{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T17:54:35Z","timestamp":1770054875858,"version":"3.49.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032113573","type":"print"},{"value":"9783032113580","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-11358-0_29","type":"book-chapter","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T07:03:25Z","timestamp":1770015805000},"page":"351-361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Convolutional Neural Network for Burst Detection in Water Pipes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2886-4409","authenticated-orcid":false,"given":"Christian Fern\u00e1ndez","family":"Leal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4899-701X","authenticated-orcid":false,"given":"Jaime Chiang","family":"Cruz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5811-994X","authenticated-orcid":false,"given":"Iliover Vega","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4125-2656","authenticated-orcid":false,"given":"Jorge Ram\u00edrez","family":"Beltr\u00e1n","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,3]]},"reference":[{"key":"29_CR1","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":"29_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2021.108282","volume":"183","author":"Y Yu","year":"2021","unstructured":"Yu, Y., Safari, A., Niu, X., Drinkwater, B., Horoshenkov, K.V.: Acoustic and ultrasonic techniques for defect detection and condition monitoring in water and sewerage pipes: a review. Appl. Acoust. 183, 108282 (2021)","journal-title":"Appl. Acoust."},{"issue":"4","key":"29_CR3","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.3390\/smartcities4040069","volume":"4","author":"N Mashhadi","year":"2021","unstructured":"Mashhadi, N., Shahrour, I., Attoue, N., El Khattabi, J., Aljer, A.: Use of machine learning for leak detection and localization in water distribution systems. Smart Cities 4(4), 1293\u20131315 (2021)","journal-title":"Smart Cities"},{"issue":"5","key":"29_CR4","doi-asserted-by":"publisher","first-page":"04022016","DOI":"10.1061\/(ASCE)WR.1943-5452.0001542","volume":"148","author":"L Romero-Ben","year":"2022","unstructured":"Romero-Ben, L., Alves, D., Blesa, J., Cembrano, G., Puig, V., Duviella, E.: Leak localization in water distribution networks using data-driven and model-based approaches. J. Water Resour. Plan. Manag. 148(5), 04022016 (2022)","journal-title":"J. Water Resour. Plan. Manag."},{"key":"29_CR5","unstructured":"Vega-Gonzalez, I., Figueroa, O.P., Truti\u00e9-Carrero, E., Ram\u00edrez-Beltr\u00e1n, J.: Algorithm to detect bursts in water pipes for implementation in low-power devices. Rev. Cient\u00edfica Ing. Electr\u00f3nica Autom\u00e1tica Comun. 43 (2022)"},{"issue":"10","key":"29_CR6","first-page":"1995","volume":"3361","author":"Y LeCun","year":"1995","unstructured":"LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. Handb Brain Theory Neural Netw. 3361(10), 1995 (1995)","journal-title":"Handb Brain Theory Neural Netw."},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Srirangarajan, S., Iqbal, M., Lim, H.B., Allen, M., Preis, A., Whittle, A.J.: Water main burst event detection and localization. In: Water Distribution Systems Analysis 2010. Tucson, Arizona, United States: American Society of Civil Engineers, pp. 1324\u201335 (2011)","DOI":"10.1061\/41203(425)119"},{"issue":"2","key":"29_CR8","doi-asserted-by":"publisher","first-page":"211","DOI":"10.4995\/riai.2017.8738","volume":"15","author":"E Truti\u00e9-Carrero","year":"2018","unstructured":"Truti\u00e9-Carrero, E., Vald\u00e9s-Santiago, D., Le\u00f3n-Mec\u00edas, \u00c1., Ram\u00edrez-Beltr\u00e1n, J.: Detecci\u00f3n y Localizaci\u00f3n de Ruptura S\u00fabita mediante Transformada Wavelet Discreta y Correlaci\u00f3n Cruzada. Rev. Iberoam Autom\u00e1tica E Inform\u00e1tica Ind. 15(2), 211 (2018)","journal-title":"Rev. Iberoam Autom\u00e1tica E Inform\u00e1tica Ind."},{"issue":"2","key":"29_CR9","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.2166\/ws.2021.337","volume":"22","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Long, Z., Yao, T., Zhou, H., Yu, T., Zhou, Y.: Real-time burst detection based on multiple features of pressure data. Water Supply. 22(2), 1474\u20131491 (2022)","journal-title":"Water Supply."},{"issue":"7553","key":"29_CR10","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"29_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103256","volume":"117","author":"H Shukla","year":"2020","unstructured":"Shukla, H., Piratla, K.: Leakage detection in water pipelines using supervised classification of acceleration signals. Autom. Constr. 117, 103256 (2020)","journal-title":"Autom. Constr."},{"issue":"16","key":"29_CR12","doi-asserted-by":"publisher","first-page":"8034","DOI":"10.3390\/app12168034","volume":"12","author":"YL Tsai","year":"2022","unstructured":"Tsai, Y.L., Chang, H.C., Lin, S.N., Chiou, A.H., Lee, T.L.: Using convolutional neural networks in the development of a water pipe leakage and location identification system. Appl. Sci. 12(16), 8034 (2022)","journal-title":"Appl. Sci."},{"issue":"1","key":"29_CR13","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1177\/14759217221080198","volume":"22","author":"C Zhang","year":"2023","unstructured":"Zhang, C., Alexander, B.J., Stephens, M.L., Lambert, M.F., Gong, J.: A convolutional neural network for pipe crack and leak detection in smart water network. Struct. Health Monit. 22(1), 232\u2013244 (2023)","journal-title":"Struct. Health Monit."},{"issue":"5","key":"29_CR14","doi-asserted-by":"publisher","first-page":"4279","DOI":"10.1109\/TIE.2017.2764861","volume":"65","author":"J Kang","year":"2018","unstructured":"Kang, J., Park, Y.J., Lee, J., Wang, S.H., Eom, D.S.: Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Trans. Ind. Electron. 65(5), 4279\u20134289 (2018)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"13","key":"29_CR15","doi-asserted-by":"publisher","first-page":"5049","DOI":"10.1007\/s11269-022-03291-1","volume":"36","author":"S Kim","year":"2022","unstructured":"Kim, S., Jun, S., Jung, D.: Ensemble CNN model for effective pipe burst detection in water distribution systems. Water Resour Manag. 36(13), 5049\u20135061 (2022)","journal-title":"Water Resour Manag."},{"key":"29_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.123611","volume":"278","author":"X Hu","year":"2021","unstructured":"Hu, X., Han, Y., Yu, B., Geng, Z., Fan, J.: Novel leakage detection and water loss management of urban water supply network using multiscale neural networks. J. Clean. Prod. 278, 123611 (2021)","journal-title":"J. Clean. Prod."},{"issue":"1","key":"29_CR17","doi-asserted-by":"publisher","first-page":"04020097","DOI":"10.1061\/(ASCE)WR.1943-5452.0001296","volume":"147","author":"J Bohorquez","year":"2021","unstructured":"Bohorquez, J., Simpson, A.R., Lambert, M.F., Alexander, B.: Merging fluid transient waves and artificial neural networks for burst detection and identification in pipelines. J. Water Resour. Plan. Manag. 147(1), 04020097 (2021)","journal-title":"J. Water Resour. Plan. Manag."},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C..CM., Zhu, Y., Gharghabi, S., et al.: The UCR time series archive. IEEECAA J. Autom Sin. (2019)","DOI":"10.1109\/JAS.2019.1911747"},{"key":"29_CR19","unstructured":"Anh, P.T.M.: Overview of class activation maps for visualization explainability (2023)"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-11358-0_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T07:03:27Z","timestamp":1770015807000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-11358-0_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032113573","9783032113580"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-11358-0_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"3 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"IWAIPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Congress on Artificial Intelligence and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Varadero","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuba","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwaipr2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eventos.uci.cu\/en\/event\/ix-international-congress-on-artificial-intelligence-and-pattern-recognition-iwaipr-2025-2\/register","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}