28
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A hybrid machine learning approach for enhanced anomaly detection in drinking water quality

&

References

  • Muharemi, F., Logofătu, D. and Leon, F., 2019, Machine learning approaches for anomaly detection of water quality on a real-world data set. Journal of Information and Telecommunication 3(3), 294–307. doi: 10.1080/24751839.2019.1565653
  • Jansi Rani, S.V., Ramakrishnan, A.M. and Rishivardhan, K., 2022, Improving water quality assessment through anomaly detection using hybrid convolutional neural network approach. Global NEST Journal 24(1), 1–8.
  • Leigh, C., Alsibai, O., Hyndman, R.J., Kandanaarachchi, S., King, O.C., McGree, J.M., Neelamraju, C., Strauss, J., Talagala, P.D., Turner, R.D., Mengersen, K. and Peterson, E.E., 2019, A framework for automated anomaly detection in high frequency water-quality data from in situ sensors. Science of the Total Environment 664, 885–898. doi: 10.1016/j.scitotenv.2019.02.085
  • Yang, Z., Liu, Y., Hou, D., Feng, T., Wei, Y., Zhang, J., Huang, P. and Zhang, G., 2014, Water quality event detection based on Multivariate empirical mode decomposition. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (San Diego, USA: IEEE), 05–08 October, pp. 2663–2668.
  • Candelieri, A., 2017, Clustering and support vector regression for water demand forecasting and anomaly detection. Water 9(3), 224. doi: 10.3390/w9030224
  • Fehst, V., La, H.C., Nghiem, T.D., Mayer, B.E., Englert, P. and Fiebig, K.H., 2018, Automatic vs. Manual feature engineering for anomaly detection of drinking-water quality. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion (Kyoto, Japan: Association for Computing Machinery), 15–19 July, pp. 5–6.
  • Raciti, M., Cucurull, J. and Nadjm-Tehrani, S., 2012, Anomaly detection in water management systems. In: J. Lopez, R. Setola and S.D. Wolthusen (Eds) Critical infrastructure protection (Berlin Heidelberg: Springer), pp. 98–119.
  • Hill, D.J. and Minsker, B.S., 2010, Anomaly detection in streaming environmental sensor data: A data-driven modeling approach. Environmental Modelling & Software 25(9), 1014–1022. doi: 10.1016/j.envsoft.2009.08.010
  • Eggimann, S., Mutzner, L., Wani, O., Schneider, M.Y., Spuhler, D., Moy de Vitry, M., Beutler, P. and Maurer, M., 2017, The potential of knowing more: A review of data-driven urban water management. Environmental Science & Technology 51(5), 2538–2553. doi: 10.1021/acs.est.6b04267
  • Chen, X., Feng, F., Wu, J. and Liu, W., 2018, Anomaly detection for drinking water quality via deep biLSTM ensemble. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion (Kyoto, Japan: Association for Computing Machinery), 15–19 July, pp. 3–4.
  • Tan, F.H.S., Park, J.R., Jung, K., Lee, J.S. and Kang, D.K., 2020, Cascade of one class classifiers for water level anomaly detection. Electronics 9(6), 1012. doi: 10.3390/electronics9061012
  • Russo, S., Disch, A., Blumensaat, F. and Villez, K., 2019, Anomaly detection using deep autoencoders for in-situ wastewater systems monitoring data. Proceedings of the 10th IWA Symposium on Systems Analysis and Integrated Assessment (Watermatex 2019), Copenhagen, Denmark, 1–4 September.
  • Mokua, N., Maina, C.W. and Kiragu, H., 2021, Anomaly detection for raw water quality—A comparative analysis of the local outlier factor algorithm and the random forest algorithms. International Journal of Computer Applications 174(26), 49–54. doi: 10.5120/ijca2021921196
  • Tien, C.W., Huang, T.Y., Chen, P.C. and Wang, J.H., 2021, Using autoencoders for anomaly detection and transfer learning in IoT. Computers 10(7), 88. doi: 10.3390/computers10070088
  • Nicholaus, I.T., Park, J.R., Jung, K., Lee, J.S. and Kang, D.K., 2021, Anomaly detection of water level using deep autoencoder. Sensors 21(19), 6679. doi: 10.3390/s21196679
  • Finke, T., Krämer, M., Morandini, A., Mück, A. and Oleksiyuk, I., 2021, Autoencoders for unsupervised anomaly detection in high energy physics. Journal of High Energy Physics 2021(6), 1–32. doi: 10.1007/JHEP06(2021)161
  • Zhang, J., Zhu, X., Yue, Y. and Wong, P.W., 2017, A real-time anomaly detection algorithm/or water quality data using dual time-moving windows. In: 2017 Seventh International Conference on Innovative Computing Technology (INTECH), (Luton, UK: IEEE), 16–18 August, pp. 36–41.
  • Kulanuwat, L., Chantrapornchai, C., Maleewong, M., Wongchaisuwat, P., Wimala, S., Sarinnapakorn, K. and Boonya-Aroonnet, S., 2021, Anomaly detection using a Sliding Window Technique and data imputation with machine learning for hydrological time series. Water 13(13), 1862. doi: 10.3390/w13131862
  • Liu, J., Wang, P., Jiang, D., Nan, J. and Zhu, W., 2020, An integrated data-driven framework for surface water quality anomaly detection and early warning. Journal of Cleaner Production 251, 119145. doi: 10.1016/j.jclepro.2019.119145
  • Tavakoli, N., Siami-Namini, S., Khanghah, M.A., Soltani, F.M. and Namin, A.S., 2020, An autoencoder-based deep learning approach for clustering time series data. SN Applied Sciences 2(5), 1–25. doi: 10.1007/s42452-020-2584-8
  • Huang, G.B., Zhu, Q.Y. and Siew, C.K., 2006, Extreme learning machine: Theory and applications. Neurocomputing 70(1–3), 489–501. doi: 10.1016/j.neucom.2005.12.126
  • Avola, D., Bernardi, M., Cinque, L., Foresti, G.L. and Massaroni, C., 2020, Online separation of handwriting from freehand drawing using extreme learning machines. Multimedia Tools and Applications 79(7–8), 4463–4481. doi: 10.1007/s11042-019-7196-1
  • Huang, G., Huang, G.B., Song, S. and You, K., 2015, Trends in extreme learning machines: A review. Neural Networks 61, 32–48. doi: 10.1016/j.neunet.2014.10.001
  • Wang, B., Hua, Q., Zhang, H., Tan, X., Nan, Y., Chen, R. and Shu, X., 2022, Research on anomaly detection and real-time reliability evaluation with the log of cloud platform. Alexandria Engineering Journal 61(9), 7183–7193. doi: 10.1016/j.aej.2021.12.061
  • Yu, Y., Lv, P., Tong, X. and Dong, J., 2020, Anomaly detection in high-dimensional data based on autoregressive flow. In: Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020 (Jeju, South Korea: Springer-Verlag), 24–27 September, pp. 125–140.
  • Muneer, A., Taib, S.M., Fati, S.M., Balogun, A.O. and Aziz, I.A., 2021, A hybrid deep learning-based unsupervised anomaly detection in high dimensional data. Computers Materials & Continua 70(3), 5363–5381. doi: 10.32604/cmc.2022.021113
  • Inoue, J., Yamagata, Y., Chen, Y., Poskitt, C. M., and Sun, J, 2017, Anomaly detection for a water treatment system using unsupervised machine learning. In: 2017 IEEE international conference on data mining workshops (ICDMW) (New Orleans, USA: IEEE), 18–21 November, pp. 1058–1065.
  • Muharemi, F., Logofătu, D., Andersson, C. and Leon, F., 2018, Approaches to building a detection model for water quality: A case study. In: A. Sieminski, A.K.M. Nunez and Q.T. Ha (Eds) Modern approaches for intelligent information and database systems (Cham: Springer), pp. 173–183.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.