Abstract
Water distribution network (WDN) is one of the most essential infrastructures all over the world and ensuring water quality is always a top priority. To this end, water quality sensors are often deployed at multiple points of WDNs for real-time contamination detection and fast contamination source identification (CSI). Specifically, CSI aims to identify the location of the contamination source, together with some other variables such as the starting time and the duration. Such information is important in making an efficient plan to mitigate the contamination event. In the literature, simulation-optimisation methods, which combine simulation tools with evolutionary algorithms (EAs), show great potential in solving CSI problems. However, the application of EAs for CSI is still facing big challenges due to their high computational cost. In this paper, we propose DLGEA, a deep learning guided evolutionary algorithm to improve the efficiency by optimising the search space of EAs. Firstly, based on a large number of simulated contamination events, DLGEA trains a deep neural network (DNN) model to capture the relationship between the time series of sensor data and the contamination source nodes. Secondly, given a contamination event, DLGEA guides the initialisation and optimise the search space of EAs based on the top K contamination nodes predicated by the DNN model. Empirically, based on two benchmark WDNs, we show that DLGEA outperforms the CSI method purely based on EAs in terms of both the average topological distance and the accumulated errors between the predicted and the real contamination events.
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Acknowledgements
This research is supported by the National Natural Science Foundation of China under Grant No. 61873119, the National Key R&D Program of China under Grant No. 2019YFC0810705 and the Science and Technology Innovation Commission of Shenzhen under Grant No. KQJSCX20180322151418232.
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Appendix: Layouts of sensor nodes
Appendix: Layouts of sensor nodes
Figures 6, 7, 8 and 9 show the locations of the 4 sensor nodes in WDN\(_1\) with respect to the four layouts of sensor nodes used in this paper. The blue triangles indicate the locations of the sensor nodes and the numbers next to the blue triangles indicate the ID of the sensor nodes in \(\hbox {WDN}_1\).
The first layout of sensor nodes for \(\mathbf{WDN }_1\) (\( {N}=97\), \( {M}=4\))
The second layout of sensor nodes for \(\mathbf{WDN }_1\) (\( {N}=97\), \( {M}=4\))
The third layout of sensor nodes for \(\mathbf{WDN }_1\) (\( {N}=97\), \( {M}=4\))
The fourth layout of sensor nodes for \(\mathbf{WDN }_1\) (\( {N}=97\), \( {M}=4\))
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Qian, K., Jiang, J., Ding, Y. et al. DLGEA: a deep learning guided evolutionary algorithm for water contamination source identification. Neural Comput & Applic 33, 11889–11903 (2021). https://doi.org/10.1007/s00521-021-05894-y
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DOI: https://doi.org/10.1007/s00521-021-05894-y