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

  • Mingcen Jiang
  • Yeyao Wang
  • Qi Yang
  • Fansheng Meng
  • Zhipeng Yao
  • Peixuan Cheng
Article
  • 91 Downloads

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.

Keywords

Water quality assessment Growing hierarchical self-organizing map Major contaminant identification Spatial feature Temporal feature Water quality management 

Notes

Acknowledgements

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

References

  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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
  5. Aksela, K., Aksela, M., & Vahala, R. (2009). Leakage detection in a real distribution network using a SOM. Urban Water Journal, 6(4), 279–289.CrossRefGoogle Scholar
  6. 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.Google Scholar
  7. 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
  8. 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.CrossRefGoogle Scholar
  9. 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.Google Scholar
  10. 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.Google Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.Google Scholar
  14. Fritzke, B. (1994). Growing cell structures—a self-organizing network for unsupervised and supervised learning. Neural Networks, 7(9), 1441–1460.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. 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.Google Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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.Google Scholar
  24. 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.CrossRefGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. 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.CrossRefGoogle Scholar
  27. 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.).Google Scholar
  28. 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.CrossRefGoogle Scholar
  29. Liu, H., Wang, J., & Zheng, C. (2004). Growing hierarchical self-organizing map models for mental task classification. Shengwu Wuli Xuebao, 21(6), 443–448.Google Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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
  32. 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.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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.Google Scholar
  35. 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.CrossRefGoogle Scholar
  36. 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.CrossRefGoogle Scholar
  37. 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.Google Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. 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.CrossRefGoogle Scholar
  41. Templ, M., Filzmoser, P., & Reimann, C. (2008). Cluster analysis applied to regional geochemical data: problems and possibilities. Applied Geochemistry, 23(8), 2198–2213.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. 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
  44. 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.CrossRefGoogle Scholar
  45. 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.Google Scholar
  46. 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.CrossRefGoogle Scholar
  47. 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.CrossRefGoogle Scholar
  48. 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.CrossRefGoogle Scholar
  49. 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.CrossRefGoogle Scholar
  50. 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
  51. 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.CrossRefGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. 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.CrossRefGoogle Scholar
  54. 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.Google Scholar
  55. 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.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mingcen Jiang
    • 1
  • Yeyao Wang
    • 1
    • 2
  • Qi Yang
    • 1
  • Fansheng Meng
    • 3
  • Zhipeng Yao
    • 2
  • Peixuan Cheng
    • 1
  1. 1.Beijing Key Laboratory of Water Resources & Environmental EngineeringChina University of Geosciences (Beijing)BeijingPeople’s Republic of China
  2. 2.China National Environmental Monitoring CenterBeijingPeople’s Republic of China
  3. 3.State Key Laboratory of Environmental Criteria and Risk AssessmentChinese Research Academy of Environmental SciencesBeijingPeople’s Republic of China

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