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

Author

Listed:
  • Ianis Delpla

    (Université Laval)

  • Mihai Florea

    (Thales Research & Technology (TRT) Canada)

  • Manuel J. Rodriguez

    (Université Laval)

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.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:1:d:10.1007_s11269-018-2092-4
    DOI: 10.1007/s11269-018-2092-4
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    References listed on IDEAS

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    1. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    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.
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