Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification
During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to pro...
Gespeichert in:
| Veröffentlicht in: | IEEE access Jg. 9; S. 78428 - 78451 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Piscataway
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 2169-3536, 2169-3536 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to process and analyze boreholes data to monitor digging operations and identify the boreholes shortcomings. This research study presents a boreholes data analysis architecture based on data and predictive analysis models to improve borehole efficiency, underground safety verification, and risk evaluation. The proposed architecture aims to process and analyze borehole data based on different hydrogeological characteristics using data and predictive analytics to enhance underground safety verification and planning of borehole resources. The proposed architecture is developed based on two modules; descriptive data analysis and predictive analysis modules. The descriptive analysis aims to utilize data and clustering analysis techniques to process and extract hidden hydrogeological characteristics from borehole history data. The predictive analysis aims to develop a bi-directional long short-term memory (BD-LSTM) to predict the boreholes depth to minimize the cost and time of the digging operations. Furthermore, different performance measures are utilized to evaluate the performance of the proposed clustering and regression models. Moreover, our proposed BD-LSTM model is evaluated and compared with conventional machine learning (ML) regression models. The <inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula> score of the proposed BD-LSTM is 0.989, which indicates that the proposed model accurately and precisely predicts boreholes depth compared to the conventional regression models. The experimental and comparative analysis results reveal the significance and effectiveness of the proposed borehole data analysis architecture. The experimental results will improve underground safety management and the efficiency of boreholes for future wells. |
|---|---|
| AbstractList | During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to process and analyze boreholes data to monitor digging operations and identify the boreholes shortcomings. This research study presents a boreholes data analysis architecture based on data and predictive analysis models to improve borehole efficiency, underground safety verification, and risk evaluation. The proposed architecture aims to process and analyze borehole data based on different hydrogeological characteristics using data and predictive analytics to enhance underground safety verification and planning of borehole resources. The proposed architecture is developed based on two modules; descriptive data analysis and predictive analysis modules. The descriptive analysis aims to utilize data and clustering analysis techniques to process and extract hidden hydrogeological characteristics from borehole history data. The predictive analysis aims to develop a bi-directional long short-term memory (BD-LSTM) to predict the boreholes depth to minimize the cost and time of the digging operations. Furthermore, different performance measures are utilized to evaluate the performance of the proposed clustering and regression models. Moreover, our proposed BD-LSTM model is evaluated and compared with conventional machine learning (ML) regression models. The <inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula> score of the proposed BD-LSTM is 0.989, which indicates that the proposed model accurately and precisely predicts boreholes depth compared to the conventional regression models. The experimental and comparative analysis results reveal the significance and effectiveness of the proposed borehole data analysis architecture. The experimental results will improve underground safety management and the efficiency of boreholes for future wells. During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to process and analyze boreholes data to monitor digging operations and identify the boreholes shortcomings. This research study presents a boreholes data analysis architecture based on data and predictive analysis models to improve borehole efficiency, underground safety verification, and risk evaluation. The proposed architecture aims to process and analyze borehole data based on different hydrogeological characteristics using data and predictive analytics to enhance underground safety verification and planning of borehole resources. The proposed architecture is developed based on two modules; descriptive data analysis and predictive analysis modules. The descriptive analysis aims to utilize data and clustering analysis techniques to process and extract hidden hydrogeological characteristics from borehole history data. The predictive analysis aims to develop a bi-directional long short-term memory (BD-LSTM) to predict the boreholes depth to minimize the cost and time of the digging operations. Furthermore, different performance measures are utilized to evaluate the performance of the proposed clustering and regression models. Moreover, our proposed BD-LSTM model is evaluated and compared with conventional machine learning (ML) regression models. The <tex-math notation="LaTeX">$R^{2}$ </tex-math> score of the proposed BD-LSTM is 0.989, which indicates that the proposed model accurately and precisely predicts boreholes depth compared to the conventional regression models. The experimental and comparative analysis results reveal the significance and effectiveness of the proposed borehole data analysis architecture. The experimental results will improve underground safety management and the efficiency of boreholes for future wells. During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction. Furthermore, boreholes depth can be considered one of the crucial factors in digging borehole efficiency. Therefore, a new solution is needed to process and analyze boreholes data to monitor digging operations and identify the boreholes shortcomings. This research study presents a boreholes data analysis architecture based on data and predictive analysis models to improve borehole efficiency, underground safety verification, and risk evaluation. The proposed architecture aims to process and analyze borehole data based on different hydrogeological characteristics using data and predictive analytics to enhance underground safety verification and planning of borehole resources. The proposed architecture is developed based on two modules; descriptive data analysis and predictive analysis modules. The descriptive analysis aims to utilize data and clustering analysis techniques to process and extract hidden hydrogeological characteristics from borehole history data. The predictive analysis aims to develop a bi-directional long short-term memory (BD-LSTM) to predict the boreholes depth to minimize the cost and time of the digging operations. Furthermore, different performance measures are utilized to evaluate the performance of the proposed clustering and regression models. Moreover, our proposed BD-LSTM model is evaluated and compared with conventional machine learning (ML) regression models. The [Formula Omitted] score of the proposed BD-LSTM is 0.989, which indicates that the proposed model accurately and precisely predicts boreholes depth compared to the conventional regression models. The experimental and comparative analysis results reveal the significance and effectiveness of the proposed borehole data analysis architecture. The experimental results will improve underground safety management and the efficiency of boreholes for future wells. |
| Author | Kim, Bong Wan Kim, Do-Hyeun Kim, Kwangsoo Ahmad, Rashid Iqbal, Naeem Khan, Anam Nawaz Rizwan, Atif |
| Author_xml | – sequence: 1 givenname: Naeem orcidid: 0000-0003-2749-6344 surname: Iqbal fullname: Iqbal, Naeem organization: Department of Computer Engineering, Jeju National University, Jeju, Republic of Korea – sequence: 2 givenname: Atif orcidid: 0000-0001-6669-8147 surname: Rizwan fullname: Rizwan, Atif organization: Department of Computer Engineering, Jeju National University, Jeju, Republic of Korea – sequence: 3 givenname: Anam Nawaz orcidid: 0000-0001-6260-5820 surname: Khan fullname: Khan, Anam Nawaz organization: Department of Computer Engineering, Jeju National University, Jeju, Republic of Korea – sequence: 4 givenname: Rashid surname: Ahmad fullname: Ahmad, Rashid organization: Department of Computer Science, COMSATS University Islamabad at Attock, Attock, Pakistan – sequence: 5 givenname: Bong Wan surname: Kim fullname: Kim, Bong Wan organization: Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea – sequence: 6 givenname: Kwangsoo surname: Kim fullname: Kim, Kwangsoo organization: Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea – sequence: 7 givenname: Do-Hyeun orcidid: 0000-0002-3457-2301 surname: Kim fullname: Kim, Do-Hyeun email: kimdh@jejunu.ac.kr organization: Department of Computer Engineering, Jeju National University, Jeju, Republic of Korea |
| BookMark | eNqFkUFr3DAQhU1IIWmaX5CLIOfdSpZtWceNu2kCKS1sk6sYS6NdLa6VSvJhoT--2jiEkkt10TDzvjcw72NxOvoRi-KK0SVjVH5edd16s1mWtGRLTlvORH1SnJeskQte8-b0n_qsuIxxT_Nrc6sW58WfGx9w5weM5AskIKsRhkN0kayC3rmEOk0ByQ1ENMSPpBummDC4cUtgNORHQON0cnnyzRscIrE-kPW4g1EfNY-jwbANfsraDVhMB_KUaes0HKFPxQcLQ8TL1_-ieLxd_-zuFg_fv953q4eFrkSbFshR97Vuja76vhKyLy1HW9coZK4otBw406zRKJrWMmBlb0otrLAcTFXV_KK4n32Nh716Du4XhIPy4NRLw4etgpCcHlAxKdoeWQ-lNZXgEkQjebawrKmltE32up69noP_PWFMau-nkK8WVZkvTDmVVGQVn1U6-BgD2retjKpjampOTR1TU6-pZUq-o7RLL5dKAdzwH_ZqZh0ivm2TFZdVw_hfc-ioxA |
| CODEN | IAECCG |
| CitedBy_id | crossref_primary_10_1109_ACCESS_2021_3133889 crossref_primary_10_1007_s41748_024_00516_8 crossref_primary_10_1186_s42408_023_00203_5 crossref_primary_10_3390_bioengineering11060533 crossref_primary_10_1109_ACCESS_2022_3211528 crossref_primary_10_1016_j_pce_2025_104093 crossref_primary_10_1109_ACCESS_2021_3111112 crossref_primary_10_1016_j_future_2023_11_036 crossref_primary_10_3390_math12243980 crossref_primary_10_3390_s21216972 |
| Cites_doi | 10.1016/j.apgeochem.2011.03.041 10.1016/j.petrol.2019.106682 10.1007/s10586-014-0413-9 10.1007/s10639-017-9645-7 10.1016/j.petrol.2018.12.013 10.1007/s12145-019-00381-4 10.1007/s10040-007-0165-1 10.1002/2016WR019933 10.3390/info10030103 10.1016/j.asoc.2014.02.002 10.3390/sym13030405 10.1016/j.cie.2018.08.018 10.1016/j.neucom.2018.12.093 10.1016/j.neucom.2018.09.082 10.1109/ACCESS.2019.2934179 10.1016/j.petrol.2018.08.083 10.1016/j.cageo.2015.05.019 10.1016/j.ijrmms.2019.03.010 10.1007/s00366-019-00715-2 10.1162/neco.1997.9.8.1735 10.1016/B978-0-12-815739-8.00013-4 10.3390/su11061678 10.1145/2996913.2996984 10.3390/su13052461 10.1029/2004WR003299 10.1109/ACCESS.2021.3060457 10.1109/ICECA.2017.8212735 10.3390/su8010087 10.1109/ACCESS.2018.2866364 10.1007/s11269-014-0810-0 10.1007/s10040-002-0196-6 10.1016/j.ins.2014.01.015 10.3390/w9100781 10.1016/j.energy.2020.119708 10.1016/j.oregeorev.2016.10.002 10.1016/j.ijinfomgt.2014.10.007 10.1109/ACCESS.2021.3049325 10.1109/MSP.2012.2205597 10.1016/j.compchemeng.2014.05.008 10.5120/8282-1278 10.5194/hess-20-2611-2016 10.3390/s20164410 10.2118/191141-PA 10.1016/j.apenergy.2017.12.051 10.1016/j.tust.2020.103450 10.1109/ACCESS.2020.3042598 10.1109/CloudTech.2015.7336964 10.1109/ICCCNT.2013.6726842 10.1109/ACCESS.2020.2990765 10.1016/j.ins.2020.04.009 10.1109/ACCESS.2020.2988173 10.1016/j.jngse.2018.06.006 10.1016/j.jbi.2017.05.002 10.1627/jpi.57.65 10.1016/j.petrol.2018.09.027 10.1016/j.coldregions.2017.08.009 10.1097/EDE.0b013e3181c30fb2 10.1109/JIOT.2020.3028743 10.1016/j.jngse.2017.02.019 10.1016/j.compchemeng.2017.08.009 10.1109/TPAMI.2013.50 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA |
| DOI | 10.1109/ACCESS.2021.3083175 |
| DatabaseName | IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Materials Research Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2169-3536 |
| EndPage | 78451 |
| ExternalDocumentID | oai_doaj_org_article_1978be1ba2fd4739a7693453f16599f6 10_1109_ACCESS_2021_3083175 9439461 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Korea Agency for Infrastructure Technology Advancement (KAIA) funderid: 10.13039/501100007694 – fundername: Energy Cloud Research and Development Program through the National Research Foundation of Korea (NRF) funderid: 10.13039/501100003725 – fundername: Ministry of Science, ICT grantid: 2019M3F2A1073387 – fundername: Ministry of Land, Infrastructure and Transport grantid: 20DCRU-B158151-01 funderid: 10.13039/501100003565 |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c478t-e3ecb5c8dc4bb479b2f3ef55e792f30a83a31c16ce768f1a12bd2c7f7f3ad4453 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 10 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000673783400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2169-3536 |
| IngestDate | Fri Oct 03 12:34:01 EDT 2025 Mon Jun 30 06:52:47 EDT 2025 Sat Nov 29 06:12:18 EST 2025 Tue Nov 18 21:42:24 EST 2025 Wed Aug 27 02:29:54 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0/legalcode |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c478t-e3ecb5c8dc4bb479b2f3ef55e792f30a83a31c16ce768f1a12bd2c7f7f3ad4453 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-3457-2301 0000-0003-2749-6344 0000-0001-6260-5820 0000-0001-6669-8147 |
| OpenAccessLink | https://doaj.org/article/1978be1ba2fd4739a7693453f16599f6 |
| PQID | 2536030907 |
| PQPubID | 4845423 |
| PageCount | 24 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_1978be1ba2fd4739a7693453f16599f6 crossref_citationtrail_10_1109_ACCESS_2021_3083175 ieee_primary_9439461 crossref_primary_10_1109_ACCESS_2021_3083175 proquest_journals_2536030907 |
| PublicationCentury | 2000 |
| PublicationDate | 20210000 2021-00-00 20210101 2021-01-01 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – year: 2021 text: 20210000 |
| PublicationDecade | 2020 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE access |
| PublicationTitleAbbrev | Access |
| PublicationYear | 2021 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref57 ref13 ref56 ref12 ref59 ref15 ref58 ref14 ref53 ref55 ref11 ref10 kelleher (ref28) 2020 ref17 reimann (ref54) 2011 ref16 ref19 ref18 widodo (ref52) 2016 ref51 ref50 iqbal (ref37) 2021; 13 ref46 ref45 ref48 ref42 ref41 ref44 ref43 kristjansson (ref47) 2016 ref49 (ref1) 2021 ref8 ref7 ref9 ref4 ref3 ref5 ref40 ref35 ref34 ref36 ref31 ref30 ref33 russom (ref6) 2011; 19 ref32 ref2 ref39 ref38 alasadi (ref66) 2017; 12 ref68 ref24 ref67 ref23 ref26 ref25 ref64 ref20 ref63 ref22 ref65 ref21 ref27 ref29 ref60 ref62 ref61 |
| References_xml | – ident: ref55 doi: 10.1016/j.apgeochem.2011.03.041 – ident: ref23 doi: 10.1016/j.petrol.2019.106682 – year: 2011 ident: ref54 publication-title: Statistical Data Analysis Explained Applied Environmental Statistics With R – ident: ref56 doi: 10.1007/s10586-014-0413-9 – ident: ref35 doi: 10.1007/s10639-017-9645-7 – ident: ref63 doi: 10.1016/j.petrol.2018.12.013 – ident: ref64 doi: 10.1007/s12145-019-00381-4 – ident: ref50 doi: 10.1007/s10040-007-0165-1 – ident: ref15 doi: 10.1002/2016WR019933 – ident: ref44 doi: 10.3390/info10030103 – ident: ref31 doi: 10.1016/j.asoc.2014.02.002 – ident: ref38 doi: 10.3390/sym13030405 – ident: ref65 doi: 10.1016/j.cie.2018.08.018 – ident: ref34 doi: 10.1016/j.neucom.2018.12.093 – ident: ref24 doi: 10.1016/j.neucom.2018.09.082 – ident: ref46 doi: 10.1109/ACCESS.2019.2934179 – ident: ref30 doi: 10.1016/j.petrol.2018.08.083 – ident: ref11 doi: 10.1016/j.cageo.2015.05.019 – ident: ref48 doi: 10.1016/j.ijrmms.2019.03.010 – ident: ref58 doi: 10.1007/s00366-019-00715-2 – ident: ref22 doi: 10.1162/neco.1997.9.8.1735 – ident: ref45 doi: 10.1016/B978-0-12-815739-8.00013-4 – ident: ref5 doi: 10.3390/su11061678 – ident: ref36 doi: 10.1145/2996913.2996984 – volume: 13 start-page: 2461 year: 2021 ident: ref37 article-title: Towards mountain fire safety using fire spread predictive analytics and mountain fire containment in IoT environment publication-title: Sustainability doi: 10.3390/su13052461 – ident: ref49 doi: 10.1029/2004WR003299 – ident: ref40 doi: 10.1109/ACCESS.2021.3060457 – ident: ref67 doi: 10.1109/ICECA.2017.8212735 – ident: ref7 doi: 10.3390/su8010087 – ident: ref42 doi: 10.1109/ACCESS.2018.2866364 – volume: 12 start-page: 4102 year: 2017 ident: ref66 article-title: Review of data preprocessing techniques in data mining publication-title: J Eng Appl Sci – ident: ref29 doi: 10.1007/s11269-014-0810-0 – ident: ref51 doi: 10.1007/s10040-002-0196-6 – ident: ref10 doi: 10.1016/j.ins.2014.01.015 – ident: ref57 doi: 10.3390/w9100781 – ident: ref25 doi: 10.1016/j.energy.2020.119708 – ident: ref53 doi: 10.1016/j.oregeorev.2016.10.002 – year: 2016 ident: ref52 article-title: Application of clustering system to analyze geological, geotechnical and hydrogeological data base according to HC-system approach publication-title: Proc 9th Asian Rock Mech Symp – volume: 19 start-page: 1 year: 2011 ident: ref6 article-title: Big data analytics publication-title: TDWI Best Practices Report TDWI – ident: ref3 doi: 10.1016/j.ijinfomgt.2014.10.007 – ident: ref19 doi: 10.1109/ACCESS.2021.3049325 – ident: ref17 doi: 10.1109/MSP.2012.2205597 – ident: ref27 doi: 10.1016/j.compchemeng.2014.05.008 – ident: ref43 doi: 10.5120/8282-1278 – ident: ref12 doi: 10.5194/hess-20-2611-2016 – ident: ref18 doi: 10.3390/s20164410 – ident: ref61 doi: 10.2118/191141-PA – ident: ref21 doi: 10.1016/j.apenergy.2017.12.051 – ident: ref2 doi: 10.1016/j.tust.2020.103450 – ident: ref14 doi: 10.1109/ACCESS.2020.3042598 – ident: ref4 doi: 10.1109/CloudTech.2015.7336964 – ident: ref9 doi: 10.1109/ICCCNT.2013.6726842 – ident: ref8 doi: 10.1109/ACCESS.2020.2990765 – ident: ref41 doi: 10.1016/j.ins.2020.04.009 – ident: ref13 doi: 10.1109/ACCESS.2020.2988173 – start-page: 1 year: 2016 ident: ref47 article-title: Use of historic data to improve drilling efficiency: A pattern recognition method and trial results publication-title: Proceedings of IADC/SPE Drilling Conference and Exhibition – ident: ref60 doi: 10.1016/j.jngse.2018.06.006 – ident: ref16 doi: 10.1016/j.jbi.2017.05.002 – ident: ref59 doi: 10.1627/jpi.57.65 – ident: ref62 doi: 10.1016/j.petrol.2018.09.027 – ident: ref32 doi: 10.1016/j.coldregions.2017.08.009 – ident: ref68 doi: 10.1097/EDE.0b013e3181c30fb2 – year: 2021 ident: ref1 publication-title: Ministry of Environment to Carry Out Detailed Investigation of Old Sewage Pipes Within the Year – ident: ref39 doi: 10.1109/JIOT.2020.3028743 – ident: ref33 doi: 10.1016/j.jngse.2017.02.019 – ident: ref26 doi: 10.1016/j.compchemeng.2017.08.009 – year: 2020 ident: ref28 publication-title: Fundamentals of machine learning for predictive data analytics algorithms worked examples and case studies – ident: ref20 doi: 10.1109/TPAMI.2013.50 |
| SSID | ssj0000816957 |
| Score | 2.26389 |
| Snippet | During the last decade, substantial resources have been invested to exploit massive amounts of boreholes data collected through groundwater extraction.... |
| SourceID | doaj proquest crossref ieee |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 78428 |
| SubjectTerms | Analytical models Boreholes boreholes data Cluster analysis Clustering Data analysis data and predictive analytics Data mining Data models deep learning Efficiency Groundwater Hydrogeology Long short term memory Machine learning Modules Performance evaluation Prediction models Predictive analytics Predictive models Regression models Risk assessment ROP Safety management Verification Water resources |
| SummonAdditionalLinks | – databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LaxsxEBZp6KE9JG3TUjdp0aHHbGKtVq-j7Sb0FAJ9kJvQ6kELZl3sdSCQH98ZrbwYWgq9CaERWj5pRyNpvo-Qj0qAVwmsqRSXvmoghKi0Q8LLxLRSHhx4DFlsQt3c6Ls7c3tAzsdcmBhjfnwWL7CY7_LDym_xqOzSYBonxjpPlJJDrtZ4noICEkaoQizEpuZytljAN0AIWLMLjoJa-JZwz_lkjv4iqvLHnzi7l-vj_xvYC3JUtpF0NuD-khzE7hV5vkcueEIe59ALqt9u6CfXO7qjH6GzvbsDOgcvFuiqo4vlFjkTwJS6LtDbNd7gIGoU5dKWGwq7W3rV_UB-DmiT9ZIwJQTafnEp9g_0O1incgT4mny7vvq6-FwVrYXKN0r3VeTRt8Lr4Ju2bZRp68RjEiIqA6Wp09xx5pn0EeKTxByr21B7lVTiLjSN4G_IYbfq4ltCVZRgJSREKq7hWmrpNeZoQwVPEPJNSL0DwfpCRI56GEubA5KpsQNyFpGzBbkJOR-Nfg08HP9uPkd0x6ZIop0rADZb1qRlEEG3kbWuTqFR3DjUhYRPSUwKYxIM9AShHjspKE_I2W6u2LLgN7YWXOJt1VS9-7vVKXmGAxxOb87IYb_exvfkqb_vf27WH_Jc_g28MfGK priority: 102 providerName: IEEE |
| Title | Boreholes Data Analysis Architecture Based on Clustering and Prediction Models for Enhancing Underground Safety Verification |
| URI | https://ieeexplore.ieee.org/document/9439461 https://www.proquest.com/docview/2536030907 https://doaj.org/article/1978be1ba2fd4739a7693453f16599f6 |
| Volume | 9 |
| WOSCitedRecordID | wos000673783400001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Ni9QwFA-y7EEP4rqKo-uSg0frNs33cWacxYvLgh_sLaRpgsLQkZnuwoL4t_temhkKgl68lBJe2ibvNS8vyfv9CHmjJXiVjolKcxUqASFEZTwCXiZmtA7gwGOXySb01ZW5ubHXE6ovPBM2wgOPHXfBIMxpI2t9kzqhufVI3ickT0xJa1MG2661nQRTeQw2TFmpC8wQq-3FfLmEFkFA2LB3HOm18GThxBVlxP5CsfLHuJydzeUT8rjMEul8_LoT8iD2T8mjCXbgKfm52Gwjktvu6Hs_eLpHF6HzydYAXYCT6uimp8v1LUIiQFXq-45eb3GDBpVCkQ1tvaMweaWr_hvCb4BMpkPCjA-Q_eRTHO7pV6idygrfM_LlcvV5-aEqVApVENoMVeQxtDKYLoi2Fdq2TeIxSRm1hbvaG-45C0yFCOFHYp41bdcEnXTivhPQ3c_JUb_p4wtCdVRQSyoIRLzgRhkVDKZgQwFPENHNSLPvVRcKzjjSXaxdjjdq60ZVOFSFK6qYkbeHSj9GmI2_iy9QXQdRxMjOBWA5rliO-5flzMgpKvvwEItJworNyNle-a78zzvXSK5wM6rWL__Hq1-Rh9iccSnnjBwN29v4mhyHu-H7bnueTRmuH3-tznNC4m9PPvXK |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1BaxQxFA6lFbSHVq3i2qo5eOy0k0kySY67a0vFuhSs0lvIZBIqLLOyO1sQ_PG-N5MdFhTBWwh5IcOXzMtL8r6PkPdKglepmcgUL30mIITItEPCy8i0Uh4ceKg7sQk1m-m7O3OzQ06HXJgQQvf4LJxhsbvLrxd-jUdl5wbTODHW2ZNCFHmfrTWcqKCEhJEqUQux3JyPp1P4CggCC3bGUVILXxNuuZ-OpT_JqvzxL-4czOXh_w3tKTlIG0k67pF_RnZC85zsb9ELHpFfE-gF9W9X9INrHd0QkNDx1u0BnYAfq-miodP5GlkTwJS6pqY3S7zDQdwoCqbNVxT2t_SiuUeGDmjTKSZhUgi0_eJiaH_Sb2Ad0yHgC_L18uJ2epUltYXMC6XbLPDgK-l17UVVCWWqIvIQpQzKQCl3mjvOPCt9gAglMseKqi68iipyVwsh-Uuy2yya8IpQFUqwkiXEKk5wXerSa8zShgoeIegbkWIDgvWJihwVMea2C0lyY3vkLCJnE3IjcjoY_eiZOP7dfILoDk2RRrurANhsWpWWQQxdBVa5ItZCceNQGRI-JbJSGhNhoEcI9dBJQnlETjZzxaYlv7KF5CXeV-Xq9d-t3pHHV7efr-31x9mnY_IEB9uf5ZyQ3Xa5Dm_II__Qfl8t33bz-jdsLvTR |
| 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=Boreholes+Data+Analysis+Architecture+Based+on+Clustering+and+Prediction+Models+for+Enhancing+Underground+Safety+Verification&rft.jtitle=IEEE+access&rft.au=Iqbal%2C+Naeem&rft.au=Rizwan%2C+Atif&rft.au=Khan%2C+Anam+Nawaz&rft.au=Ahmad%2C+Rashid&rft.date=2021&rft.issn=2169-3536&rft.eissn=2169-3536&rft.volume=9&rft.spage=78428&rft.epage=78451&rft_id=info:doi/10.1109%2FACCESS.2021.3083175&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_ACCESS_2021_3083175 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon |