{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:37:49Z","timestamp":1776886669830,"version":"3.51.2"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819601271","type":"print"},{"value":"9789819601288","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0128-8_5","type":"book-chapter","created":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T18:17:15Z","timestamp":1731781035000},"page":"56-66","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Attention Model Based Approach for\u00a0Leakage Detection in\u00a0Water Distribution Networks Using Normal Pressure Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6097-1559","authenticated-orcid":false,"given":"Juan","family":"Luo","sequence":"first","affiliation":[]},{"given":"Du","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Chongxiao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jielong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xionghu","family":"Zhong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"5_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106062","volume":"122","author":"HM Tornyeviadzi","year":"2023","unstructured":"Tornyeviadzi, H.M., Seidu, R.: Leakage detection in water distribution networks via 1D CNN deep autoencoder for multivariate SCADA data. Eng. Appl. Artif. Intell. 122, 106062 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"5_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":"5_CR3","doi-asserted-by":"crossref","unstructured":"Jung, D., Lansey, K.: Water distribution system burst detection using a nonlinear Kalman filter. J. Water Resources Plan. Manag. 141(5), 04014070 (2015)","DOI":"10.1061\/(ASCE)WR.1943-5452.0000464"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Karray, F., Garcia-Ortiz, A., Jmal, M.W., Obeid, A.M., Abid, M.: Earnpipe: a testbed for smart water pipeline monitoring using wireless sensor network. Procedia Comput. Sci. 96, 285\u2013294 (2016)","DOI":"10.1016\/j.procs.2016.08.141"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Ye, G., Fenner, R. A.: Weighted least squares with expectation-maximization algorithm for burst detection in UK water distribution systems. J. Water Resources Plan. Manag. 140(4), 417\u2013424 (2014)","DOI":"10.1061\/(ASCE)WR.1943-5452.0000344"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Romano, M., Kapelan, Z., Savi\u0107, D.A.: Automated detection of pipe bursts and other events in water distribution systems. J. Water Resources Plan. Manag. 140(4), 457\u2013467 (2014)","DOI":"10.1061\/(ASCE)WR.1943-5452.0000339"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Rahali, A., Akhloufi, M.A.: End-to-End transformer-based models in textual-based NLP. AI 4, 54\u2013110 (2023)","DOI":"10.3390\/ai4010004"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"You, J., Korhonen, J.: Transformer for image quality assessment. In: 2021 IEEE International Conference on Image Processing, pp. 1389\u20131393 (2021)","DOI":"10.1109\/ICIP42928.2021.9506075"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Galili, I., Kaplan, D., Lehavi, Y.: Teaching Faraday\u2019s law of electromagnetic induction in an introductory physics course. Am. J. Phys. 74(4), 337\u2013343 (2006)","DOI":"10.1119\/1.2180283"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Bach, P.M., Kodikara, J.K.: Reliability of infrared thermography in detecting leaks in buried water reticulation pipes. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 10(9), 4210\u20134224 (2017)","DOI":"10.1109\/JSTARS.2017.2708817"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Chandrasekar, V., Chen, H., Tan, H.: Rainfall estimation from ground radar and TRMM Precipitation Radar using hybrid deep neural networks. Geophys. Res. Lett. 46(17), 10669\u201310678 (2019)","DOI":"10.1029\/2019GL084771"},{"key":"5_CR12","unstructured":"Duzinkiewicz, K., Borowa, A., Mazur, K., Grochowski, M., Brdys, M.A., Jezior, K.: Leakage detection and localisation in drinking water distribution networks by multiregional PCA. Stud. Inf. Control 17(2), 135(2008)"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Ravichandran, T., Gavahi, K., Ponnambalam, K., Burtea, V., Mousavi, S.J.: Ensemble-based machine learning approach for improved leak detection in water mains. J. Hydroinform. 23(2), 307\u2013323 (2021)","DOI":"10.2166\/hydro.2021.093"},{"key":"5_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2022.109810","volume":"185","author":"T Yu","year":"2023","unstructured":"Yu, T., Chen, X., Yan, W., Xu, Z., Ye, M.: Leak detection in water distribution systems by classifying vibration signals. Mech. Syst. Signal Process. 185, 109810 (2023)","journal-title":"Mech. Syst. Signal Process."},{"key":"5_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2022.127934","volume":"610","author":"P Mei","year":"2022","unstructured":"Mei, P., Li, M., Zhang, Q., Li, G.: Prediction model of drinking water source quality with potential industrial-agricultural pollution based on CNN-GRU-Attention. J. Hydrol. 610, 127934 (2022)","journal-title":"J. Hydrol."},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Zhou, M., Pan, Z., Liu, Y., Zhang, Q., Cai, Y., Pan, H.: Leak detection and location based on ISLMD and CNN in a pipeline. IEEE Access 7, 30457\u201330464 (2019)","DOI":"10.1109\/ACCESS.2019.2902711"},{"key":"5_CR17","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(2019)","DOI":"10.1109\/CoDIT.2019.8820627"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Leonzio, D.U., Bestagini, P., Marcon, M., Quarta, G.P., Tubaro, S.: Water leak detection and localization using convolutional autoencoders. In: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023), pp. 1\u20135 (2023)","DOI":"10.1109\/ICASSP49357.2023.10095760"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Kim, H., Park, M., Kim, C.W., Shin, D.: Source localization for hazardous material release in an outdoor chemical plant via a combination of LSTM-RNN and CFD simulation. Comput. Chem. Eng. 125, 476\u2013489 (2019)","DOI":"10.1016\/j.compchemeng.2019.03.012"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Wu, Y., Liu, S., Wu, X., Liu, Y., Guan, Y.: Burst detection in district metering areas using a data driven clustering algorithm. Water Res. 100, 28\u201337 (2016)","DOI":"10.1016\/j.watres.2016.05.016"},{"key":"5_CR21","unstructured":"Waswani, A., et al.: Attention is all you need. In: NIPS (2017)"},{"key":"5_CR22","unstructured":"Van Der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15(1), 3221\u20133245 (2014)"},{"key":"5_CR23","unstructured":"Vrachimis, S.G., Kyriakou, M.S.: LeakDB: a benchmark dataset for leakage diagnosis in water distribution networks. In: WDSA\/CCWI Joint Conference Proceedings 1(146) (2018)"},{"key":"5_CR24","doi-asserted-by":"crossref","unstructured":"Klise, K.A., Murray, R., Haxton, T.: An overview of the water network tool for resilience (WNTR). In: WDSA\/CCWI Joint Conference Proceedings, vol. 1 (2018)","DOI":"10.2172\/1376816"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2024: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0128-8_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T19:23:32Z","timestamp":1731785012000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0128-8_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,12]]},"ISBN":["9789819601271","9789819601288"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0128-8_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,12]]},"assertion":[{"value":"12 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}