journal of Environmental Sciences, 18(5), 1020–1023.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank the China National Environmental Monitoring Center for providing the water quality monitoring data.

Funding

This research was a part of Major Science and Technology Program for Water Pollution Control and Treatment of China entitled “National Water Environment Monitoring Intelligent Management Integrated Platform Construction Technology and Operational Demonstration” (2014ZX07502-002) and was also supported by the Fundamental Research Funds for the Central Universities (2652016084).

Author information

Authors and Affiliations

  1. Beijing Key Laboratory of Water Resources & Environmental Engineering, China University of Geosciences (Beijing), Beijing, 100083, People’s Republic of China

    Mingcen Jiang, Yeyao Wang, Qi Yang & Peixuan Cheng

  2. China National Environmental Monitoring Center, Beijing, 100012, People’s Republic of China

    Yeyao Wang & Zhipeng Yao

  3. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, People’s Republic of China

    Fansheng Meng

Authors
  1. Mingcen Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yeyao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fansheng Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhipeng Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Peixuan Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yeyao Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, M., Wang, Y., Yang, Q. et al. Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin, northeastern China, from 2011 to 2015. Environ Monit Assess 190, 260 (2018). https://doi.org/10.1007/s10661-018-6635-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-018-6635-1

Keywords

Search

Navigation