{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:50:39Z","timestamp":1767340239105,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031493607"},{"type":"electronic","value":"9783031493614"}],"license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-49361-4_5","type":"book-chapter","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T05:02:31Z","timestamp":1702443751000},"page":"86-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Real-Time Leakage Zone Detection in\u00a0Water Distribution Networks: A Machine Learning-Based Stream Processing Algorithm"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6256-2752","authenticated-orcid":false,"given":"Domenico","family":"Garlisi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0447-6222","authenticated-orcid":false,"given":"Gabriele","family":"Restuccia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1305-0248","authenticated-orcid":false,"given":"Ilenia","family":"Tinnirello","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9122-7993","authenticated-orcid":false,"given":"Francesca","family":"Cuomo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8955-9270","authenticated-orcid":false,"given":"Ioannis","family":"Chatzigiannakis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"5_CR1","unstructured":"LEAKSTREAM. https:\/\/github.com\/domenico-garlisi\/LEAKSTREAM. Accessed 07 July 2023"},{"key":"5_CR2","unstructured":"OpenWaterAnalytics. https:\/\/raw.githubusercontent.com\/OpenWaterAnalytics\/epanet-example-networks\/master\/epanet-tests\/large"},{"issue":"6","key":"5_CR3","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1007\/s00778-014-0357-y","volume":"23","author":"A Alexandrov","year":"2014","unstructured":"Alexandrov, A., et al.: The stratosphere platform for big data analytics. VLDB J. 23(6), 939\u2013964 (2014). https:\/\/doi.org\/10.1007\/s00778-014-0357-y","journal-title":"VLDB J."},{"issue":"6","key":"5_CR4","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.3390\/app10061965","volume":"10","author":"D Amaxilatis","year":"2020","unstructured":"Amaxilatis, D., Chatzigiannakis, I., Tselios, C., Tsironis, N., Niakas, N., Papadogeorgos, S.: A smart water metering deployment based on the fog computing paradigm. Appl. Sci. 10(6), 1965 (2020). https:\/\/doi.org\/10.3390\/app10061965","journal-title":"Appl. Sci."},{"issue":"4","key":"5_CR5","doi-asserted-by":"publisher","first-page":"1938","DOI":"10.1177\/1475921720950470","volume":"20","author":"J Chen","year":"2021","unstructured":"Chen, J., Feng, X., Xiao, S.: An iterative method for leakage zone identification in water distribution networks based on machine learning. Struct. Health Monit. 20(4), 1938\u20131956 (2021)","journal-title":"Struct. Health Monit."},{"key":"5_CR6","unstructured":"Cross, H.: Analysis of flow in networks of conduits or conductors. Technical report. University of Illinois at Urbana Champaign, College of Engineering... (1936)"},{"key":"5_CR7","volume-title":"Chemical Process Safety: Fundamentals with Applications","author":"DA Crowl","year":"2001","unstructured":"Crowl, D.A., Louvar, J.F.: Chemical Process Safety: Fundamentals with Applications. Pearson Education, London (2001)"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"Ferrandez-Gamot, L., et al.: Leak localization in water distribution networks using pressure residuals and classifiers. IFAC-PapersOnLine (2015). https:\/\/doi.org\/10.1016\/j.ifacol.2015.09.531. 9th IFAC Symposium on Fault Detection, Supervision andSafety for Technical Processes SAFEPROCESS 2015","DOI":"10.1016\/j.ifacol.2015.09.531"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Garlisi, D., Restuccia, G., Tinnirello, I., Cuomo, F., Chatzigiannakis, I.: Leakage detection via edge processing in LoRaWAN-based smart water distribution networks. In: 2022 18th International Conference on Mobility, Sensing and Networking (MSN), pp. 223\u2013230. IEEE (2022)","DOI":"10.1109\/MSN57253.2022.00047"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Ghemawat, S., Gobioff, H., Leung, S.T.: The google file system. In: Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, SOSP \u201903, pp. 29\u201343. Association for Computing Machinery, New York (2003). https:\/\/doi.org\/10.1145\/945445.945450","DOI":"10.1145\/945445.945450"},{"key":"5_CR11","doi-asserted-by":"publisher","first-page":"107177","DOI":"10.1109\/ACCESS.2022.3212769","volume":"10","author":"MR Islam","year":"2022","unstructured":"Islam, M.R., Azam, S., Shanmugam, B., Mathur, D.: A review on current technologies and future direction of water leakage detection in water distribution network. IEEE Access 10, 107177\u2013107201 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3212769","journal-title":"IEEE Access"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Javadiha, M., Blesa, J., Soldevila, A., Puig, V.: Leak localization in water distribution networks using deep learning. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 1426\u20131431. IEEE (2019)","DOI":"10.1109\/CoDIT.2019.8820627"},{"key":"5_CR13","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1016\/j.envsoft.2017.06.022","volume":"95","author":"KA Klise","year":"2017","unstructured":"Klise, K.A., Bynum, M., Moriarty, D., Murray, R.: A software framework for assessing the resilience of drinking water systems to disasters with an example earthquake case study. Environ. Model. Softw. 95, 420\u2013431 (2017)","journal-title":"Environ. Model. Softw."},{"key":"5_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21139\/wej.2019.023","volume":"4","author":"T Randall","year":"2019","unstructured":"Randall, T., Koech, R.: Smart water metering technology for water management in urban areas. Water eJ 4, 1\u201314 (2019)","journal-title":"Water eJ"},{"key":"5_CR15","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.conengprac.2016.07.006","volume":"55","author":"A Soldevila","year":"2016","unstructured":"Soldevila, A., Blesa, J., Tornil-Sin, S., Duviella, E., Fernandez-Canti, R., Puig, V.: Leak localization in water distribution networks using a mixed model-based\/data-driven approach. Control. Eng. Pract. 55, 162\u2013173 (2016). https:\/\/doi.org\/10.1016\/j.conengprac.2016.07.006","journal-title":"Control. Eng. Pract."},{"key":"5_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jprocont.2017.03.015","volume":"55","author":"A Soldevila","year":"2017","unstructured":"Soldevila, A., Fernandez-Canti, R.M., Blesa, J., Tornil-Sin, S., Puig, V.: Leak localization in water distribution networks using bayesian classifiers. J. Process Control 55, 1\u20139 (2017)","journal-title":"J. Process Control"},{"key":"5_CR17","unstructured":"Sornin, N., Y.A.E.A.: LoRaWAN 1.1 Specification (2017). https:\/\/lora-alliance.org\/resource-hub\/lorawantm-specification-v11"},{"key":"5_CR18","unstructured":"Urama, K.E.A.: Options for decoupling economic growth from water use and water pollution: a report of the water working group of the UNEP international resource panel (2016)"},{"key":"5_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1007\/978-3-030-34255-5_5","volume-title":"Ambient Intelligence","author":"JM Valtorta","year":"2019","unstructured":"Valtorta, J.M., Martino, A., Cuomo, F., Garlisi, D.: A clustering approach for profiling LoRaWAN IoT devices. In: Chatzigiannakis, I., De Ruyter, B., Mavrommati, I. (eds.) AmI 2019. LNCS, vol. 11912, pp. 58\u201374. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-34255-5_5"},{"key":"5_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55944-5","volume-title":"Modeling and Monitoring of Pipelines and Networks","author":"C Verde","year":"2017","unstructured":"Verde, C., Torres, L.: Modeling and Monitoring of Pipelines and Networks. Springer, Cham (2017)"},{"issue":"6","key":"5_CR21","doi-asserted-by":"publisher","first-page":"04020031","DOI":"10.1061\/(ASCE)WR.1943-5452.0001223","volume":"146","author":"X Wang","year":"2020","unstructured":"Wang, X., Guo, G., Liu, S., Wu, Y., Xu, X., Smith, K.: Burst detection in district metering areas using deep learning method. J. Water Resour. Plan. Manag. 146(6), 04020031 (2020)","journal-title":"J. Water Resour. Plan. Manag."},{"key":"5_CR22","unstructured":"Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: Cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud\u201910, p. 10 (2010)"},{"key":"5_CR23","unstructured":"Zeuch, S., et al.: The nebulastream platform: data and application management for the internet of things. In: Conference on Innovative Data Systems Research (CIDR) (2020)"},{"issue":"11","key":"5_CR24","doi-asserted-by":"publisher","first-page":"04016042","DOI":"10.1061\/(ASCE)WR.1943-5452.0000661","volume":"142","author":"Q Zhang","year":"2016","unstructured":"Zhang, Q., Wu, Z.Y., Zhao, M., Qi, J., Huang, Y., Zhao, H.: Leakage zone identification in large-scale water distribution systems using multiclass support vector machines. J. Water Resour. Plan. Manag. 142(11), 04016042 (2016)","journal-title":"J. Water Resour. Plan. Manag."},{"issue":"10","key":"5_CR25","doi-asserted-by":"publisher","first-page":"03122002","DOI":"10.1061\/(ASCE)WR.1943-5452.0001597","volume":"148","author":"X Wan","year":"2022","unstructured":"Wan, X., Kuhanestani, P.K., Farmani, R., Keedwell, E.: Literature review of data analytics for leak detection in water distribution networks: a focus on pressure and flow smart sensors. J. Water Resour. Plan. Manage. 148(10), 03122002 (2022)","journal-title":"J. Water Resour. Plan. Manage."},{"key":"5_CR26","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1061\/(ASCE)WR.1943-5452.0000227","volume":"139","author":"J Liu","year":"2013","unstructured":"Liu, J., Guoping, Y.: Iterative methodology of pressure-dependent demand based on EPANET for pressure-deficient water distribution analysis. J. Water Resour. Plan. Manage. 139, 34\u201344 (2013). https:\/\/doi.org\/10.1061\/(ASCE)WR.1943-5452.0000227","journal-title":"J. Water Resour. Plan. Manage."}],"container-title":["Lecture Notes in Computer Science","Algorithmic Aspects of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49361-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T05:04:12Z","timestamp":1702443852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49361-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,14]]},"ISBN":["9783031493607","9783031493614"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49361-4_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,14]]},"assertion":[{"value":"14 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ALGOCLOUD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Algorithmic Aspects of Cloud Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"algocloud2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/algo-conference.org\/2023\/","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":"24","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":"13","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":"0","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":"54% - 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":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","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)"}}]}}