{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T22:03:11Z","timestamp":1770415391934,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,11]],"date-time":"2019-07-11T00:00:00Z","timestamp":1562803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["No. 2016YFC0400600"],"award-info":[{"award-number":["No. 2016YFC0400600"]}]},{"name":"the National Science and Technology Major Projects for Water Pollution Control and Treatment","award":["2017ZX07502003-05"],"award-info":[{"award-number":["2017ZX07502003-05"]}]},{"name":"the Science and Technology Program of Zhejiang Province","award":["Nos.2017C33174 and 2015C33007"],"award-info":[{"award-number":["Nos.2017C33174 and 2015C33007"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 51761145022"],"award-info":[{"award-number":["No. 51761145022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Leak detection is nowadays an important task for water utilities as leakages in water distribution systems (WDS) increase economic costs significantly and create water resource shortages. Monitoring data such as pressure and flow rate of WDS fluctuate with time. Diagnosis based on time series monitoring data is thought to be more convincing than one-time point data. In this paper, a threshold selection method for the correlation coefficient based on time series data is proposed based on leak scenario falsification, to explore the advantages of data interpretation based on time series for leak detection. The approach utilizes temporal varying correlation between data from multiple pressure sensors, updates the threshold values over time, and scans multiple times for a scanning time window. The effect of scanning time window length on threshold selection is also tested. The performance of the proposed method is tested on a real, full-scale water distribution network using synthetic data, considering the uncertainty of demand and leak flow rates, sensor noise, and so forth. The case study shows that the scanning time window length of 3\u20136 achieves better performance; the potential of the method for leak detection performance improvement is confirmed, though affected by many factors such as modeling and measurement uncertainties.<\/jats:p>","DOI":"10.3390\/s19143070","type":"journal-article","created":{"date-parts":[[2019,7,11]],"date-time":"2019-07-11T11:28:28Z","timestamp":1562844508000},"page":"3070","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Time-Series-Based Leakage Detection Using Multiple Pressure Sensors in Water Distribution Systems"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2435-5618","authenticated-orcid":false,"given":"Yu","family":"Shao","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-5461","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuqiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shipeng","family":"Chu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4013-7197","authenticated-orcid":false,"given":"Xiaowei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China"},{"name":"Institute of Water Resources &amp; Ocean Engineering, Ocean College, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1080\/15730621003610878","article-title":"A review of methods for leakage management in pipe networks","volume":"7","author":"Puust","year":"2010","journal-title":"Urban Water J."},{"key":"ref_2","unstructured":"Alaska Department of Environmental Conservation (1999). Technical Review of Leak Detection Technologies: Crude Oil Transmission Pipelines."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jlp.2016.03.010","article-title":"A review on different pipeline fault detection methods","volume":"41","author":"Datta","year":"2016","journal-title":"J. Loss Prev. Process Industries"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/978-94-017-2677-1_22","article-title":"Leak detection through hydraulic transient analysis","volume":"7","author":"Bryan","year":"1992","journal-title":"Pipeline Systems"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/S0309-1708(02)00102-1","article-title":"Pipe system diagnosis and leak detection by unsteady-state tests. 2. Wavelet analysis","volume":"26","author":"Ferrante","year":"2003","journal-title":"Adv. Water Resour."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1061\/(ASCE)0733-9496(2005)131:4(316)","article-title":"Pipeline break detection using pressure transient monitoring","volume":"131","author":"Misiunas","year":"2005","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1061\/(ASCE)0733-9429(2007)133:11(1274)","article-title":"Wavelets for the analysis of transient pressure signals for leak detection","volume":"133","author":"Ferrante","year":"2007","journal-title":"J. Hydraul. Eng. ASCE."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lin, C. (2017). A Hybrid Heuristic Optimization Approach for Leak Detection in Pipe Networks Using Ordinal Optimization Approach and the Symbiotic Organism Search. Water, 9.","DOI":"10.3390\/w9100812"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1061\/(ASCE)WR.1943-5452.0000298","article-title":"Frequency Response Diagram for Pipeline Leak Detection: Comparing the Odd and Even Harmonics","volume":"140","author":"Gong","year":"2014","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.jher.2009.02.003","article-title":"A selective literature review of transient-based leak detection methods","volume":"2","author":"Colombo","year":"2009","journal-title":"J. Hydro Environ. Resour."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1061\/(ASCE)0733-9429(1992)118:7(1031)","article-title":"Leaks in pipe networks","volume":"118","author":"Pudar","year":"1992","journal-title":"J. Hydraul. Eng. ASCE"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"04015057","DOI":"10.1061\/(ASCE)WR.1943-5452.0000592","article-title":"Leak Detection and Localization through Demand Components Calibration","volume":"142","author":"Sanz","year":"2016","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_13","unstructured":"Ulanicki, B., Vairavamoorthy, K., Butler, D., Bounds, P.L.M., and Memon, F.A. (2007). Pressure dependent demand optimization for leakage detection in water distribution systems. Water Management Challenges in Global Change, Proceedings of Combined CCWI2007 and SUWM2007, Leicester, UK, 3\u20135 September 2007, Taylor & Francis."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1080\/15730620802541631","article-title":"Unified parameter optimisation approach for leakage detection and extended-period simulation model calibration","volume":"6","author":"Wu","year":"2009","journal-title":"Urban Water J."},{"key":"ref_15","unstructured":"Wu, Z.Y., Burrows, R., and Moorcroft, J. (2010, January 12\u201315). Pressure-dependent leakage detection method compared with conventional techniques. Proceedings of the 12th Annual Conference on Water Distribution Systems Analysis (WDSA), Tucson, AZ, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1002\/prs.680220208","article-title":"Using neural networks to monitor piping systems","volume":"22","author":"Caputo","year":"2003","journal-title":"Process Saf. Prog."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1061\/(ASCE)WR.1943-5452.0000030","article-title":"Development and Verification of an Online Artificial Intelligence System for Detection of Bursts and Other Abnormal Flows","volume":"136","author":"Mounce","year":"2010","journal-title":"J. Water Resour. Plan. Manag. ASCE"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3937","DOI":"10.1016\/j.eswa.2010.09.055","article-title":"Bayesian reasoning approach based recursive algorithm for online updating belief rule based expert system of pipeline leak detection","volume":"38","author":"Zhou","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1016\/j.proeng.2014.11.493","article-title":"Model Calibration as a Tool for Leakage Identification in WDS: A Real Case Study","volume":"89","author":"Costanzo","year":"2014","journal-title":"Proced. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wu, Z.Y., Zhao, M., Qi, J., Huang, Y., and Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. J. Water Resour. Plan. Manag., 142.","DOI":"10.1061\/(ASCE)WR.1943-5452.0000661"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"672","DOI":"10.2166\/hydro.2010.144","article-title":"Novelty detection for time series data analysis in water distribution systems using support vector machines","volume":"13","author":"Mounce","year":"2011","journal-title":"J. Hydroinform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"25","DOI":"10.13176\/11.548","article-title":"Virtual DMA municipal water supply pipeline leak detection and classification using advance pattern recognizer multi-class SVM","volume":"1","author":"Mamo","year":"2014","journal-title":"J. Pattern Recognit. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1016\/j.conengprac.2011.06.004","article-title":"Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks","volume":"19","author":"Perez","year":"2011","journal-title":"Control Eng. Pract."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MCS.2014.2320336","article-title":"Leak Localization in Water Networks A Model-Based Methodology Using Pressure Sensors Applied to a Real Network in Barcelona","volume":"34","author":"Perez","year":"2014","journal-title":"Ieee Control Syst. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.3390\/w7031129","article-title":"Leak Signature Space: An Original Representation for Robust Leak Location in Water Distribution Networks","volume":"7","author":"Casillas","year":"2015","journal-title":"Water"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.aei.2013.01.001","article-title":"Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks","volume":"27","author":"Goulet","year":"2013","journal-title":"Adv. Eng. Inform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"04017077","DOI":"10.1061\/(ASCE)CP.1943-5487.0000729","article-title":"Leak Detection of Water Supply Networks Using Error-Domain Model Falsification","volume":"32","author":"Moser","year":"2017","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.envsoft.2014.06.025","article-title":"A decision support system for on-line leakage localization","volume":"60","author":"Meseguer","year":"2014","journal-title":"Environ. Modell. Softw."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jung, D., and Lansey, K. (2014). Water Distribution System Burst Detection Using a Nonlinear Kalman Filter. J. Water Res. Plan. Man., 141.","DOI":"10.1061\/(ASCE)WR.1943-5452.0000464"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1061\/(ASCE)PS.1949-1204.0000070","article-title":"Kalman Filtering of Hydraulic Measurements for Burst Detection in Water Distribution Systems","volume":"2","author":"Ye","year":"2011","journal-title":"J. Pipeline Syst. Eng. Pract."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6014001.1","DOI":"10.1061\/(ASCE)WR.1943-5452.0000394","article-title":"Study of Burst Alarming and Data Sampling Frequency in Water Distribution Networks","volume":"140","author":"Ye","year":"2014","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1061\/(ASCE)HY.1943-7900.0000086","article-title":"Real-Time Demand Estimation and Confidence Limit Analysis for Water Distribution Systems","volume":"135","author":"Kang","year":"2009","journal-title":"J. Hydraul. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Arandia, E., Ba, A., Eck, B., and McKenna, S. (2015). Tailoring Seasonal Time Series Models to Forecast Short-Term Water Demand. J. Water Resour. Plan. Manag., 142.","DOI":"10.1061\/(ASCE)WR.1943-5452.0000591"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xie, X., Zhang, H., and Hou, D. (2017). Bayesian Approach for Joint Estimation of Demand and Roughness in Water Distribution Systems. J. Water Resour. Plan. Manag., 143.","DOI":"10.1061\/(ASCE)WR.1943-5452.0000791"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3389\/fbuil.2016.00008","article-title":"Studies of sensor data interpretation for asset management of the built environment","volume":"2","author":"Smith","year":"2016","journal-title":"Front. Built Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.compstruc.2013.07.009","article-title":"Structural identification with systematic errors and unknown uncertainty dependencies","volume":"128","author":"Goulet","year":"2013","journal-title":"Comput. Struct."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1061\/(ASCE)CP.1943-5487.0000250","article-title":"Performance-driven measure-ment system design for structural identification","volume":"27","author":"Goulet","year":"2013","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1007\/s11269-018-2032-3","article-title":"Comparison Among Resilience and Entropy Index in the Optimal Rehabilitation of Water Distribution Networks Under Limited-Budgets","volume":"32","author":"Cimorelli","year":"2018","journal-title":"Water Resour. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1061\/(ASCE)WR.1943-5452.0000086","article-title":"Demand and Roughness Estimation in Water Distribution Systems","volume":"137","author":"Kang","year":"2011","journal-title":"J. Water Resour. Plan. Manag. ASCE"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wu, Y., Liu, S., Smith, K., and Wang, X. (2017). Using Correlation between Data from Multiple Monitoring Sensors to Detect Bursts in Water Distribution Systems. J. Water Resour. Plan. Manag., 144.","DOI":"10.1061\/(ASCE)WR.1943-5452.0000870"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Abokifa, A.A., Haddad, K., Lo, C., and Biswas, P. (2018). Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning-Based Anomaly Detection Techniques. J. Water Resour. Plan. Manag., 145.","DOI":"10.1061\/(ASCE)WR.1943-5452.0001023"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wu, Y., Liu, S., and Wang, X. (2018). Distance-Based Burst Detection Using Multiple Pressure Sensors in District Metering Areas. J. Water Resour. Plan. Manag., 144.","DOI":"10.1061\/(ASCE)WR.1943-5452.0001001"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"649","DOI":"10.2166\/hydro.2013.019","article-title":"Model-based leak detection and location in water distribution networks considering an extended-horizon analysis of pressure sensitivities","volume":"16","year":"2014","journal-title":"J. Hydroinform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3070\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:04:48Z","timestamp":1760187888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3070"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,11]]},"references-count":43,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19143070"],"URL":"https:\/\/doi.org\/10.3390\/s19143070","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,11]]}}}