Multivariate weather anomaly detection using DBSCAN clustering algorithm
Weather is highly influential for human life. Weather anomalies describe conditions that are out of the ordinary and need special attention because they can affect various aspects of human life both socially and economically and also can cause natural disasters. Anomaly detection aims to get rid of...
Gespeichert in:
| Veröffentlicht in: | Journal of physics. Conference series Jg. 1869; H. 1; S. 12077 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Bristol
IOP Publishing
01.04.2021
|
| Schlagworte: | |
| ISSN: | 1742-6588, 1742-6596 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Weather is highly influential for human life. Weather anomalies describe conditions that are out of the ordinary and need special attention because they can affect various aspects of human life both socially and economically and also can cause natural disasters. Anomaly detection aims to get rid of unwanted data (noise, erroneous data, or unwanted data) or to study the anomaly phenomenon itself (unusual but interesting). In the absence of an anomaly-labeled dataset, an unsupervised Machine Learning approach can be utilized to detect or label the anomalous data. This research uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to separate between normal and anomalous weather data by considering multiple weather variables. Then, PCA is used to visualize the clusters. The experimental result had demonstrated that DBSCAN is capable of identifying peculiar data points that are deviating from the ‘normal’ data distribution. |
|---|---|
| AbstractList | Weather is highly influential for human life. Weather anomalies describe conditions that are out of the ordinary and need special attention because they can affect various aspects of human life both socially and economically and also can cause natural disasters. Anomaly detection aims to get rid of unwanted data (noise, erroneous data, or unwanted data) or to study the anomaly phenomenon itself (unusual but interesting). In the absence of an anomaly-labeled dataset, an unsupervised Machine Learning approach can be utilized to detect or label the anomalous data. This research uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to separate between normal and anomalous weather data by considering multiple weather variables. Then, PCA is used to visualize the clusters. The experimental result had demonstrated that DBSCAN is capable of identifying peculiar data points that are deviating from the ‘normal’ data distribution. |
| Author | Anwar, M T Wibisono, S Supriyanto, A Amin, I H A |
| Author_xml | – sequence: 1 givenname: S surname: Wibisono fullname: Wibisono, S – sequence: 2 givenname: M T surname: Anwar fullname: Anwar, M T – sequence: 3 givenname: A surname: Supriyanto fullname: Supriyanto, A – sequence: 4 givenname: I H A surname: Amin fullname: Amin, I H A |
| BookMark | eNqFkE9PwzAMxSM0JLbBZ6AS59I4bZr0wGGMP0MacGD3yG3TLVPXjiQF7dvTamgHLvhiy37vWfpNyKhpG03INdBboFJGIBIWpjxLI5BpFkFEgVEhzsj4dBmdZikvyMS5LaVxX2JMFq9d7c0XWoNeB98a_UbbAJt2h_UhKLXXhTdtE3TONOvg4f5jPnsLirpzXtthg_W6tcZvdpfkvMLa6avfPiWrp8fVfBEu359f5rNlWLAkE6HIilhrwCqRnJdlCSlymTMsKgZpzBBznjBkgia0yvNU97Ik4znL05jLWMdTcnOM3dv2s9POq23b2ab_qBgHDoJBDL1KHFWFbZ2zulJ7a3ZoDwqoGqipgYca2KiBmgJ1pNY77_44C-NxQOAtmvpf_w-FcXSH |
| CitedBy_id | crossref_primary_10_1007_s10115_024_02079_6 crossref_primary_10_3390_rs17101729 crossref_primary_10_3233_AIC_230064 crossref_primary_10_3390_electronics12153213 crossref_primary_10_1007_s10584_022_03444_6 crossref_primary_10_1002_spy2_212 crossref_primary_10_1145_3689036 crossref_primary_10_1186_s12982_024_00245_3 crossref_primary_10_3390_computers12120253 crossref_primary_10_1109_ACCESS_2023_3253022 crossref_primary_10_3233_JIFS_201191 crossref_primary_10_3390_s25154757 crossref_primary_10_1145_3639273 crossref_primary_10_1029_2023EA003014 crossref_primary_10_1088_1402_4896_ad351a crossref_primary_10_3390_app15179391 crossref_primary_10_1002_cpe_6707 |
| Cites_doi | 10.1016/j.procs.2015.08.220 10.1186/s40537-017-0077-4 10.1016/j.eij.2015.11.004 10.1016/j.patcog.2017.09.037 10.1145/3068335 10.1088/1757-899X/835/1/012036 10.1186/s12874-019-0737-5 10.1007/s10742-017-0172-1 |
| ContentType | Journal Article |
| Copyright | 2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION 8FD 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO H8D HCIFZ L7M P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS |
| DOI | 10.1088/1742-6596/1869/1/012077 |
| DatabaseName | CrossRef Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central Aerospace Database SciTech Premium Collection Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Collection ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China |
| DatabaseTitle | CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences Aerospace Database ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic Advanced Technologies Database with Aerospace ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: PIMPY name: ProQuest Publicly Available Content url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Physics |
| EISSN | 1742-6596 |
| ExternalDocumentID | 10_1088_1742_6596_1869_1_012077 |
| Genre | Conference Proceeding |
| GroupedDBID | 1JI 29L 2WC 4.4 5B3 5GY 5PX 5VS 7.Q AAJIO AAJKP AAYXX ABHWH ACAFW ACHIP AEFHF AEINN AEJGL AFFHD AFKRA AFYNE AIYBF AKPSB ALMA_UNASSIGNED_HOLDINGS ARAPS ASPBG ATQHT AVWKF AZFZN BENPR BGLVJ CCPQU CEBXE CITATION CJUJL CRLBU CS3 DU5 E3Z EBS EDWGO EQZZN F5P FRP GX1 HCIFZ HH5 IJHAN IOP IZVLO J9A KQ8 LAP N5L N9A O3W OK1 OVT P2P PHGZM PHGZT PIMPY PJBAE PQGLB RIN RNS RO9 ROL SY9 T37 TR2 W28 XSB ~02 8FD 8FE 8FG ABUWG AZQEC DWQXO H8D L7M P62 PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c2497-79c3ee1af4855ddd16a58b2acf21632aab542a27040fbb6e485495b2b63583e3 |
| IEDL.DBID | BENPR |
| ISSN | 1742-6588 |
| IngestDate | Wed Aug 13 06:18:53 EDT 2025 Tue Nov 18 20:49:13 EST 2025 Sat Nov 29 01:48:05 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2497-79c3ee1af4855ddd16a58b2acf21632aab542a27040fbb6e485495b2b63583e3 |
| Notes | ObjectType-Conference Proceeding-1 SourceType-Scholarly Journals-1 content type line 14 |
| OpenAccessLink | https://www.proquest.com/docview/2515172131?pq-origsite=%requestingapplication% |
| PQID | 2515172131 |
| PQPubID | 4998668 |
| ParticipantIDs | proquest_journals_2515172131 crossref_primary_10_1088_1742_6596_1869_1_012077 crossref_citationtrail_10_1088_1742_6596_1869_1_012077 |
| PublicationCentury | 2000 |
| PublicationDate | 20210401 |
| PublicationDateYYYYMMDD | 2021-04-01 |
| PublicationDate_xml | – month: 04 year: 2021 text: 20210401 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Bristol |
| PublicationPlace_xml | – name: Bristol |
| PublicationTitle | Journal of physics. Conference series |
| PublicationYear | 2021 |
| Publisher | IOP Publishing |
| Publisher_xml | – name: IOP Publishing |
| References | Bansal (JPCS_1869_1_012077bib10) 2016 Piruthevi (JPCS_1869_1_012077bib7) 2017 Agrawal (JPCS_1869_1_012077bib3) 2015; 60 Kaur (JPCS_1869_1_012077bib2) 2016; 17 Domingues (JPCS_1869_1_012077bib4) 2018; 74 Sunderland (JPCS_1869_1_012077bib5) 2019; 19 Piruthevi (JPCS_1869_1_012077bib8) 2017 Saneja (JPCS_1869_1_012077bib9) 2018 Winarno (JPCS_1869_1_012077bib15) 2019 Ester (JPCS_1869_1_012077bib16) 1996; 96 Aggarwal (JPCS_1869_1_012077bib1) 2016 Majumdar (JPCS_1869_1_012077bib13) 2017; 4 Anwar (JPCS_1869_1_012077bib11) 2019; 1 Zuliarso (JPCS_1869_1_012077bib14) 2020; 835 Schubert (JPCS_1869_1_012077bib12) 2017; 42 Bauder (JPCS_1869_1_012077bib6) 2017; 17 |
| References_xml | – year: 2016 ident: JPCS_1869_1_012077bib1 – volume: 60 start-page: 708 year: 2015 ident: JPCS_1869_1_012077bib3 article-title: Survey on anomaly detection using data mining techniques publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.08.220 – start-page: 1 year: 2017 ident: JPCS_1869_1_012077bib7 article-title: Filtering of anomalous weather events and tracing their behavior – volume: 4 start-page: 20 year: 2017 ident: JPCS_1869_1_012077bib13 article-title: Analysis of agriculture data using data mining techniques: application of big data publication-title: J. Big data doi: 10.1186/s40537-017-0077-4 – volume: 17 start-page: 199 year: 2016 ident: JPCS_1869_1_012077bib2 article-title: A survey of data mining and social network analysis based anomaly detection techniques publication-title: Egypt. informatics J. doi: 10.1016/j.eij.2015.11.004 – volume: 74 start-page: 406 year: 2018 ident: JPCS_1869_1_012077bib4 article-title: A comparative evaluation of outlier detection algorithms: Experiments and analyses publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.09.037 – volume: 42 start-page: 1 year: 2017 ident: JPCS_1869_1_012077bib12 article-title: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN publication-title: ACM Trans. Database Syst. doi: 10.1145/3068335 – start-page: 373 year: 2016 ident: JPCS_1869_1_012077bib10 article-title: Outlier detection: applications and techniques in data mining – start-page: 1 year: 2017 ident: JPCS_1869_1_012077bib8 article-title: Filtering of anomalous weather events over the region of Tamil Nadu – start-page: 321 year: 2018 ident: JPCS_1869_1_012077bib9 article-title: A Hybrid Approach for Outlier Detection in Weather Sensor Data – volume: 835 start-page: 12036 year: 2020 ident: JPCS_1869_1_012077bib14 article-title: Detecting Hoaxes in Indonesian News Using TF/TDM and K Nearest Neighbor publication-title: IOP Conference Series: Materials Science and Engineering doi: 10.1088/1757-899X/835/1/012036 – volume: 96 start-page: 226 year: 1996 ident: JPCS_1869_1_012077bib16 article-title: A density-based algorithm for discovering clusters in large spatial databases with noise publication-title: Kdd – volume: 19 start-page: 102 year: 2019 ident: JPCS_1869_1_012077bib5 article-title: The utility of multivariate outlier detection techniques for data quality evaluation in large studies: an application within the ONDRI project publication-title: BMC Med. Res. Methodol. doi: 10.1186/s12874-019-0737-5 – volume: 17 start-page: 256 year: 2017 ident: JPCS_1869_1_012077bib6 article-title: Multivariate outlier detection in medicare claims payments applying probabilistic programming methods publication-title: Heal. Serv. Outcomes Res. Methodol. doi: 10.1007/s10742-017-0172-1 – volume: 1 year: 2019 ident: JPCS_1869_1_012077bib11 article-title: Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation publication-title: Adv. Sustain. Sci. Eng. Technol. – start-page: 301 year: 2019 ident: JPCS_1869_1_012077bib15 article-title: Attendance System Based on Face Recognition System Using CNN-PCA Method and Realtime Camera |
| SSID | ssj0033337 |
| Score | 2.431235 |
| Snippet | Weather is highly influential for human life. Weather anomalies describe conditions that are out of the ordinary and need special attention because they can... |
| SourceID | proquest crossref |
| SourceType | Aggregation Database Enrichment Source Index Database |
| StartPage | 12077 |
| SubjectTerms | Algorithms Anomalies Clustering Data points Machine learning Meteorological data Natural disasters Physics |
| Title | Multivariate weather anomaly detection using DBSCAN clustering algorithm |
| URI | https://www.proquest.com/docview/2515172131 |
| Volume | 1869 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIOP databaseName: Institute of Physics Open Access Journal Titles customDbUrl: eissn: 1742-6596 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0033337 issn: 1742-6588 databaseCode: O3W dateStart: 20040101 isFulltext: true titleUrlDefault: http://iopscience.iop.org/ providerName: IOP Publishing – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Collection customDbUrl: eissn: 1742-6596 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0033337 issn: 1742-6588 databaseCode: P5Z dateStart: 20040801 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1742-6596 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0033337 issn: 1742-6588 databaseCode: BENPR dateStart: 20040801 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content customDbUrl: eissn: 1742-6596 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0033337 issn: 1742-6588 databaseCode: PIMPY dateStart: 20040801 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV07T8MwED5BAYmFN6K8lIHVKrGT2JkQT8FAFQEDsFh-FZDaFGgL4t9zThwQCwxkSpxYifLZ97C_uwPYc8m-ya0wJGOWkwTPSa4ZIzplqTDOGk1VVWyCd7vi9jYvwoLbKNAqG5lYCWo7NH6NvIN6OPXuCosPnl-Irxrld1dDCY1pmPGZypIWzByddourRhYzPHgdEkkJ6lrRMLzQ7QttedbxVZk6cceHkXL-Uz_9FM-Vzjlb_O_XLsFCsDajw3p4LMOUK1dgrmJ9mtEqnFfht2_oLqPFGb3X1mCkyuFA9T8i68YVT6uMPDn-ITo5uj4-7EamP_HJFXyL6j_gS8ePgzW4OTu9OT4nobQCMehvccJzw5yLVc_nhrHWxplKBcJiehQNNKqUThOqKMcp3tM6c_gYelKaarRPBHNsHVrlsHQbENlUoYnIdGKtThxjimuuUGow1hOaG9qGrPmf0oS04776RV9W299CSA-E9EBID4SMZQ1EG_a_Oj7XmTf-7rLdoCHDVBzJbyg2f7-9BfPUE1YqWs42tMavE7cDs-Zt_DR63Q0jaxemi_Qer4qLy-LuE5vj1WE |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB7BAmov5VmVlocPcLSW2EnsHCrEU7sCViuxBzhZfi2ttGQpG0D8qP5HxnmAuMCJAzlFSRzF-SbziL-ZAdjy8Y7NnLQ05U7QGPdpZjinJuGJtN5Zw3TZbEL0evLiIutPwf8mFybQKhudWCpqN7bhH3kb7XASwhUe7d78o6FrVFhdbVpoVGJx4h8fMGSb_O4eIr7bjB0fDQ46tO4qQC2GGoKKzHLvIz0MZVGcc1GqE4lPZIcMfROmtUlipplA6R4ak3q8DIMIwwyaZsk9x9tOw0yMsi5bMNPvnvUvG9XPcRNVBiajaNplQyjDKLM-lqXt0ASqHbVD1qoQr83ha2tQmrjj-U_2chbgW-1Lk71K-BdhyudLMFdyWu1kGTplcvG9xk-s8OSh8nWJzsfXevRInC9KFlpOAvX_ihzunx_s9Ygd3YXSEeGIHl3hHIs_1ysw-IhZfIdWPs79DyAu0egAcxM7Z2LPuRZGaNSJnA-lEZatQtrAp2xdVD309hipcnFfShVwVwF3FXBXkapwX4Wd54E3VV2R94esNeCrWtFM1AvyP98-vQlfOoOzU3Xa7Z38gq8sUHNKAtIatIrbO78Os_a--Du53aiFmoD6YEl5AgIDLhk |
| 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=Multivariate+weather+anomaly+detection+using+DBSCAN+clustering+algorithm&rft.jtitle=Journal+of+physics.+Conference+series&rft.au=Wibisono%2C+S&rft.au=Anwar%2C+M+T&rft.au=Supriyanto%2C+A&rft.au=Amin%2C+I+H+A&rft.date=2021-04-01&rft.issn=1742-6588&rft.eissn=1742-6596&rft.volume=1869&rft.issue=1&rft.spage=12077&rft_id=info:doi/10.1088%2F1742-6596%2F1869%2F1%2F012077&rft.externalDBID=n%2Fa&rft.externalDocID=10_1088_1742_6596_1869_1_012077 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1742-6588&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1742-6588&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1742-6588&client=summon |