Abstract
Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to consumers. In order to guarantee a working network at all times, the water supply company continuously monitors the network and takes actions when necessary – e.g. reacting to leakages, sensor faults and drops in water quality. Since real world networks are too large and complex to be monitored by a human, algorithmic monitoring systems have been developed. A popular type of such systems are residual based anomaly detection systems that can detect events such as leakages and sensor faults. For a continuous high quality monitoring, it is necessary for these systems to adapt to changed demands and presence of various anomalies.
In this work, we propose an adaption of the incremental SAM-kNN classifier for regression to build a residual based anomaly detection system for water distribution networks that is able to adapt to any kind of change.
J. Jakob and A. Artelt—Contributed equally.
We gratefully acknowledge funding from the VW-Foundation for the project IMPACT funded in the frame of the funding line AI and its Implications for Future Society, and funding from the European Research Council (ERC) under the ERC Synergy Grant Water-Futures (Grant agreement No. 951424).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Implementation is available on GitHub: https://github.com/andreArtelt/SAM-kNN-Regressor_OnlineLearning_WDNs.
- 2.
References
Alexander, A., Julius, T., Andrew, T., Ezera, A., Christine, A.: Contamination potentials of household water handling and storage practices in Kirundo subcounty, Kisoro district, Uganda (2019)
Chan, T.K., Chin, C.S., Zhong, X.: Review of current technologies and proposed intelligent methodologies for water distributed network leakage detection. IEEE Access 6, 78846–78867 (2018)
Farley, M., Trow, S.: Losses in Water Distribution Networks. IWA Publishing (2003)
Gama, J.: A survey on learning from data streams: current and future trends. Prog. Artif. Intell. 1(1), 45–55 (2012)
Gomes, H.M., Read, J., Bifet, A., Barddal, J.P., Gama, J.: Machine learning for streaming data: state of the art, challenges, and opportunities. In: ACM SIGKDD Explorations Newsletter, pp. 6–22 (2019)
Jakob, J., Hasenjäger, M., Hammer, B.: On the suitability of incremental learning for regression tasks in exoskeleton control. In: IEEE Symposium on Computational Intelligence in Data Mining (CIDM). IEEE, December 2021
Jodelet, Q., Liu, X., Murata, T.: Balanced softmax cross-entropy for incremental learning. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds.) ICANN 2021. LNCS, vol. 12892, pp. 385–396. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86340-1_31
Klise, K.A., Murray, R., Haxton, T.: An overview of the water network tool for resilience (WNTR) (2018)
Lei, C.-H., Chen, Y.-H., Peng, W.-H., Chiu, W.-C.: Class-incremental learning with rectified feature-graph preservation. In: Ishikawa, H., Liu, C.-L., Pajdla, T., Shi, J. (eds.) ACCV 2020. LNCS, vol. 12627, pp. 358–374. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69544-6_22
Li, H., Dong, W., Hu, B.G.: Incremental concept learning via online generative memory recall (2019). https://arxiv.org/abs/1907.02788
Liemberger, R., Marin, P., et al.: The challenge of reducing non-revenue water in developing countries-how the private sector can help: a look at performance-based service contracting (2006)
Liu, Y., Su, Y., Liu, A.A., Schiele, B., Sun, Q.: Mnemonics training: multi-class incremental learning without forgetting. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2020. https://doi.org/10.1109/cvpr42600.2020.01226
Losing, V., Hammer, B., Wersing, H.: KNN classifier with self adjusting memory for heterogeneous concept drift. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 291–300 (2016). https://doi.org/10.1109/ICDM.2016.0040
Makropoulos, C., Savić, D.: Urban hydroinformatics: past, present and future. Water 11(10), 1959 (2019)
Nikolopoulos, D., Moraitis, G., Bouziotas, D., Lykou, A., Karavokiros, G., Makropoulos, C.: Cyber-physical stress-testing platform for water distribution networks. J. Environ. Eng. 146(7), 04020061 (2020)
Vrachimis, S.G., et al.: BattLeDIM: battle of the leakage detection and isolation methods (2020)
Wu, Y., Liu, S.: A review of data-driven approaches for burst detection in water distribution systems. Urban Water J. 14(9), 972–983 (2017)
Wu, Y., et al.: Incremental classifier learning with generative adversarial networks (2018). https://arxiv.org/abs/1802.00853
Yan, J., Tian, C., Wang, Y., Huang, J.: Online incremental regression for electricity price prediction. In: Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 31–35. IEEE (2012). https://doi.org/10.1109/SOLI.2012.6273500
Yang, Y., Che, J., Li, Y., Zhao, Y., Zhu, S.: An incremental electric load forecasting model based on support vector regression. Energy 113, 796–808 (2016). https://doi.org/10.1016/j.energy.2016.07.092
Zhu, Q., He, Z., Ye, X.: Incremental classifier learning based on PEDCC-loss and cosine distance (2019). https://arxiv.org/abs/1906.04734
Acknowledgment
We acknowledge the bachelor thesis by Augustin Harter (Bielefeld University) and Yannik Sander (Bielefeld University) which served as a mental starting point for this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jakob, J., Artelt, A., Hasenjäger, M., Hammer, B. (2022). SAM-kNN Regressor for Online Learning in Water Distribution Networks. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13531. Springer, Cham. https://doi.org/10.1007/978-3-031-15934-3_62
Download citation
DOI: https://doi.org/10.1007/978-3-031-15934-3_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15933-6
Online ISBN: 978-3-031-15934-3
eBook Packages: Computer ScienceComputer Science (R0)