Advertisement

Water Resources Management

, Volume 29, Issue 11, pp 3957–3970 | Cite as

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

  • Rebecca M. PageEmail author
  • Peter Huggenberger
  • Gunnar Lischeid
Article

Abstract

Groundwater extracted from alluvial aquifers close to rivers is vulnerable to contamination by infiltrating river water. Infiltration is often increased during high discharge events, when the levels of waterborne pathogens are also increased. Water suppliers with low-level treatment thus rely on alternative measures derived from information on system state to manage the resource and maintain drinking-water quality. In this study, a combination of Self-Organizing Maps and Sammon’s Mapping (SOM-SM) was used as a proxy analysis of a multivariate time-series to detect critical system states whereby contamination of the drinking water extraction wells is imminent. Groundwater head, temperature and electrical conductivity time-series from groundwater observation wells were analysed using the SOM-SM method. Independent measurements (spectral absorption coefficient, turbidity, particle density and river stage) were used. This approach can identify critical system states and can be integrated into an adaptive, online, automated groundwater-management process.

Keywords

Groundwater Time-series analysis Self-organizing map Sammon’s mapping Drinking water quality 

Notes

Acknowledgments

The authors thank Stefan Scheidler from the Applied and Environmental Geology Group, University of Basel, Endress+Hauser Metso AG and the Waterworks Reinach and Surroundings (WWRuU) for their support. This work was funded by the Swiss Innovation Promotion Agency CTI (projects number 8999.1 PFIW-IW and 12611.2 PFIW-IW) and the Freiwillige Akademische Gesellschaft Basel.

References

  1. Affolter A, Huggenberger P, Scheidler S, Epting J (2010) Adaptive groundwater management in urban areas: effect of surface water-groundwater interaction using the example of artificial groundwater recharge and in- and exfiltration of the river Birs (Switzerland). Grundwasser 15(3):147–161CrossRefGoogle Scholar
  2. Auckenthaler A, Raso G, Huggenberger P (2002) Particle transport in a karst aquifer: natural and artificial tracer experiments with bacteria, bacteriophages and microspheres. Water Sci Technol 46(3):131–138Google Scholar
  3. Bernataviciene J, Dzemyda G, Kurasova O, Marcinkevicius V (2006) Optimal decisions in combining the SOM with nonlinear projection methods. Eur J Oper Res 173(3):729–745CrossRefGoogle Scholar
  4. Camplani M, Cannas B, Fanni A, Pautasso G, Sias G, Sonato P, Asdex Upgrade Team (2009) Tracking of the plasma states in a nuclear fusion device using SOMs. In: Engineering Applications of Neural Networks. Brown DP, Draganova C, Pimenidis E, Mouratidis H (eds.) Communications in Computer and Information Science 43, 430–437Google Scholar
  5. Cirpka OA, Fienen MN, Hofer M, Hoehn E, Tessarini A, Kipfer R, Kitanidis PK (2007) Analyzing bank filtration by deconvoluting time series of electric conductivity. Ground Water 45(3):318–328CrossRefGoogle Scholar
  6. Corona F, Mulas M, Baratti R, Romagnoli JA (2010) On the topological modeling and analysis of industrial process data using the SOM. Comput Chem Eng 34(12):2022–2032CrossRefGoogle Scholar
  7. Dash RR, Prakash EVPB, Kumar P, Mehrotra I, Sandhu C, Grishek T (2010) River bank filtration in Hardiwar, India: removal of turbidity, organics and bacteria. Hydrogeol J 18(4):973–983CrossRefGoogle Scholar
  8. Dominguez M, Fuertes JJ, Reguera P, Diaz I, Cuadrado AA (2007) Internet-based remote supervision of industrial processes using Self-Organizing maps. Eng Appl Artif Intel 20(6):757–765CrossRefGoogle Scholar
  9. Fuertes JJ, Dominguez M, Reguera P, Prada MA, Diaz I, Cuadrado AA (2010) Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes. Eng Appl Artif Intel 23:8–17CrossRefGoogle Scholar
  10. Iglesias C, Martinez TJ, Garcia Nieto PJ, Alonso Fernandez JR, Diaz Muniz C, Pineiro JI, Taboada J (2014) Turbidity prediction in a river basin by using artificial neural networks: a case study in Northern Spain. Water Resour Manag 28(2):319–331CrossRefGoogle Scholar
  11. Kohonen T (2001) Self-organizing maps. SpringerGoogle Scholar
  12. Kolehmainen M, Ronkko P, Raatikainen A (2003) Monitoring of yeast fermentation by ion mobility spectrometry measurement and data visualisation with self-organizing maps. Anal Chim Acta 484(1):93–100CrossRefGoogle Scholar
  13. Lewandowski J, Lischeid G, Nützmann G (2009) Drivers of water level fluctuations and hydrological exchange between groundwater and surface water at the lowland River Spree (Germany): filed study and statistical analysis. Hydrol Process 23(15):2117–2128CrossRefGoogle Scholar
  14. Lischeid G (2009) Non-linear visualization and analysis of large water quality data sets: a model-free basis for efficient monitoring and risk assessment. Stochastic Envir Res Risk Assess 23(7):977–990CrossRefGoogle Scholar
  15. Mustonen SM, Tissari S, Huikko M, Kolehmainen M, Lehtola MJ, Hirvonen A (2008) Evaluating online data of water quality changes in a pilot drinking water distribution system with multivariate data exploration methods. Water Res 42(10–11):2421–2430CrossRefGoogle Scholar
  16. O’Flynn B, Regan F, Lawlor A, Wallace J, Torres J, O’Mathuna C (2010) Experiences and recommendations in deploying a real-time, water quality monitoring system. Meas Sci Technol 21(124004):10Google Scholar
  17. Page RM, Lischeid G, Epting J, Huggenberger P (2012) Principal component analysis of time series for identifying indicator variables for riverine groundwater extraction management. J Hydrol 432–433:137–144CrossRefGoogle Scholar
  18. Postolache OA, Silva Girão PMB, Dias Pereia JM, Geirinhas Ramos HM (2005) Self-organizing maps application in a remote water quality monitoring system. IEEE Trans Instrum Meas 54(1):322–329CrossRefGoogle Scholar
  19. Pronk M, Goldscheider N, Zopfi J (2007) Particle-size distribution as indicator for faecal bacteria contamination of drinking water from karst springs. Environ Sci Tech 42(24):8400–8405CrossRefGoogle Scholar
  20. Regli C, Rauber M, Huggenberger P (2003) Analysis of aquifer hetereogeneity within a well capture zone, comparison of model data with field experiments: a case study from the river Wiese, Switzerland. Aquat Sci 65(2):111–128Google Scholar
  21. Sammon J (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput C18(5):401–409CrossRefGoogle Scholar
  22. Stadler H, Klock E, Skritek P, Mach RL, Zerobin W, Farnleitner AH (2010) The spectral absorption coefficient at 254 nm as a real-time early warning proxy for detecting faecal pollution events at alpine karst water resources. Water Sci Technol 62(8):1898–1906CrossRefGoogle Scholar
  23. Stefanovic N, Radojevic I, Ostojic A, Comic L, Topuzovi M (2015) Composite Web information system for management of water resources. Water Resour Manag 29:2285–2301Google Scholar
  24. Taylor R, Cronin A, Pedley S, Barker J, Atkinson T (2004) The implications of groundwater velocity variations on microbial transport and wellhead protection—review of field evidence. FEMS Microbiol Ecol 49(1):17–26CrossRefGoogle Scholar
  25. Vesanto J, Alhoniemi E (2000) Clustering of the self-organizing map. IEEE Trans Neural Netw 11(3):586–600CrossRefGoogle Scholar
  26. Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) SOM toolbox for Matlab 5. Helsinki University of Technology, FinlandGoogle Scholar
  27. Zektser IS, Everett LG (2004) Groundwater resources of the world and their use. UNESCO IHP-VI, Series on Groundwater No.6Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Rebecca M. Page
    • 1
    • 2
    Email author
  • Peter Huggenberger
    • 1
  • Gunnar Lischeid
    • 3
  1. 1.Applied and Environmental Geology, Department of Environmental SciencesUniversity of BaselBaselSwitzerland
  2. 2.Endress+Hauser Metso AGReinachSwitzerland
  3. 3.Institute of Landscape HydrologyLeibniz Centre for Agricultural Landscape ResearchMünchebergGermany

Personalised recommendations