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- W2078522997 abstract "In this work we present the problem of contamination event detection in drinking water distribution networks using real-time learning approaches and a model-based event detection scheme. By using chlorine concentration measurements a contamination event detection algorithm is developed with the aim of detecting the occurrence of contaminant injection into a water tank by monitoring the change of the chlorine concentration. The proposed methodology is comprised of two steps: a) learn in real-time the unknown chlorine reaction dynamics using a Radial-Basis Function network; b) activate a contamination-event detection methodology which uses an adaptive detection threshold. A contamination event detection alarm is activated when the magnitude of the estimation error exceeds the detection threshold. To demonstrate the proposed methodology realistic case study is evaluated with Monte-Carlo simulations of contamination events of different magnitudes occurrence times and environmental characteristics. The results demonstrate the improvement in the contamination event detection performance when using the real-time learning approach." @default.
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- W2078522997 date "2014-07-01" @default.
- W2078522997 modified "2023-09-26" @default.
- W2078522997 title "Contamination event detection in drinking water systems using a real-time learning approach" @default.
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- W2078522997 doi "https://doi.org/10.1109/ijcnn.2014.6889977" @default.
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