A Robust Estimation of Blasting-Induced Flyrock Using Machine Learning Decision Tree Algorithms: Random Forest, Gradient Boosting Machine, and XGBoost

In surface mining, blasting operations may have harmful effects on the nearby environment. One of the several undesirable scenarios that should be avoided is flyrock, which is generated during the rock fragmentation process. This paper presents three machine learning decision tree algorithms that we...

Full description

Saved in:

Bibliographic Details
Published in:	Mining, metallurgy & exploration Vol. 42; no. 3; pp. 1609 - 1624
Main Authors:	Mohammed, Bouhannache, Hamza, Cheniti
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.06.2025
Subjects:	Engineering Materials Engineering Metallic Materials Mineral Resources Random forest Gradient boosting machine Decision trees Blasting Flyrock Extreme gradient boosting machines
ISSN:	2524-3462, 2524-3470
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	In surface mining, blasting operations may have harmful effects on the nearby environment. One of the several undesirable scenarios that should be avoided is flyrock, which is generated during the rock fragmentation process. This paper presents three machine learning decision tree algorithms that were implemented to predict blasting-induced flyrock. The distinctively developed models are called random forest (RF), gradient boosting machines (GBM), and extreme gradient boosting machines (XGBoost). Accordingly, 61 blasting events were selected from three limestone quarry predestined for cement production in Algeria. Ten controllable parameters are used as the input variables, and the measured flyrock distance is the output variable. The accuracy criteria, including determination coefficient ( R 2 ), root-mean-square error (RMSE), and the variance accounted for (VAF) mean absolute error (MAE), were used for the evaluation of the performance of models. Furthermore, the datasets were also analyzed using the Shapley Additive Explanations (SHAP) method to find out the importance and contribution of parameters to flyrock distance prediction. As a result, the XGBoost and GBM models have demonstrated a comparable high degree of accuracy, with R 2 (0.9935, 1), RMSE (2.04, 0.041), VAF (99.35, 100), and MAE (0.43, 0.016) for the XGBoost model. Followed by nearly the same results from the GBM model with R 2 (0.9935, 1), RMSE (2.05, 0.49), VAF (99.35, 100), and MAE (0.05, 0.049) for both training and test data, while the RF model revealed inferior performance with R 2 (0.86, 0.91), RMSE (9.59, 5.35), VAF (86, 91), and MAE (7.92, 4.42). In addition, the sensitivity analysis indicates that the stemming (ST) parameter had the most significant impact on blasting-induced flyrock prediction.
AbstractList	In surface mining, blasting operations may have harmful effects on the nearby environment. One of the several undesirable scenarios that should be avoided is flyrock, which is generated during the rock fragmentation process. This paper presents three machine learning decision tree algorithms that were implemented to predict blasting-induced flyrock. The distinctively developed models are called random forest (RF), gradient boosting machines (GBM), and extreme gradient boosting machines (XGBoost). Accordingly, 61 blasting events were selected from three limestone quarry predestined for cement production in Algeria. Ten controllable parameters are used as the input variables, and the measured flyrock distance is the output variable. The accuracy criteria, including determination coefficient ( R 2 ), root-mean-square error (RMSE), and the variance accounted for (VAF) mean absolute error (MAE), were used for the evaluation of the performance of models. Furthermore, the datasets were also analyzed using the Shapley Additive Explanations (SHAP) method to find out the importance and contribution of parameters to flyrock distance prediction. As a result, the XGBoost and GBM models have demonstrated a comparable high degree of accuracy, with R 2 (0.9935, 1), RMSE (2.04, 0.041), VAF (99.35, 100), and MAE (0.43, 0.016) for the XGBoost model. Followed by nearly the same results from the GBM model with R 2 (0.9935, 1), RMSE (2.05, 0.49), VAF (99.35, 100), and MAE (0.05, 0.049) for both training and test data, while the RF model revealed inferior performance with R 2 (0.86, 0.91), RMSE (9.59, 5.35), VAF (86, 91), and MAE (7.92, 4.42). In addition, the sensitivity analysis indicates that the stemming (ST) parameter had the most significant impact on blasting-induced flyrock prediction.
Author	Mohammed, Bouhannache Hamza, Cheniti
Author_xml	– sequence: 1 givenname: Bouhannache surname: Mohammed fullname: Mohammed, Bouhannache organization: Department of Mining Engineering, Metallurgy, and Materials, National Higher School of Technology and Engineering, Laboratory of Mining, Metallurgy and Materials L3M, Mineral Resources, Geohazards and Environment, National Higher School of Technology and Engineering – sequence: 2 givenname: Cheniti orcidid: 0000-0001-6544-2537 surname: Hamza fullname: Hamza, Cheniti email: h.cheniti@ensti-annaba.dz organization: Department of Mining Engineering, Metallurgy, and Materials, National Higher School of Technology and Engineering, Environmental Research Center (CRE)
BookMark	eNp9kN1OAjEQhRuDiYi8gFd9AKptt_uDd4CAJBgTAol3Tbc7C6tLa9rlghfhee2CeukkzUxP5pw23y3qGGsAoXtGHxil6aMXXCSMUB4TyniSEnGFujzmgkQipZ2_OeE3qO99lVPBGaOpiLroNMIrmx98g6e-qfaqqazBtsTjWoW72ZKFKQ4aCjyrj87qT7zxQcWvSu8qA3gJyplWeAZd-da7dgB4VG-tq5rd3j_hlTKF3eOZdeCbAZ47VVRgGjy29vzCb9YAh0X8Pj_rd-i6VLWH_k_voc1sup68kOXbfDEZLYnmKWtImZQq0_FQqHJY5AAqA162R6gYeKxzyiiILINQadASziIOOTANeSzUMOohfsnVznrvoJRfLlBwR8mobOHKC1wZ4MozXCmCKbqYfFg2W3Dywx6cCf_8z_UN0KOBlg
Cites_doi	10.1016/B978-0-12-809633-8.20349-X 10.1007/s10064-014-0588-6 10.1007/s11053-023-10259-4 10.1016/j.enbuild.2017.11.039 10.1007/s00366-020-01231-4 10.1007/BF00058655 10.48550/arxiv.1908.08474 10.1016/j.jrmge.2021.07.007 10.1007/s11053-020-09710-7 10.1023/A:1010933404324 10.1007/s11053-019-09611-4 10.1007/s11749-016-0481-7 10.1016/j.jrmge.2014.07.003 10.1007/s11053-020-09794-1 10.1007/s00603-022-02866-z 10.1007/s00366-015-0402-5 10.3390/su15108424 10.1016/j.ssci.2005.07.006 10.1016/j.gsf.2020.11.005 10.1016/j.trc.2015.02.019 10.1007/s10706-018-0457-3 10.1007/s00521-015-1889-9 10.48550/arxiv.2202.05594 10.1007/978-981-16-8237-7 10.1007/s00366-020-01105-9 10.1016/j.jsr.2003.07.003 10.1016/j.enggeo.2007.10.009 10.1007/s00366-021-01393-9 10.1201/9781315139470 10.32604/cmes.2024.048398 10.17159/2411-9717/1873/2022 10.1073/pnas.39.5.349 10.1016/j.ijrmms.2012.03.011 10.1214/aos/1013203451 10.21236/ada800105 10.1016/j.ijrmms.2021.104856 10.3390/su11030660 10.1007/978-1-4899-7641-3_10 10.1145/2939672.2939785 10.1007/s11053-020-09616-4 10.1007/s11600-019-00268-4
ContentType	Journal Article
Copyright	Society for Mining, Metallurgy & Exploration Inc. 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Copyright_xml	– notice: Society for Mining, Metallurgy & Exploration Inc. 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
DBID	AAYXX CITATION
DOI	10.1007/s42461-025-01267-4
DatabaseName	CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2524-3470
EndPage	1624
ExternalDocumentID	10_1007_s42461_025_01267_4
GroupedDBID	0R~ 406 7RQ 7WY 883 88I 8AF 8FE 8FG 8FL 8FW AACDK AAEWM AAHNG AAJBT AASML AATNV AAUYE ABAKF ABBRH ABDBE ABECU ABFTV ABJCF ABKCH ABMQK ABTEG ABTKH ABTMW ABUWG ABXPI ACAOD ACDTI ACHSB ACMLO ACOKC ACPIV ACZOJ ADKNI ADTPH ADURQ ADYFF AEFQL AEJRE AEMSY AESKC AFDZB AFKRA AFOHR AFQWF AGDGC AGJBK AGMZJ AGQEE AHPBZ AIGIU AILAN AITGF AJZVZ ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF ATHPR AXYYD AYFIA AZQEC BENPR BEZIV BGLVJ BGNMA BPHCQ CCPQU CSCUP D1I DPUIP DWQXO EBLON EBS EJD FIGPU FINBP FNLPD FRNLG FSGXE GGCAI GNUQQ HCIFZ IKXTQ IWAJR JZLTJ K60 K6~ KB. KOV LLZTM M0F M2P M2Q M4Y NPVJJ NQJWS NU0 PDBOC PHGZM PHGZT PMFND PQBIZ PQBZA PQQKQ PROAC PT4 ROL RSV S0X SCLPG SCV SJYHP SNE SNPRN SOHCF SOJ SRMVM SSLCW STPWE TSG UOJIU UTJUX VEKWB VFIZW ZMTXR AAYXX ABRTQ AEUYN AFFHD BHPHI BKSAR CITATION M0C PCBAR PQGLB
ID	FETCH-LOGICAL-c271t-f6fa8c594af9dbeea8e2f8e2f4a5e25cb010e488eeee74a562132ebe1ceb54a93
IEDL.DBID	RSV
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001482063100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2524-3462
IngestDate	Sat Nov 29 07:51:23 EST 2025 Thu Jun 12 01:45:12 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	3
Keywords	Random forest Gradient boosting machine Decision trees Blasting Flyrock Extreme gradient boosting machines
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c271t-f6fa8c594af9dbeea8e2f8e2f4a5e25cb010e488eeee74a562132ebe1ceb54a93
ORCID	0000-0001-6544-2537
PageCount	16
ParticipantIDs	crossref_primary_10_1007_s42461_025_01267_4 springer_journals_10_1007_s42461_025_01267_4
PublicationCentury	2000
PublicationDate	20250600 2025-06-00
PublicationDateYYYYMMDD	2025-06-01
PublicationDate_xml	– month: 6 year: 2025 text: 20250600
PublicationDecade	2020
PublicationPlace	Cham
PublicationPlace_xml	– name: Cham
PublicationSubtitle	An Official International Peer-reviewed Journal of the Society
PublicationTitle	Mining, metallurgy & exploration
PublicationTitleAbbrev	Mining, Metallurgy & Exploration
PublicationYear	2025
Publisher	Springer International Publishing
Publisher_xml	– name: Springer International Publishing
References	AK Raina (1267_CR16) 2007; 87 N Lundborg (1267_CR14) 1975; 176 H Nguyen (1267_CR4) 2019; 67 S Touzani (1267_CR45) 2017; 158 1267_CR15 T Szendrei (1267_CR21) 2023; 122 1267_CR17 JH Friedman (1267_CR37) 2001; 29 TS Bajpayee (1267_CR10) 2004; 35 K Zorlu (1267_CR44) 2007; 96 H Fattahi (1267_CR27) 2021; 38 1267_CR32 K Cao (1267_CR43) 2019; 11 R Trivedi (1267_CR19) 2014; 6 M Jamei (1267_CR23) 2021; 13 1267_CR34 Y Zhang (1267_CR39) 2015; 58 1267_CR13 H Han (1267_CR35) 2020; 29 D Li (1267_CR5) 2021; 30 TR Rehak (1267_CR11) 2001; 1 DJ Armaghani (1267_CR20) 2015; 32 ET Mohamad (1267_CR8) 2018; 36 L Breiman (1267_CR33) 2001; 45 V Kecojevic (1267_CR9) 2005; 43 L Breiman (1267_CR31) 2017 H Nguyen (1267_CR6) 2020; 30 M Hasanipanah (1267_CR29) 2020; 38 M Yari (1267_CR22) 2015; 27 E Ghasemi (1267_CR12) 2012; 52 C Li (1267_CR26) 2023; 32 S Hosseini (1267_CR3) 2022; 55 1267_CR48 X Ding (1267_CR28) 2023; 15 C Xie (1267_CR38) 2020; 12 1267_CR2 RN Gupta (1267_CR18) 1980 L Breiman (1267_CR30) 1996; 24 H Shapley (1267_CR46) 1953; 39 1267_CR1 1267_CR40 1267_CR42 Y Qiu (1267_CR41) 2021; 38 1267_CR47 AK Raina (1267_CR7) 2014; 73 J Zhou (1267_CR25) 2024; 140 G Biau (1267_CR36) 2016; 25 M Hasanipanah (1267_CR24) 2020; 29
References_xml	– start-page: 23 volume-title: Surface blasting and its impact on environment year: 1980 ident: 1267_CR18 – ident: 1267_CR42 doi: 10.1016/B978-0-12-809633-8.20349-X – volume: 73 start-page: 1199 year: 2014 ident: 1267_CR7 publication-title: Bull Eng Geol Environ doi: 10.1007/s10064-014-0588-6 – volume: 32 start-page: 2995 year: 2023 ident: 1267_CR26 publication-title: Nat Resour Res doi: 10.1007/s11053-023-10259-4 – volume: 158 start-page: 1533 year: 2017 ident: 1267_CR45 publication-title: Energy Build doi: 10.1016/j.enbuild.2017.11.039 – volume: 38 start-page: 2619 year: 2021 ident: 1267_CR27 publication-title: Eng Comput doi: 10.1007/s00366-020-01231-4 – volume: 24 start-page: 123 year: 1996 ident: 1267_CR30 publication-title: Mach Learn doi: 10.1007/BF00058655 – ident: 1267_CR47 doi: 10.48550/arxiv.1908.08474 – volume: 13 start-page: 1438 year: 2021 ident: 1267_CR23 publication-title: J Rock Mech Geotech Eng doi: 10.1016/j.jrmge.2021.07.007 – volume: 30 start-page: 191 year: 2020 ident: 1267_CR6 publication-title: Nat Resour Res doi: 10.1007/s11053-020-09710-7 – volume: 45 start-page: 5 year: 2001 ident: 1267_CR33 publication-title: Mach Learn doi: 10.1023/A:1010933404324 – volume: 29 start-page: 655 year: 2020 ident: 1267_CR35 publication-title: Nat Resour Res doi: 10.1007/s11053-019-09611-4 – volume: 25 start-page: 197 year: 2016 ident: 1267_CR36 publication-title: TEST doi: 10.1007/s11749-016-0481-7 – ident: 1267_CR13 – volume: 6 start-page: 447 year: 2014 ident: 1267_CR19 publication-title: J Rock Mech Geotech Eng doi: 10.1016/j.jrmge.2014.07.003 – volume: 30 start-page: 1905 year: 2021 ident: 1267_CR5 publication-title: Nat Resour Res doi: 10.1007/s11053-020-09794-1 – ident: 1267_CR17 – ident: 1267_CR34 – volume: 55 start-page: 4373 year: 2022 ident: 1267_CR3 publication-title: Rock Mech Rock Eng doi: 10.1007/s00603-022-02866-z – volume: 32 start-page: 109 year: 2015 ident: 1267_CR20 publication-title: Eng Comput doi: 10.1007/s00366-015-0402-5 – volume: 87 start-page: 13 year: 2007 ident: 1267_CR16 publication-title: J Inst Eng India – volume: 15 start-page: 8424 year: 2023 ident: 1267_CR28 publication-title: Sustain doi: 10.3390/su15108424 – volume: 43 start-page: 739 year: 2005 ident: 1267_CR9 publication-title: Saf Sci doi: 10.1016/j.ssci.2005.07.006 – volume: 12 start-page: 101108 year: 2020 ident: 1267_CR38 publication-title: Geosci Front doi: 10.1016/j.gsf.2020.11.005 – volume: 58 start-page: 308 year: 2015 ident: 1267_CR39 publication-title: Transp Res Part C Emerg Technol doi: 10.1016/j.trc.2015.02.019 – volume: 36 start-page: 2217 year: 2018 ident: 1267_CR8 publication-title: Geotech Geol Eng doi: 10.1007/s10706-018-0457-3 – volume: 27 start-page: 699 year: 2015 ident: 1267_CR22 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1889-9 – volume: 176 start-page: 95 year: 1975 ident: 1267_CR14 publication-title: Eng Min J – ident: 1267_CR48 doi: 10.48550/arxiv.2202.05594 – ident: 1267_CR1 doi: 10.1007/978-981-16-8237-7 – volume: 38 start-page: 1257 year: 2020 ident: 1267_CR29 publication-title: Eng Comput doi: 10.1007/s00366-020-01105-9 – volume: 35 start-page: 47 year: 2004 ident: 1267_CR10 publication-title: J Saf Res doi: 10.1016/j.jsr.2003.07.003 – volume: 96 start-page: 141 year: 2007 ident: 1267_CR44 publication-title: Eng Geol doi: 10.1016/j.enggeo.2007.10.009 – volume: 38 start-page: 4145 year: 2021 ident: 1267_CR41 publication-title: Eng Comput doi: 10.1007/s00366-021-01393-9 – year: 2017 ident: 1267_CR31 publication-title: Routledge eBooks doi: 10.1201/9781315139470 – volume: 140 start-page: 1595 year: 2024 ident: 1267_CR25 publication-title: Comput Model Eng Sci doi: 10.32604/cmes.2024.048398 – volume: 122 start-page: 725 year: 2023 ident: 1267_CR21 publication-title: J S Afr Inst Min Metall doi: 10.17159/2411-9717/1873/2022 – volume: 39 start-page: 349 year: 1953 ident: 1267_CR46 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.39.5.349 – volume: 52 start-page: 163 year: 2012 ident: 1267_CR12 publication-title: Int J Rock Mech Min Sci doi: 10.1016/j.ijrmms.2012.03.011 – volume: 29 start-page: 1189 year: 2001 ident: 1267_CR37 publication-title: Ann Stat doi: 10.1214/aos/1013203451 – ident: 1267_CR15 doi: 10.21236/ada800105 – ident: 1267_CR2 doi: 10.1016/j.ijrmms.2021.104856 – volume: 11 start-page: 660 year: 2019 ident: 1267_CR43 publication-title: China Sustain doi: 10.3390/su11030660 – ident: 1267_CR32 doi: 10.1007/978-1-4899-7641-3_10 – volume: 1 start-page: 165 year: 2001 ident: 1267_CR11 publication-title: Florida. Int Soc Explos Eng, Cleveland (OH) – ident: 1267_CR40 doi: 10.1145/2939672.2939785 – volume: 29 start-page: 669 year: 2020 ident: 1267_CR24 publication-title: Nat Resour Res doi: 10.1007/s11053-020-09616-4 – volume: 67 start-page: 477 year: 2019 ident: 1267_CR4 publication-title: Acta Geophys doi: 10.1007/s11600-019-00268-4
SSID	ssib042110743 ssj0002144478
Score	2.3000672
Snippet	In surface mining, blasting operations may have harmful effects on the nearby environment. One of the several undesirable scenarios that should be avoided is...
SourceID	crossref springer
SourceType	Index Database Publisher
StartPage	1609
SubjectTerms	Engineering Materials Engineering Metallic Materials Mineral Resources
Title	A Robust Estimation of Blasting-Induced Flyrock Using Machine Learning Decision Tree Algorithms: Random Forest, Gradient Boosting Machine, and XGBoost
URI	https://link.springer.com/article/10.1007/s42461-025-01267-4
Volume	42
WOSCitedRecordID	wos001482063100001&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: PRVAVX databaseName: Springer Standard Collection customDbUrl: eissn: 2524-3470 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002144478 issn: 2524-3462 databaseCode: RSV dateStart: 20190101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8QwEA6-DnrwLb6Zgzc34KbpNvW26u56UWRdZW8lSScq6lbaKvhH_L0m2VZcEEELvYSQlsw7mfmGkAMWKJMGJqJpEIQ2QIkMVU2uqaWHEUcYiCiVvtlEdHkphsP4qioKK-ps9_pK0mvqr2I37qDPqGu_apWqQ-ueJrPW3Aknjv3r25qLuA9pKpApp48dKBj3KpmFjNOAt1hVPfPzspMWavJ61Fud7tL__neZLFZeJrTHbLFCpnC0Sha-YQ-ukY829DP1WpTQsVI-LmCEzMCJdaddKjR1TT00ptB9erdG7hF8cgFc-ORLhAqX9Q7OqiY9MMgRof10l-UP5f1zcQx9OUqzZ3DNP4uyAb3cZ5eVcJJl_gv1Wg2wE2HY8-Pr5KbbGZye06pJA9UsapbUtIwUOoy5NHGqEKVAZtzLZYgs1MoGfGi1BNonsmMtZuNfyzlNjSrkMg42yMwoG-EmAdSSaRWo0FifQ8VKKCFasRA8lgYNii1yWBMmeRljcSRfqMt-txO724nf7YRvkUZNl6SSy-KX6dt_m75D5pknrTuP2SUzZf6Ke2ROv5UPRb7vGfITxnjbMA
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1fa9swEBdbO1j30O5fWdpuu4e9LYJFkmO5b0nbtGNJGFk28mYk-dSVNfaw3UK_SD9vJcUOLYzBZvCLOGRzd7rTSXe_I-QD49pm3MY04zxyAUpsqe4JQ508rPyEXMaZCs0m4ulULhbJ16YorGqz3dsryWCp18VuwkOfUd9-1RlVj9b9mGwK57F8It_s249Wi0QIaRqQKW-PPSiYCCaZRUxQLvqsqZ7587QPPdTD69HgdUY7__e_z8l2s8uEwUotXpBHmL8kz-5hD74itwOYFfqqquHErfJVASMUFoZuO-1Toalv6mEwg9HljXNyvyAkF8AkJF8iNLis53DcNOmBeYkIg8vzoryofy6rQ5ipPCuW4Jt_VnUXTsuQXVbDsCjCF9q5uuAIYXEaxl-T76OT-dEZbZo0UMPiXk1t3yppokQom2QaUUlk1r9CRcgio13Ah85KoHtiN9ZnLv51mtMzqCOhEr5LNvIixzcE0ChmNNeRdXsOnWippewnUopEWbQoO-RjK5j09wqLI12jLgdup47baeB2Kjqk28olbdZl9RfyvX8jf0-ens0n43T8efpln2yxIGZ_NnNANuryCt-SJ-a6vqjKd0E57wAVrN4U
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fb9MwELagIAQPAwaI8vMeeKPWVsdJnL11a7tNG1VVtqlvke2cS8WWVEmGxD-yvxfbTaZOmpAQkfJiWU50dz777LvvI-QLC5TJAhPTLAhCG6DEhqo-19Tqw4hdDEScSU82EU8mYj5PphtV_D7bvb2SXNc0OJSmvN5ZZWbntvCNOxg06qhYrYN1yN0PySPuSINcvP79orUo7sObBnDK-WYHEMa9e2Yh4zTgEWsqae4f9u5qdfeq1K9A4-f__-8vyFaz-4TB2lxekgeYb5NnG5iEr8jNAGaFuq5qGNnZvy5shMLAvt1muxRp6sg-NGYwvvxtF7-f4JMO4JtPykRo8FoXMGzIe-CsRITB5aIol_WPq2oPZjLPiitwpKBV3YPD0med1bBfFP4L7Vg9sB1hfujbX5Pz8ejs4Ig25A1Us7hfUxMZKXSYcGmSTCFKgcy4l8sQWaiVDQTReg-0T2zbImbjYmtRfY0q5DIJ3pBOXuT4lgBqybQKVGjsXkQlSighokQInkiDBkWXfG2VlK7WGB3pLRqzl3ZqpZ16aae8S3qtjtJmvlZ_6f7u37p_Jk-mw3F6ejw5eU-eMq9ld2TzgXTq8ho_ksf6V72syk_eTv8ABjfm-A
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=A+Robust+Estimation+of+Blasting-Induced+Flyrock+Using+Machine+Learning+Decision+Tree+Algorithms%3A+Random+Forest%2C+Gradient+Boosting+Machine%2C+and+XGBoost&rft.jtitle=Mining%2C+metallurgy+%26+exploration&rft.au=Mohammed%2C+Bouhannache&rft.au=Hamza%2C+Cheniti&rft.date=2025-06-01&rft.pub=Springer+International+Publishing&rft.issn=2524-3462&rft.eissn=2524-3470&rft.volume=42&rft.issue=3&rft.spage=1609&rft.epage=1624&rft_id=info:doi/10.1007%2Fs42461-025-01267-4&rft.externalDocID=10_1007_s42461_025_01267_4
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2524-3462&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2524-3462&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2524-3462&client=summon