Unsupervised anomaly detection in hourly water demand data using an asymmetric encoder–decoder model
Water demand forecasting (WDF) is essential for the design and optimal management of water distribution systems (WDS). Historical water demand data contribute significantly to WDF. Yet the obtained water demand data contain anomalies on occasions due to failures in WDS or monitoring systems. The con...
Saved in:
| Published in: | Journal of hydrology (Amsterdam) Vol. 613; p. 128389 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.10.2022
|
| Subjects: | |
| ISSN: | 0022-1694, 1879-2707 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Water demand forecasting (WDF) is essential for the design and optimal management of water distribution systems (WDS). Historical water demand data contribute significantly to WDF. Yet the obtained water demand data contain anomalies on occasions due to failures in WDS or monitoring systems. The contaminated water demand data are invalid to describe the actual water demand, thus degrading the performance of WDF models. However, the importance of anomaly detection in water demand data is underestimated, or at least not explicitly described in many published papers. To fill the gap, we propose an unsupervised anomaly detection method based on an asymmetric encoder–decoder (asyED) model. Different from the symmetric structure of a traditional autoencoder where a signal is reproduced from itself, asyED is asymmetric where a signal is reproduced from the upstream and downstream information of the signal, while the signal itself does not participate in the reconstruction. In light of this feature, asyED is powerful in identifying anomalies. The proposed method is employed to detect anomalies in hourly water demand data which exhibit trend and seasonality. The results show the superiority of the proposed method over the other four commonly used anomaly detection methods: Z-score, isolation forest, local outlier factor, and seasonal hybrid ESD (Extreme Studentized Deviate test).
•An anomaly detection method based on an asymmetric encoder-decoder model is proposed.•The method applies to hourly water demand data which exhibit trend and seasonality.•A cluster-based metric is proposed for model evaluation.•The metric considers the continuity mechanism of anomalies found in the data.•Measures to quantify the strengths of different components in the data are proposed. |
|---|---|
| AbstractList | Water demand forecasting (WDF) is essential for the design and optimal management of water distribution systems (WDS). Historical water demand data contribute significantly to WDF. Yet the obtained water demand data contain anomalies on occasions due to failures in WDS or monitoring systems. The contaminated water demand data are invalid to describe the actual water demand, thus degrading the performance of WDF models. However, the importance of anomaly detection in water demand data is underestimated, or at least not explicitly described in many published papers. To fill the gap, we propose an unsupervised anomaly detection method based on an asymmetric encoder–decoder (asyED) model. Different from the symmetric structure of a traditional autoencoder where a signal is reproduced from itself, asyED is asymmetric where a signal is reproduced from the upstream and downstream information of the signal, while the signal itself does not participate in the reconstruction. In light of this feature, asyED is powerful in identifying anomalies. The proposed method is employed to detect anomalies in hourly water demand data which exhibit trend and seasonality. The results show the superiority of the proposed method over the other four commonly used anomaly detection methods: Z-score, isolation forest, local outlier factor, and seasonal hybrid ESD (Extreme Studentized Deviate test).
•An anomaly detection method based on an asymmetric encoder-decoder model is proposed.•The method applies to hourly water demand data which exhibit trend and seasonality.•A cluster-based metric is proposed for model evaluation.•The metric considers the continuity mechanism of anomalies found in the data.•Measures to quantify the strengths of different components in the data are proposed. Water demand forecasting (WDF) is essential for the design and optimal management of water distribution systems (WDS). Historical water demand data contribute significantly to WDF. Yet the obtained water demand data contain anomalies on occasions due to failures in WDS or monitoring systems. The contaminated water demand data are invalid to describe the actual water demand, thus degrading the performance of WDF models. However, the importance of anomaly detection in water demand data is underestimated, or at least not explicitly described in many published papers. To fill the gap, we propose an unsupervised anomaly detection method based on an asymmetric encoder–decoder (asyED) model. Different from the symmetric structure of a traditional autoencoder where a signal is reproduced from itself, asyED is asymmetric where a signal is reproduced from the upstream and downstream information of the signal, while the signal itself does not participate in the reconstruction. In light of this feature, asyED is powerful in identifying anomalies. The proposed method is employed to detect anomalies in hourly water demand data which exhibit trend and seasonality. The results show the superiority of the proposed method over the other four commonly used anomaly detection methods: Z-score, isolation forest, local outlier factor, and seasonal hybrid ESD (Extreme Studentized Deviate test). |
| ArticleNumber | 128389 |
| Author | Tao, Tao Yan, Jieru |
| Author_xml | – sequence: 1 givenname: Jieru orcidid: 0000-0003-1073-5396 surname: Yan fullname: Yan, Jieru email: yan_jieru@tongji.edu.cn – sequence: 2 givenname: Tao orcidid: 0000-0003-4214-7097 surname: Tao fullname: Tao, Tao email: taotao@tongji.edu.cn |
| BookMark | eNqFkEtuHCEQhlFkSxk_jhCJZTY94THd0Moiiqw4sWQpm3iNylDYjLphArSj2fkOuWFOEibjVTZmASWqvl-q74ycxBSRkHecrTnjw4ftevu4dzlNa8GEWHOhpR7fkBXXauyEYuqErFjrdHwYN2_JWSlb1o6UmxXxd7EsO8xPoaCjENMM0546rGhrSJGGSB_TktvfL6iYW2eG6KiDCnQpIT40hkLZzzPWHCzFaJPD_Of5t8N_FZ3bPV2QUw9TwcuX95zcXX_5cfWtu_3-9ebq821nhRK1U5Ip5pUbhbC97jX2sh88unGEQWjmUQIX9wy47YW196MWA3pQVkvrwW96eU7eH3N3Of1csFQzh2JxmiBiWooRimuphmHD2-jH46jNqZSM3thQ4bB0zRAmw5k52DVb82LXHOyao91G9__RuxxmyPtXuU9HDpuFp4DZFBuaNHQhN-XGpfBKwl9jQZz4 |
| CitedBy_id | crossref_primary_10_1016_j_jwpe_2025_108207 crossref_primary_10_1016_j_autcon_2024_105794 crossref_primary_10_1016_j_jenvman_2024_120496 crossref_primary_10_1177_14759217251372614 crossref_primary_10_1016_j_jhydrol_2024_132599 crossref_primary_10_1016_j_nucengdes_2024_113493 crossref_primary_10_1061_JPSEA2_PSENG_1589 |
| Cites_doi | 10.1002/2017WR022007 10.1061/(ASCE)HE.1943-5584.0000182 10.1145/2133360.2133363 10.1061/(ASCE)WR.1943-5452.0001276 10.3390/w13050582 10.1061/(ASCE)WR.1943-5452.0001535 10.1147/JRD.2009.5429018 10.1061/(ASCE)WR.1943-5452.0000992 10.1061/(ASCE)WR.1943-5452.0000983 10.1080/00401706.1983.10487848 10.1145/3444690 10.1155/2019/9765468 10.1007/s11269-021-02808-4 10.1145/335191.335388 10.1029/2009WR008147 10.1007/s10115-006-0026-6 10.3758/BRM.38.2.344 10.1016/j.envsoft.2014.06.016 10.1016/j.cam.2016.02.009 10.1016/j.patcog.2018.11.019 10.1038/nmeth.3945 10.1016/j.jhydrol.2010.04.005 10.1016/j.jhydrol.2021.126353 10.1016/j.jhydrol.2022.127440 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier B.V. |
| Copyright_xml | – notice: 2022 Elsevier B.V. |
| DBID | AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.jhydrol.2022.128389 |
| DatabaseName | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography |
| EISSN | 1879-2707 |
| ExternalDocumentID | 10_1016_j_jhydrol_2022_128389 S002216942200957X |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1RT 1~. 1~5 29K 4.4 457 4G. 5GY 5VS 6TJ 7-5 71M 8P~ 9JM 9JN AABNK AABVA AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALCJ AALRI AAOAW AAQFI AAQXK AATLK AAXUO ABEFU ABFNM ABGRD ABJNI ABMAC ABQEM ABQYD ABTAH ABXDB ABYKQ ACDAQ ACGFS ACIUM ACLVX ACNCT ACRLP ACSBN ADBBV ADEZE ADMUD ADQTV AEBSH AEKER AENEX AEQOU AFFNX AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHHHB AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BLXMC CBWCG CS3 D-I DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FA8 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HLV HMA HVGLF HZ~ H~9 IHE IMUCA J1W K-O KOM LW9 LY3 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAB SCC SDF SDG SDP SEP SES SEW SPC SPCBC SPD SSA SSE SSZ T5K TN5 UQL VOH WUQ Y6R ZCA ZMT ZY4 ~02 ~G- ~KM 9DU AAHBH AATTM AAXKI AAYWO AAYXX ABUFD ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO ADVLN AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7S9 L.6 |
| ID | FETCH-LOGICAL-c272t-73070f7d922c5858e5356fed99a6280fe3a12b0a1c52ccb9826efa7c83cfaf453 |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000862204200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0022-1694 |
| IngestDate | Sun Sep 28 02:16:03 EDT 2025 Sat Nov 29 07:27:50 EST 2025 Tue Nov 18 21:59:35 EST 2025 Fri Feb 23 02:38:42 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Hourly water demand Asymmetric encoder–decoder Autoencoder Anomaly detection Time series |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c272t-73070f7d922c5858e5356fed99a6280fe3a12b0a1c52ccb9826efa7c83cfaf453 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0003-4214-7097 0000-0003-1073-5396 |
| PQID | 2718376641 |
| PQPubID | 24069 |
| ParticipantIDs | proquest_miscellaneous_2718376641 crossref_citationtrail_10_1016_j_jhydrol_2022_128389 crossref_primary_10_1016_j_jhydrol_2022_128389 elsevier_sciencedirect_doi_10_1016_j_jhydrol_2022_128389 |
| PublicationCentury | 2000 |
| PublicationDate | October 2022 2022-10-00 20221001 |
| PublicationDateYYYYMMDD | 2022-10-01 |
| PublicationDate_xml | – month: 10 year: 2022 text: October 2022 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of hydrology (Amsterdam) |
| PublicationYear | 2022 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Guo, Liu, Wu, Li, Zhou, Zhu (b16) 2018; 144 Lempitsky (b22) 2021 An, Cho (b2) 2015 Breunig, Kriegel, Ng, Sander (b7) 2000; 29 Mehrang, Helander, Pavel, Chieh, Korhonen (b26) 2015 Carrera, Rossi, Fragneto, Boracchi (b10) 2019; 88 Liu, Ting, Zhou (b25) 2012; 6 Chen, Boccelli (b12) 2018; 54 Chen, Yan, Yan, Wang, Tao, Xin, Li, Pu, Qiu (b13) 2022; 606 Hochenbaum, Vallis, Kejariwal (b18) 2017 Lever, Krzywinski, Altman (b23) 2016; 13 Thompson (b33) 2006; 38 Lecun (b21) 1987 Herrera, Torgo, Izquierdo, Pérez-García (b17) 2010; 387 Blázquez-García, Conde, Mori, Lozano (b5) 2021; 54 Brentan, Luvizotto Jr., Herrera, Izquierdo, Pérez-García (b6) 2017; 309 Huang, Zhang, Song (b20) 2021; 35 Salloom, Kaynak, He (b31) 2021; 599 Carter, Streilein (b11) 2012 Hu, Gao, Zhong, Deng, Ou, Xin (b19) 2021; 13 Wong, Zhang, Chen (b34) 2010; 46 Basu, Meckesheimer (b4) 2007; 11 Yan (b35) 2022 Bunn, Reynolds (b8) 2009; 53 Liu, Ting, Zhou (b24) 2008 Mu, Zheng, Tao, Zhang, Kapelan (b27) 2020; 146 Romano, Kapelan (b29) 2014; 60 Daniel, Pesantez, Letzgus, Fasaee, Alghamdi, Berglund, Mahinthakumar, Cominola (b15) 2022; 148 Cleveland, Cleveland, McRae, Terpenning (b14) 1990 Rosner (b30) 1983; 25 Ambrosio, Brentan, Herrera, Luvizotto, Ribeiro, Izquierdo (b1) 2019 Caiado (b9) 2010; 15 Arandia, Ba, Eck, McKenna (b3) 2015; 142 Reddy, Ordway-West, Lee, Dugan, Whitney, Kahana, Ford, Muedsam, Henslee, Rao (b28) 2017 Taormina, Galelli (b32) 2018; 144 Caiado (10.1016/j.jhydrol.2022.128389_b9) 2010; 15 Herrera (10.1016/j.jhydrol.2022.128389_b17) 2010; 387 Reddy (10.1016/j.jhydrol.2022.128389_b28) 2017 Blázquez-García (10.1016/j.jhydrol.2022.128389_b5) 2021; 54 Hu (10.1016/j.jhydrol.2022.128389_b19) 2021; 13 Mu (10.1016/j.jhydrol.2022.128389_b27) 2020; 146 Mehrang (10.1016/j.jhydrol.2022.128389_b26) 2015 Wong (10.1016/j.jhydrol.2022.128389_b34) 2010; 46 Huang (10.1016/j.jhydrol.2022.128389_b20) 2021; 35 Liu (10.1016/j.jhydrol.2022.128389_b24) 2008 Guo (10.1016/j.jhydrol.2022.128389_b16) 2018; 144 Brentan (10.1016/j.jhydrol.2022.128389_b6) 2017; 309 Cleveland (10.1016/j.jhydrol.2022.128389_b14) 1990 Romano (10.1016/j.jhydrol.2022.128389_b29) 2014; 60 Yan (10.1016/j.jhydrol.2022.128389_b35) 2022 Ambrosio (10.1016/j.jhydrol.2022.128389_b1) 2019 Hochenbaum (10.1016/j.jhydrol.2022.128389_b18) 2017 Breunig (10.1016/j.jhydrol.2022.128389_b7) 2000; 29 Chen (10.1016/j.jhydrol.2022.128389_b12) 2018; 54 Chen (10.1016/j.jhydrol.2022.128389_b13) 2022; 606 Rosner (10.1016/j.jhydrol.2022.128389_b30) 1983; 25 Bunn (10.1016/j.jhydrol.2022.128389_b8) 2009; 53 Carrera (10.1016/j.jhydrol.2022.128389_b10) 2019; 88 Lecun (10.1016/j.jhydrol.2022.128389_b21) 1987 An (10.1016/j.jhydrol.2022.128389_b2) 2015 Thompson (10.1016/j.jhydrol.2022.128389_b33) 2006; 38 Taormina (10.1016/j.jhydrol.2022.128389_b32) 2018; 144 Daniel (10.1016/j.jhydrol.2022.128389_b15) 2022; 148 Lempitsky (10.1016/j.jhydrol.2022.128389_b22) 2021 Salloom (10.1016/j.jhydrol.2022.128389_b31) 2021; 599 Liu (10.1016/j.jhydrol.2022.128389_b25) 2012; 6 Lever (10.1016/j.jhydrol.2022.128389_b23) 2016; 13 Arandia (10.1016/j.jhydrol.2022.128389_b3) 2015; 142 Basu (10.1016/j.jhydrol.2022.128389_b4) 2007; 11 Carter (10.1016/j.jhydrol.2022.128389_b11) 2012 |
| References_xml | – volume: 60 start-page: 265 year: 2014 end-page: 276 ident: b29 article-title: Adaptive water demand forecasting for near real-time management of smart water distribution systems publication-title: Environ. Model. Softw. – volume: 29 start-page: 93 year: 2000 end-page: 104 ident: b7 article-title: LOF: identifying density-based local outliers publication-title: ACM SIGMOD Rec. – year: 2015 ident: b2 article-title: Variational autoencoder based anomaly detection using reconstruction probability – start-page: 1489 year: 2015 end-page: 1496 ident: b26 article-title: Outlier detection in weight time series of connected scales publication-title: 2015 IEEE International Conference on Bioinformatics and Biomedicine – volume: 54 start-page: 1 year: 2021 end-page: 33 ident: b5 article-title: A review on outlier/anomaly detection in time series data publication-title: ACM Comput. Surv. – volume: 53 start-page: 5:1 year: 2009 end-page: 5:13 ident: b8 article-title: The energy-efficiency benefits of pump-scheduling optimization for potable water supplies publication-title: IBM J. Res. Dev. – volume: 25 start-page: 165 year: 1983 end-page: 172 ident: b30 article-title: Percentage points for a generalized ESD many-outlier procedure publication-title: Technometrics – year: 2019 ident: b1 article-title: Committee machines for hourly water demand forecasting in water supply systems publication-title: Math. Probl. Eng. – volume: 15 start-page: 215 year: 2010 end-page: 222 ident: b9 article-title: Performance of combined double seasonal univariate time series models for forecasting water demand publication-title: J. Hydrol. Eng. – start-page: 68 year: 2021 end-page: 73 ident: b22 article-title: Autoencoder publication-title: Computer Vision: A Reference Guide – volume: 11 start-page: 137 year: 2007 end-page: 154 ident: b4 article-title: Automatic outlier detection for time series: an application to sensor data publication-title: Knowl. Inf. Syst. – year: 2022 ident: b35 article-title: Test datasets for the paper ”unsupervised anomaly detection in hourly water demand data using an asymmetric encoder-decoder model” – start-page: 413 year: 2008 end-page: 422 ident: b24 article-title: Isolation forest publication-title: 2008 Eighth IEEE International Conference on Data Mining – volume: 54 start-page: 879 year: 2018 end-page: 894 ident: b12 article-title: Forecasting hourly water demands with seasonal autoregressive models for real-time application publication-title: Water Resour. Res. – volume: 148 year: 2022 ident: b15 article-title: A sequential pressure-based algorithm for data-driven leakage identification and model-based localization in water distribution networks publication-title: J. Water Resour. Plan. Manage. – volume: 387 start-page: 141 year: 2010 end-page: 150 ident: b17 article-title: Predictive models for forecasting hourly urban water demand publication-title: J. Hydrol. – volume: 88 start-page: 482 year: 2019 end-page: 492 ident: b10 article-title: Online anomaly detection for long-term ECG monitoring using wearable devices publication-title: Pattern Recognit. – volume: 142 year: 2015 ident: b3 article-title: Tailoring seasonal time series models to forecast short-term water demand publication-title: J. Water Resour. Plan. Manage. – year: 2017 ident: b18 article-title: Automatic anomaly detection in the cloud via statistical learning – volume: 13 start-page: 603 year: 2016 end-page: 604 ident: b23 article-title: Classification evaluation publication-title: Nature Methods – volume: 144 year: 2018 ident: b32 article-title: Deep-learning approach to the detection and localization of cyber-physical attacks on water distribution systems publication-title: J. Water Resour. Plan. Manage. – year: 1990 ident: b14 article-title: STL: A seasonal-trend decomposition procedure based on loess (with discussion) – volume: 599 year: 2021 ident: b31 article-title: A novel deep neural network architecture for real-time water demand forecasting publication-title: J. Hydrol. – volume: 13 year: 2021 ident: b19 article-title: An innovative hourly water demand forecasting preprocessing framework with local outlier correction and adaptive decomposition techniques publication-title: Water – volume: 309 start-page: 532 year: 2017 end-page: 541 ident: b6 article-title: Hybrid regression model for near real-time urban water demand forecasting publication-title: J. Comput. Appl. Math. – volume: 6 start-page: 1 year: 2012 end-page: 39 ident: b25 article-title: Isolation-based anomaly detection publication-title: ACM Trans. Knowl. Discov. Data – volume: 144 year: 2018 ident: b16 article-title: Short-term water demand forecast based on deep learning method publication-title: J. Water Resour. Plan. Manage. – year: 2017 ident: b28 article-title: Using Gaussian mixture models to detect outliers in seasonal univariate network traffic – year: 1987 ident: b21 article-title: Modeles Connexionnistes de L’Apprentissage (Connectionist Learning Models) – volume: 35 year: 2021 ident: b20 article-title: An ensemble-learning-based method for short-term water demand forecasting publication-title: Water Resour. Manage. – start-page: 377 year: 2012 end-page: 380 ident: b11 article-title: Probabilistic Reasoning for Streaming Anomaly Detection – volume: 146 year: 2020 ident: b27 article-title: Hourly and daily urban water demand predictions using a long short-term memory based model publication-title: J. Water Resour. Plan. Manage. – volume: 46 year: 2010 ident: b34 article-title: Statistical modeling of daily urban water consumption in Hong Kong: Trend, changing patterns, and forecast publication-title: Water Resour. Res. – volume: 38 start-page: 344 year: 2006 end-page: 352 ident: b33 article-title: An SPSS implementation of the nonrecursive outlier deletion procedure with shifting z score criterion (Van Seist & Jolicøur, 1994) publication-title: Behav. Res. Methods – volume: 606 year: 2022 ident: b13 article-title: Short-term water demand forecast based on automatic feature extraction by one-dimensional convolution publication-title: J. Hydrol. – start-page: 1489 year: 2015 ident: 10.1016/j.jhydrol.2022.128389_b26 article-title: Outlier detection in weight time series of connected scales – volume: 54 start-page: 879 issue: 2 year: 2018 ident: 10.1016/j.jhydrol.2022.128389_b12 article-title: Forecasting hourly water demands with seasonal autoregressive models for real-time application publication-title: Water Resour. Res. doi: 10.1002/2017WR022007 – start-page: 413 year: 2008 ident: 10.1016/j.jhydrol.2022.128389_b24 article-title: Isolation forest – volume: 15 start-page: 215 issue: 3 year: 2010 ident: 10.1016/j.jhydrol.2022.128389_b9 article-title: Performance of combined double seasonal univariate time series models for forecasting water demand publication-title: J. Hydrol. Eng. doi: 10.1061/(ASCE)HE.1943-5584.0000182 – volume: 6 start-page: 1 issue: 1 year: 2012 ident: 10.1016/j.jhydrol.2022.128389_b25 article-title: Isolation-based anomaly detection publication-title: ACM Trans. Knowl. Discov. Data doi: 10.1145/2133360.2133363 – year: 2015 ident: 10.1016/j.jhydrol.2022.128389_b2 – volume: 146 issue: 9 year: 2020 ident: 10.1016/j.jhydrol.2022.128389_b27 article-title: Hourly and daily urban water demand predictions using a long short-term memory based model publication-title: J. Water Resour. Plan. Manage. doi: 10.1061/(ASCE)WR.1943-5452.0001276 – year: 1987 ident: 10.1016/j.jhydrol.2022.128389_b21 – volume: 13 issue: 5 year: 2021 ident: 10.1016/j.jhydrol.2022.128389_b19 article-title: An innovative hourly water demand forecasting preprocessing framework with local outlier correction and adaptive decomposition techniques publication-title: Water doi: 10.3390/w13050582 – volume: 148 issue: 6 year: 2022 ident: 10.1016/j.jhydrol.2022.128389_b15 article-title: A sequential pressure-based algorithm for data-driven leakage identification and model-based localization in water distribution networks publication-title: J. Water Resour. Plan. Manage. doi: 10.1061/(ASCE)WR.1943-5452.0001535 – start-page: 68 year: 2021 ident: 10.1016/j.jhydrol.2022.128389_b22 article-title: Autoencoder – volume: 53 start-page: 5:1 issue: 3 year: 2009 ident: 10.1016/j.jhydrol.2022.128389_b8 article-title: The energy-efficiency benefits of pump-scheduling optimization for potable water supplies publication-title: IBM J. Res. Dev. doi: 10.1147/JRD.2009.5429018 – year: 2022 ident: 10.1016/j.jhydrol.2022.128389_b35 – year: 2017 ident: 10.1016/j.jhydrol.2022.128389_b18 – volume: 144 issue: 12 year: 2018 ident: 10.1016/j.jhydrol.2022.128389_b16 article-title: Short-term water demand forecast based on deep learning method publication-title: J. Water Resour. Plan. Manage. doi: 10.1061/(ASCE)WR.1943-5452.0000992 – volume: 144 issue: 10 year: 2018 ident: 10.1016/j.jhydrol.2022.128389_b32 article-title: Deep-learning approach to the detection and localization of cyber-physical attacks on water distribution systems publication-title: J. Water Resour. Plan. Manage. doi: 10.1061/(ASCE)WR.1943-5452.0000983 – volume: 25 start-page: 165 issue: 2 year: 1983 ident: 10.1016/j.jhydrol.2022.128389_b30 article-title: Percentage points for a generalized ESD many-outlier procedure publication-title: Technometrics doi: 10.1080/00401706.1983.10487848 – year: 2017 ident: 10.1016/j.jhydrol.2022.128389_b28 – volume: 54 start-page: 1 year: 2021 ident: 10.1016/j.jhydrol.2022.128389_b5 article-title: A review on outlier/anomaly detection in time series data publication-title: ACM Comput. Surv. doi: 10.1145/3444690 – year: 2019 ident: 10.1016/j.jhydrol.2022.128389_b1 article-title: Committee machines for hourly water demand forecasting in water supply systems publication-title: Math. Probl. Eng. doi: 10.1155/2019/9765468 – volume: 35 year: 2021 ident: 10.1016/j.jhydrol.2022.128389_b20 article-title: An ensemble-learning-based method for short-term water demand forecasting publication-title: Water Resour. Manage. doi: 10.1007/s11269-021-02808-4 – volume: 29 start-page: 93 year: 2000 ident: 10.1016/j.jhydrol.2022.128389_b7 article-title: LOF: identifying density-based local outliers publication-title: ACM SIGMOD Rec. doi: 10.1145/335191.335388 – volume: 46 issue: 3 year: 2010 ident: 10.1016/j.jhydrol.2022.128389_b34 article-title: Statistical modeling of daily urban water consumption in Hong Kong: Trend, changing patterns, and forecast publication-title: Water Resour. Res. doi: 10.1029/2009WR008147 – volume: 11 start-page: 137 issue: 2 year: 2007 ident: 10.1016/j.jhydrol.2022.128389_b4 article-title: Automatic outlier detection for time series: an application to sensor data publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-006-0026-6 – year: 1990 ident: 10.1016/j.jhydrol.2022.128389_b14 – start-page: 377 year: 2012 ident: 10.1016/j.jhydrol.2022.128389_b11 – volume: 38 start-page: 344 year: 2006 ident: 10.1016/j.jhydrol.2022.128389_b33 article-title: An SPSS implementation of the nonrecursive outlier deletion procedure with shifting z score criterion (Van Seist & Jolicøur, 1994) publication-title: Behav. Res. Methods doi: 10.3758/BRM.38.2.344 – volume: 60 start-page: 265 year: 2014 ident: 10.1016/j.jhydrol.2022.128389_b29 article-title: Adaptive water demand forecasting for near real-time management of smart water distribution systems publication-title: Environ. Model. Softw. doi: 10.1016/j.envsoft.2014.06.016 – volume: 309 start-page: 532 year: 2017 ident: 10.1016/j.jhydrol.2022.128389_b6 article-title: Hybrid regression model for near real-time urban water demand forecasting publication-title: J. Comput. Appl. Math. doi: 10.1016/j.cam.2016.02.009 – volume: 88 start-page: 482 year: 2019 ident: 10.1016/j.jhydrol.2022.128389_b10 article-title: Online anomaly detection for long-term ECG monitoring using wearable devices publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2018.11.019 – volume: 142 year: 2015 ident: 10.1016/j.jhydrol.2022.128389_b3 article-title: Tailoring seasonal time series models to forecast short-term water demand publication-title: J. Water Resour. Plan. Manage. – volume: 13 start-page: 603 issue: 8 year: 2016 ident: 10.1016/j.jhydrol.2022.128389_b23 article-title: Classification evaluation publication-title: Nature Methods doi: 10.1038/nmeth.3945 – volume: 387 start-page: 141 issue: 1 year: 2010 ident: 10.1016/j.jhydrol.2022.128389_b17 article-title: Predictive models for forecasting hourly urban water demand publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2010.04.005 – volume: 599 year: 2021 ident: 10.1016/j.jhydrol.2022.128389_b31 article-title: A novel deep neural network architecture for real-time water demand forecasting publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2021.126353 – volume: 606 year: 2022 ident: 10.1016/j.jhydrol.2022.128389_b13 article-title: Short-term water demand forecast based on automatic feature extraction by one-dimensional convolution publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2022.127440 |
| SSID | ssj0000334 |
| Score | 2.4449258 |
| Snippet | Water demand forecasting (WDF) is essential for the design and optimal management of water distribution systems (WDS). Historical water demand data contribute... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 128389 |
| SubjectTerms | Anomaly detection Asymmetric encoder–decoder Autoencoder forests Hourly water demand hybrids hydrology Time series water distribution water pollution |
| Title | Unsupervised anomaly detection in hourly water demand data using an asymmetric encoder–decoder model |
| URI | https://dx.doi.org/10.1016/j.jhydrol.2022.128389 https://www.proquest.com/docview/2718376641 |
| Volume | 613 |
| WOSCitedRecordID | wos000862204200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-2707 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000334 issn: 0022-1694 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LbxMxELagRYIL4qm2PGQkbtGGrL27to8RKoIeKg6tFE4rxw-lUXcTZbO0ufEf-If8EsZr724olNIDl03WiSdW5svMeDKeD6G3nCuqU6siIZmJEilEJONMRkQRw60ynGvZkE2w42M-mYjPoWysaugEWFnyy0ux_K-qhjFQtjs6ewt1d0JhAJ6D0uEKaofrPyn-tKzqpbMAlXFtWBeFPN8MtFkb1dY1zkAMjF1ITw9euNy5qxQd1JU_sjiQ1aYoHNeWGrhGl67JSCiKoNo0955C55rQdrbRK9_bCeLXceF6MWgHvC7p8MWnXY_AJ9d97qDJ2sLDdiYCNrFtTdv2yYA486TFrXXNYrplH8EbUk8Z9Jvp9lmE-XDu1zh0nzDs3_9rq-wrLqwrLGxr1uZ5EJM7MbkXcxftEpYKsH2740-Hk6PeY1OatF3l3fr7k17v_rie62KYK968CVFOHqGHQQF47DHxGN0x5RN0P9DczzZPkd3GBg7YwB028FmJPTZwgw3ssYEdNnCDDZiDe2zggI0f374HVOAGFc_Q6YfDk_cfo0C0ESnCyDpizvBbpgUhCraP3KQ0zazRQsiM8JE1VMZkOpKxSolSUwFbUmMlU5wqK22S0udop1yUZg_hVIPHkPCyTUeJJWKqIWA1AAKpTZKM2D5K2q8uV6ELvSNDOc__qrp9NOymLX0blpsm8FYveYglfYyYA95umvqm1WMOttb9gSZLs6irnEAgBw45S-KD267nBXrQ_2Reop31qjav0D31dX1WrV4HQP4EBO2oNg |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Unsupervised+anomaly+detection+in+hourly+water+demand+data+using+an+asymmetric+encoder%E2%80%93decoder+model&rft.jtitle=Journal+of+hydrology+%28Amsterdam%29&rft.au=Yan%2C+Jieru&rft.au=Tao%2C+Tao&rft.date=2022-10-01&rft.issn=0022-1694&rft.volume=613&rft.spage=128389&rft_id=info:doi/10.1016%2Fj.jhydrol.2022.128389&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_jhydrol_2022_128389 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0022-1694&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0022-1694&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0022-1694&client=summon |