{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:26:22Z","timestamp":1774448782464,"version":"3.50.1"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031159336","type":"print"},{"value":"9783031159343","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-15934-3_62","type":"book-chapter","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:02:53Z","timestamp":1662422573000},"page":"752-762","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SAM-kNN Regressor for\u00a0Online Learning in\u00a0Water Distribution Networks"],"prefix":"10.1007","author":[{"given":"Jonathan","family":"Jakob","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9","family":"Artelt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martina","family":"Hasenj\u00e4ger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara","family":"Hammer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"62_CR1","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1155\/2019\/7932193"},{"key":"62_CR2","doi-asserted-by":"publisher","first-page":"78846","DOI":"10.1109\/ACCESS.2018.2885444","volume":"6","author":"TK Chan","year":"2018","unstructured":"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\u201378867 (2018)","journal-title":"IEEE Access"},{"key":"62_CR3","unstructured":"Farley, M., Trow, S.: Losses in Water Distribution Networks. IWA Publishing (2003)"},{"issue":"1","key":"62_CR4","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s13748-011-0002-6","volume":"1","author":"J Gama","year":"2012","unstructured":"Gama, J.: A survey on learning from data streams: current and future trends. Prog. Artif. Intell. 1(1), 45\u201355 (2012)","journal-title":"Prog. Artif. Intell."},{"key":"62_CR5","doi-asserted-by":"crossref","unstructured":"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\u201322 (2019)","DOI":"10.1145\/3373464.3373470"},{"key":"62_CR6","doi-asserted-by":"crossref","unstructured":"Jakob, J., Hasenj\u00e4ger, 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","DOI":"10.1109\/SSCI50451.2021.9660138"},{"key":"62_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/978-3-030-86340-1_31","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2021","author":"Q Jodelet","year":"2021","unstructured":"Jodelet, Q., Liu, X., Murata, T.: Balanced softmax cross-entropy for\u00a0incremental learning. In: Farka\u0161, I., Masulli, P., Otte, S., Wermter, S. (eds.) ICANN 2021. LNCS, vol. 12892, pp. 385\u2013396. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86340-1_31"},{"key":"62_CR8","doi-asserted-by":"crossref","unstructured":"Klise, K.A., Murray, R., Haxton, T.: An overview of the water network tool for resilience (WNTR) (2018)","DOI":"10.2172\/1376816"},{"key":"62_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/978-3-030-69544-6_22","volume-title":"Computer Vision \u2013 ACCV 2020","author":"C-H Lei","year":"2021","unstructured":"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\u2013374. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-69544-6_22"},{"key":"62_CR10","unstructured":"Li, H., Dong, W., Hu, B.G.: Incremental concept learning via online generative memory recall (2019). https:\/\/arxiv.org\/abs\/1907.02788"},{"key":"62_CR11","unstructured":"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)"},{"key":"62_CR12","doi-asserted-by":"publisher","unstructured":"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","DOI":"10.1109\/cvpr42600.2020.01226"},{"key":"62_CR13","doi-asserted-by":"publisher","unstructured":"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\u2013300 (2016). https:\/\/doi.org\/10.1109\/ICDM.2016.0040","DOI":"10.1109\/ICDM.2016.0040"},{"issue":"10","key":"62_CR14","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.3390\/w11101959","volume":"11","author":"C Makropoulos","year":"2019","unstructured":"Makropoulos, C., Savi\u0107, D.: Urban hydroinformatics: past, present and future. Water 11(10), 1959 (2019)","journal-title":"Water"},{"issue":"7","key":"62_CR15","doi-asserted-by":"publisher","first-page":"04020061","DOI":"10.1061\/(ASCE)EE.1943-7870.0001722","volume":"146","author":"D Nikolopoulos","year":"2020","unstructured":"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)","journal-title":"J. Environ. Eng."},{"key":"62_CR16","unstructured":"Vrachimis, S.G., et al.: BattLeDIM: battle of the leakage detection and isolation methods (2020)"},{"issue":"9","key":"62_CR17","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1080\/1573062X.2017.1279191","volume":"14","author":"Y Wu","year":"2017","unstructured":"Wu, Y., Liu, S.: A review of data-driven approaches for burst detection in water distribution systems. Urban Water J. 14(9), 972\u2013983 (2017)","journal-title":"Urban Water J."},{"key":"62_CR18","unstructured":"Wu, Y., et al.: Incremental classifier learning with generative adversarial networks (2018). https:\/\/arxiv.org\/abs\/1802.00853"},{"key":"62_CR19","doi-asserted-by":"publisher","unstructured":"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\u201335. IEEE (2012). https:\/\/doi.org\/10.1109\/SOLI.2012.6273500","DOI":"10.1109\/SOLI.2012.6273500"},{"key":"62_CR20","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.energy.2016.07.092","volume":"113","author":"Y Yang","year":"2016","unstructured":"Yang, Y., Che, J., Li, Y., Zhao, Y., Zhu, S.: An incremental electric load forecasting model based on support vector regression. Energy 113, 796\u2013808 (2016). https:\/\/doi.org\/10.1016\/j.energy.2016.07.092","journal-title":"Energy"},{"key":"62_CR21","unstructured":"Zhu, Q., He, Z., Ye, X.: Incremental classifier learning based on PEDCC-loss and cosine distance (2019). https:\/\/arxiv.org\/abs\/1906.04734"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-15934-3_62","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T06:08:37Z","timestamp":1663135717000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-15934-3_62"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031159336","9783031159343"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-15934-3_62","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bristol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"561","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"255","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}