Drinking Water Source Monitoring Using Early Warning Systems Based on Data Mining Techniques
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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.
KeywordsTurbidity Data mining Rainfall Neural networks Trend analysis
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.
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