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Water Resources Management

, Volume 33, Issue 1, pp 129–140 | Cite as

Drinking Water Source Monitoring Using Early Warning Systems Based on Data Mining Techniques

  • Ianis DelplaEmail author
  • Mihai Florea
  • Manuel J. Rodriguez
Article
  • 59 Downloads

Abstract

Improving drinking water source monitoring is crucial for efficiently managing the drinking water treatment process and ensuring the delivery of safe water. Data mining techniques could prove useful to help forecast source water quality. In this study, two approaches were used to forecast turbidity mean levels and peaks in the main drinking water source of the city of Québec, Canada. Trend analysis was applied for the prediction of significant turbidity events (>99th percentile of data distribution). Artificial neural networks using antecedent moisture conditions as input parameters (all turbidity peaks) served to forecast daily turbidity time series. Results show that trend analyses help anticipate the timing of turbidity peaks ― with differences between the cold season (fall and winter) and the warm season (spring and summer) and mean anticipations between 45 and 85 min and 25 and 45 min, respectively ― and the magnitude of the peak. The artificial neural network model was developed and proven capable of predicting the mean levels of turbidity at the drinking water intake of the investigated catchment. These early warning systems could be applied to source water system forecasting and provide a framework for adjusting drinking water treatment operations.

Keywords

Turbidity Data mining Rainfall Neural networks Trend analysis 

Notes

Acknowledgments

This study was supported by MITACS. We acknowledge Francois Proulx from the City of Québec for providing the rainfall data, and Christian Pelletier and Louis Collin for providing us access to turbidity data from the Québec DWTP.

Compliance with Ethical Standards

Conflict of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, and in the decision to publish the results.

References

  1. APEL (Association pour la protection de l’environnement du lac Saint-Charles et des Marais du Nord) (2014) Suivi des rivières du bassin versant de la rivière Saint-Charles - Campagne 2013, Québec, Canada, 150 ppGoogle Scholar
  2. Bača P (2008) Hysteresis effect in suspended sediment concentration in the Rybárik basin, Slovakia. Hydrol Sci J 53:224–235.  https://doi.org/10.1623/hysj.53.1.224 CrossRefGoogle Scholar
  3. Beaudeau P, Pascal M, Mouly D, Galey C, Thomas O (2011) Health risks associated with drinking water in a context of climate change in France: a review of surveillance requirements. J Water Clim Chang 2:230–246.  https://doi.org/10.2166/wcc.2011.010 CrossRefGoogle Scholar
  4. Davies-Colley RJ, Smith DG (2001) Turbidity, suspended sediment, and water clarity: a review. J Am Water Resour Assoc 37(5):1085–1101.  https://doi.org/10.1111/j.1752-1688.2001.tb03624.x CrossRefGoogle Scholar
  5. Delpla I, Jung a-V, Baures E, Thomas O (2009) Impacts of climate change on surface water quality in relation to drinking water production. Environ Int 35:1225–1233.  https://doi.org/10.1016/j.envint.2009.07.001 CrossRefGoogle Scholar
  6. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37–53Google Scholar
  7. Fernández-Nóvoa D, Mendes R, Dias JM, Sánchez-Arcilla A, Gómez-Gesteira M (2015) Analysis of the influence of river discharge and wind on the Ebro turbid plume using MODIS-aqua and MODIS-Terra data. J Mar Syst 142:40–46CrossRefGoogle Scholar
  8. Gray J, Glysson G (2002) In: Proceedings of the federal interagency workshop on turbidity and other sediment surrogates, April 30–May 2, 2002, vol 1250. U.S. Geological Survey Circular, RenoGoogle Scholar
  9. Iglesias C, Martínez Torres J, García Nieto PJ et al (2014) Turbidity prediction in a River Basin by using artificial neural networks: a case study in northern Spain. Water Resour Manag 28:319–331.  https://doi.org/10.1007/s11269-013-0487-9 CrossRefGoogle Scholar
  10. Jimenez Cisneros BE, Oki T, Arnell NW, Benito G, Cogley JG, Doll P et al (2014) Freshwater resources. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part a: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 229–269Google Scholar
  11. Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms, 2nd edn. Wiley, New York ISBN 978-0-470-89045-5CrossRefGoogle Scholar
  12. Lana-Renault N, Regüés D (2009) Seasonal patterns of suspended sediment transport in an abandoned farmland catchment in the central Spanish Pyrenees. Earth Surf Process Landf 34:1291–1301.  https://doi.org/10.1002/esp.1825 CrossRefGoogle Scholar
  13. Maier HR, Morgan N, Chow CWK (2004) Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environ Model Softw 19:485–494.  https://doi.org/10.1016/S1364-8152(03)00163-4 CrossRefGoogle Scholar
  14. Mas DML, Ahlfeld DP (2007) Comparing artificial neural networks and regression models for predicting faecal coliform concentrations. Hydrol Sci J 52:713–731.  https://doi.org/10.1623/hysj.52.4.713 CrossRefGoogle Scholar
  15. Mather AL, Johnson RL (2015) Event-based prediction of stream turbidity using a combined cluster analysis and classification tree approach. J Hydrol 530:751–761.  https://doi.org/10.1016/j.jhydrol.2015.10.032 CrossRefGoogle Scholar
  16. MDDELCC (Ministère du Développement Durable et la Lutte contre les Changements Climatiques) (2016) Regulation on the quality of drinking water (Règlement Sur la qualité de l'eau potable). Government of Québec, QuébecGoogle Scholar
  17. Morris JK, Knocke WR (1984) Temperature effects on the use of metal-ion coagulants for water treatment. J Am Water Works Assoc 76(3):74–79Google Scholar
  18. Mukundan R, Pierson DC, Schneiderman EM, O’Donnell DM, Pradhanang SM, Zion MS et al (2013) Factors affecting storm event turbidity in a New York City water supply stream. Catena 107:80–88.  https://doi.org/10.1016/j.catena.2013.02.002 CrossRefGoogle Scholar
  19. Palani S, Liong SY, Tkalich P (2008) An ANN application for water quality forecasting. Mar Pollut Bull 56:1586–1597.  https://doi.org/10.1016/j.marpolbul.2008.05.021 CrossRefGoogle Scholar
  20. Rodriguez MJ, Sérodes JB, Levallois P (2004) Behavior of trihalomethanes and haloacetic acids in a drinking water distribution system. Water Res 38(20):4367–4382CrossRefGoogle Scholar
  21. Seeger M, Errea MP, Beguería S, Arnáez J, Martı C, Garcıa-Ruiz JM (2004) Catchment soil moisture and rainfall characteristics as determinant factors for discharge/suspended sediment hysteretic loops in a small headwater catchment in the Spanish pyrenees. J Hydrol 288:299–311.  https://doi.org/10.1016/j.jhydrol.2003.10.012 CrossRefGoogle Scholar
  22. Singh KP, Basant A, Malik A, Jain G (2009) Artificial neural network modeling of the river water quality-a case study. Ecol Model 220:888–895.  https://doi.org/10.1016/j.ecolmodel.2009.01.004 CrossRefGoogle Scholar
  23. Tomperi J, Juuso E, Etelaniemi M, Leiviska K (2014) Drinking water quality monitoring using trend analysis. J Water Health 12:230–241.  https://doi.org/10.2166/wh.2013.075 CrossRefGoogle Scholar
  24. Tomperi J, Juuso E, Leiviskä K (2016) Early warning of changing drinking water quality by trend analysis. J Water Health 14:433–442.  https://doi.org/10.2166/wh.2016.330 CrossRefGoogle Scholar
  25. USEPA (United States Environmental Protection Agency) (1999) Guidance manual for compliance with the interim enhanced surface water treatment rule: turbidity provisions. U.S. Environmental Protection Agency, EPA 815-R-99-010Google Scholar
  26. USEPA (2016) Table for regulated drinking water contaminants. https://www.epa.gov/ground-water-and-drinking-water/table-regulated-drinking-water-contaminants. Accessed 17 Jan 2017
  27. WHO (World Health Organization) (2008) Guidelines for drinking-water quality: incorporating 1st and 2nd addenda. Vol. 1, recommendations, 3rd edn. World Health Organization, GenevaGoogle Scholar
  28. Yang TM, Fan SK, Fan C, Hsu NS (2014) Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis. Environ Monit Assess 186:4925–4934.  https://doi.org/10.1007/s10661-014-3748-z CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Ianis Delpla
    • 1
    Email author
  • Mihai Florea
    • 2
  • Manuel J. Rodriguez
    • 1
  1. 1.École supérieure d’aménagement du territoire et de développement régional (ESAD)Université LavalQuébecCanada
  2. 2.Thales Research & Technology (TRT) CanadaQuébecCanada

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