IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v29y2015i11p3957-3970.html
   My bibliography  Save this article

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

Author

Listed:
  • Rebecca Page
  • Peter Huggenberger
  • Gunnar Lischeid

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. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Rebecca Page & Peter Huggenberger & Gunnar Lischeid, 2015. "Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 3957-3970, September.
  • Handle: RePEc:spr:waterr:v:29:y:2015:i:11:p:3957-3970
    DOI: 10.1007/s11269-015-1039-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-015-1039-2
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11269-015-1039-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bernataviciene, Jolita & Dzemyda, Gintautas & Kurasova, Olga & Marcinkevicius, Virginijus, 2006. "Optimal decisions in combining the SOM with nonlinear projection methods," European Journal of Operational Research, Elsevier, vol. 173(3), pages 729-745, September.
    2. C. Iglesias & J. Martínez Torres & P. García Nieto & J. Alonso Fernández & C. Díaz Muñiz & J. Piñeiro & J. Taboada, 2014. "Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 319-331, January.
    3. Nenad Stefanovic & Ivana Radojevic & Aleksandar Ostojic & Ljiljana Comic & Marina Topuzovic, 2015. "Composite Web Information System for Management of Water Resources," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2285-2301, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin Quinn & Theodore Lynn & Stephen Jollands & Binesh Nair, 2016. "Domestic Water Charges in Ireland - Issues and Challenges Conveyed through Social Media," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3577-3591, August.
    2. Gebdang B. Ruben & Ke Zhang & Hongjun Bao & Xirong Ma, 2018. "Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 273-283, January.
    3. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    4. Jae Chung Park & Myoung-Jin Um & Young-Il Song & Hyun-Dong Hwang & Mun Mo Kim & Daeryong Park, 2017. "Modeling of Turbidity Variation in Two Reservoirs Connected by a Water Transfer Tunnel in South Korea," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    5. Amanda L. Mather & Richard L. Johnson, 2016. "Forecasting Turbidity during Streamflow Events for Two Mid-Atlantic U.S. Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4899-4912, October.
    6. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    7. Hye Lee & Eun Kim & Seok Park & Jung Choi, 2015. "Effects of Climate Change on the Movement of Turbidity Flow in a Stratified Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 4095-4110, September.
    8. Henrique Vicente & Fábio Borralho & Catarina Couto & Guida Gomes & Victor Alves & José Neves, 2015. "An Adverse Event Reporting and Learning System for Water Sector Based on an Extension of the Eindhoven Classification Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 4927-4943, November.
    9. Patricia Jimeno-Sáez & Javier Senent-Aparicio & José M. Cecilia & Julio Pérez-Sánchez, 2020. "Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)," IJERPH, MDPI, vol. 17(4), pages 1-14, February.
    10. Arturas Kaklauskas & Gintautas Dzemyda & Laura Tupenaite & Ihar Voitau & Olga Kurasova & Jurga Naimaviciene & Yauheni Rassokha & Loreta Kanapeckiene, 2018. "Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment," Energies, MDPI, vol. 11(8), pages 1-20, August.
    11. Onur Genç & Özgür Kişi & Mehmet Ardıçlıoğlu, 2014. "Determination of Mean Velocity and Discharge in Natural Streams Using Neuro-Fuzzy and Neural Network Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2387-2400, July.
    12. Ianis Delpla & Mihai Florea & Manuel J. Rodriguez, 2019. "Drinking Water Source Monitoring Using Early Warning Systems Based on Data Mining Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 129-140, January.
    13. Stefano Casadei & Arnaldo Pierleoni & Michele Bellezza, 2018. "Sustainability of Water Withdrawals in the Tiber River Basin (Central Italy)," Sustainability, MDPI, vol. 10(2), pages 1-18, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:29:y:2015:i:11:p:3957-3970. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.