Wavelet support vector machine-based prediction model of dam deformation
•SVM and other methods are combined to build prediction model of dam deformation.•Three key problems on SVM are investigated.•The proposed approach can reduce the human impact on modeling process. Considering the strong nonlinear dynamic characteristics of dam deformation, the prediction model of da...
Uloženo v:
| Vydáno v: | Mechanical systems and signal processing Ročník 110; s. 412 - 427 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Berlin
Elsevier Ltd
15.09.2018
Elsevier BV |
| Témata: | |
| ISSN: | 0888-3270, 1096-1216 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •SVM and other methods are combined to build prediction model of dam deformation.•Three key problems on SVM are investigated.•The proposed approach can reduce the human impact on modeling process.
Considering the strong nonlinear dynamic characteristics of dam deformation, the prediction model of dam deformation is investigated. Support vector machine (SVM) is combined with other methods, such as phase space reconstruction, wavelet analysis and particle swarm optimization (PSO), to build the prediction model of dam deformation. Firstly, the chaotic characteristics and the predictable time scale of dam deformation are identified by implementing the phase space reconstruction of observation data series on dam deformation. Secondly, a SVM-based prediction model of dam deformation is proposed. The reconstructed phase space of observed deformation and the Morlet wavelet basis function are selected as the input vector and the kernel function of SVM. Thirdly, the PSO algorithm is improved to implement the parameter optimization of SVM-based prediction model of dam deformation. Finally, the displacement of one actual dam is taken as an example. The results demonstrate the modeling efficiency and forecasting accuracy can be improved. |
|---|---|
| AbstractList | •SVM and other methods are combined to build prediction model of dam deformation.•Three key problems on SVM are investigated.•The proposed approach can reduce the human impact on modeling process.
Considering the strong nonlinear dynamic characteristics of dam deformation, the prediction model of dam deformation is investigated. Support vector machine (SVM) is combined with other methods, such as phase space reconstruction, wavelet analysis and particle swarm optimization (PSO), to build the prediction model of dam deformation. Firstly, the chaotic characteristics and the predictable time scale of dam deformation are identified by implementing the phase space reconstruction of observation data series on dam deformation. Secondly, a SVM-based prediction model of dam deformation is proposed. The reconstructed phase space of observed deformation and the Morlet wavelet basis function are selected as the input vector and the kernel function of SVM. Thirdly, the PSO algorithm is improved to implement the parameter optimization of SVM-based prediction model of dam deformation. Finally, the displacement of one actual dam is taken as an example. The results demonstrate the modeling efficiency and forecasting accuracy can be improved. Considering the strong nonlinear dynamic characteristics of dam deformation, the prediction model of dam deformation is investigated. Support vector machine (SVM) is combined with other methods, such as phase space reconstruction, wavelet analysis and particle swarm optimization (PSO), to build the prediction model of dam deformation. Firstly, the chaotic characteristics and the predictable time scale of dam deformation are identified by implementing the phase space reconstruction of observation data series on dam deformation. Secondly, a SVM-based prediction model of dam deformation is proposed. The reconstructed phase space of observed deformation and the Morlet wavelet basis function are selected as the input vector and the kernel function of SVM. Thirdly, the PSO algorithm is improved to implement the parameter optimization of SVM-based prediction model of dam deformation. Finally, the displacement of one actual dam is taken as an example. The results demonstrate the modeling efficiency and forecasting accuracy can be improved. |
| Author | Yang, Beibei Li, Xing Wen, Zhiping Su, Huaizhi |
| Author_xml | – sequence: 1 givenname: Huaizhi surname: Su fullname: Su, Huaizhi email: su_huaizhi@hhu.edu.cn organization: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China – sequence: 2 givenname: Xing surname: Li fullname: Li, Xing organization: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China – sequence: 3 givenname: Beibei surname: Yang fullname: Yang, Beibei organization: National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Nanjing 210098, China – sequence: 4 givenname: Zhiping surname: Wen fullname: Wen, Zhiping organization: Department of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China |
| BookMark | eNqFkD1PwzAQhi1UJNrCL2CJxJxwjvNhDwyo4kuqxAJitBz7Ihw1cbDdSv33pC0TA0w33D139z4LMhvcgIRcU8go0Oq2y_Z9CGOWA-UZsAzy_IzMKYgqpTmtZmQOnPOU5TVckEUIHQCIAqo5ef5QO9xgTMJ2HJ2PyQ51dD7plf60A6aNCmiS0aOxOlo3JL0zuElcmxjVJwZb53t1aFyS81ZtAl791CV5f3x4Wz2n69enl9X9OtVM1DE1OeRUGFVCBQ2Fom44pW1TtnXLdIPMaKEFUM1rLrCoStpQ1FMcrsAUAA1bkpvT3tG7ry2GKDu39cN0UuZQiRKg4nyaEqcp7V0IHlupbTz-Gb2yG0lBHsTJTh7FyYM4CUxOlyaW_WJHb3vl9_9QdycKp_A7i14GbXHQkzg_KZXG2T_5b-GSiyM |
| CitedBy_id | crossref_primary_10_1007_s40996_021_00709_5 crossref_primary_10_1038_s41598_023_47565_z crossref_primary_10_1016_j_istruc_2023_05_136 crossref_primary_10_1177_14759217241282738 crossref_primary_10_1155_2019_7936513 crossref_primary_10_1016_j_conbuildmat_2023_130612 crossref_primary_10_1016_j_ymssp_2021_108013 crossref_primary_10_1088_1361_6501_ad850d crossref_primary_10_1007_s12517_021_08484_3 crossref_primary_10_1007_s12599_019_00593_4 crossref_primary_10_1007_s00366_021_01362_2 crossref_primary_10_3390_app122312296 crossref_primary_10_1016_j_marpetgeo_2022_105631 crossref_primary_10_1109_ACCESS_2019_2949743 crossref_primary_10_1007_s12145_022_00830_7 crossref_primary_10_1016_j_enggeo_2018_11_007 crossref_primary_10_1016_j_knosys_2021_107537 crossref_primary_10_1016_j_measurement_2024_115664 crossref_primary_10_1016_j_engstruct_2023_116827 crossref_primary_10_1016_j_engappai_2024_109109 crossref_primary_10_1016_j_engappai_2023_106813 crossref_primary_10_1016_j_aei_2023_102345 crossref_primary_10_1016_j_asr_2024_06_018 crossref_primary_10_3390_w16121646 crossref_primary_10_1016_j_asoc_2021_107281 crossref_primary_10_1016_j_compgeo_2024_106518 crossref_primary_10_3390_w14142157 crossref_primary_10_1088_1361_6501_ac750f crossref_primary_10_1088_1361_6501_ad1cc9 crossref_primary_10_3390_w14162464 crossref_primary_10_1016_j_eswa_2022_117272 crossref_primary_10_1016_j_asoc_2024_112321 crossref_primary_10_1007_s00366_021_01515_3 crossref_primary_10_1016_j_ymssp_2019_02_027 crossref_primary_10_1002_stc_2917 crossref_primary_10_1155_2020_5463893 crossref_primary_10_1177_14759217221122368 crossref_primary_10_1007_s10706_023_02451_3 crossref_primary_10_1007_s41748_024_00558_y crossref_primary_10_1007_s00477_025_03108_8 crossref_primary_10_1088_1742_6596_2005_1_012084 crossref_primary_10_1109_JSEN_2022_3148742 crossref_primary_10_3390_su12197877 crossref_primary_10_1007_s12613_024_2977_6 crossref_primary_10_3390_en17225599 crossref_primary_10_1007_s10064_021_02403_2 crossref_primary_10_1016_j_ymssp_2022_109397 crossref_primary_10_1038_s41598_022_13073_9 crossref_primary_10_1007_s00500_022_07437_6 crossref_primary_10_1109_ACCESS_2019_2928001 crossref_primary_10_3390_buildings14113675 crossref_primary_10_1007_s10010_025_00863_6 crossref_primary_10_1016_j_watres_2022_118682 crossref_primary_10_3390_w14223739 crossref_primary_10_3390_w14162538 crossref_primary_10_1016_j_aei_2025_103252 crossref_primary_10_3390_electronics13020438 crossref_primary_10_1016_j_measurement_2023_113579 crossref_primary_10_1007_s11270_025_08347_7 crossref_primary_10_1016_j_ress_2022_108513 crossref_primary_10_3390_infrastructures10070170 crossref_primary_10_3390_ma18184274 crossref_primary_10_1016_j_istruc_2023_02_076 crossref_primary_10_1016_j_renene_2025_124080 crossref_primary_10_1155_2024_4791788 crossref_primary_10_1038_s41598_025_92806_y crossref_primary_10_1016_j_eswa_2023_121752 crossref_primary_10_1177_14759217251360192 crossref_primary_10_1177_1475921719884861 crossref_primary_10_3390_w14213380 crossref_primary_10_1051_matecconf_202439605016 crossref_primary_10_1002_suco_202300450 crossref_primary_10_1002_stc_2685 crossref_primary_10_3390_rs17050755 crossref_primary_10_1155_2021_8487997 crossref_primary_10_3390_rs15164110 crossref_primary_10_1016_j_molliq_2022_119159 crossref_primary_10_1016_j_compag_2020_105921 crossref_primary_10_1177_1748006X241254603 crossref_primary_10_1038_s41598_020_69703_7 crossref_primary_10_3390_w16101388 crossref_primary_10_1007_s13042_023_01939_x crossref_primary_10_1111_mice_12810 crossref_primary_10_1007_s11356_023_27799_0 crossref_primary_10_1002_stc_3090 crossref_primary_10_3390_buildings15152803 crossref_primary_10_1016_j_measurement_2025_118178 crossref_primary_10_3390_w15193474 crossref_primary_10_1016_j_ecolind_2023_110538 crossref_primary_10_1007_s11069_022_05424_6 crossref_primary_10_1016_j_eswa_2023_122022 crossref_primary_10_1111_mice_13232 crossref_primary_10_1016_j_eti_2021_101768 crossref_primary_10_1155_2022_1511479 crossref_primary_10_1016_j_autcon_2022_104365 crossref_primary_10_3390_app13148538 crossref_primary_10_1007_s00704_022_04294_z crossref_primary_10_1007_s12517_022_11005_5 crossref_primary_10_1016_j_jconhyd_2023_104195 crossref_primary_10_1007_s00366_022_01675_w crossref_primary_10_1080_15502287_2023_2294289 crossref_primary_10_3390_app132011212 crossref_primary_10_1111_mice_12911 crossref_primary_10_1016_j_ymssp_2023_110721 crossref_primary_10_3390_su142316025 crossref_primary_10_3390_w16243687 crossref_primary_10_1002_er_8207 crossref_primary_10_1016_j_marpetgeo_2022_105597 crossref_primary_10_1007_s10064_021_02424_x crossref_primary_10_1016_j_engstruct_2025_121222 crossref_primary_10_3390_math11092010 crossref_primary_10_1016_j_knosys_2021_106964 crossref_primary_10_1155_2023_3879096 crossref_primary_10_1016_j_aei_2024_102921 crossref_primary_10_1016_j_istruc_2022_10_052 crossref_primary_10_1016_j_aei_2020_101154 crossref_primary_10_1061_IJGNAI_GMENG_8265 crossref_primary_10_3390_app10165700 crossref_primary_10_1155_2020_4961963 crossref_primary_10_1016_j_ijdrr_2025_105605 crossref_primary_10_1177_09596518231226359 crossref_primary_10_3390_pr10091842 crossref_primary_10_1016_j_measurement_2022_111811 crossref_primary_10_3390_app142310848 crossref_primary_10_1016_j_engstruct_2024_117949 crossref_primary_10_1007_s00500_022_07422_z crossref_primary_10_1016_j_aei_2023_101881 crossref_primary_10_1016_j_jhydrol_2021_126929 crossref_primary_10_1016_j_aei_2024_102574 crossref_primary_10_3390_su14105771 crossref_primary_10_1080_19648189_2020_1847689 crossref_primary_10_1016_j_aei_2023_102175 crossref_primary_10_1016_j_apm_2019_10_012 crossref_primary_10_1016_j_ins_2024_120862 crossref_primary_10_3390_w15071271 crossref_primary_10_3390_rs16213978 crossref_primary_10_1016_j_engstruct_2025_120350 crossref_primary_10_1016_j_aei_2022_101855 crossref_primary_10_1016_j_aei_2022_101615 crossref_primary_10_1111_mice_70009 crossref_primary_10_1177_17515831241241947 crossref_primary_10_3389_feart_2023_1122937 crossref_primary_10_1016_j_engappai_2025_110378 crossref_primary_10_1007_s00521_019_04375_7 crossref_primary_10_3390_w13020241 crossref_primary_10_1016_j_advengsoft_2024_103635 crossref_primary_10_1061__ASCE_GM_1943_5622_0002401 crossref_primary_10_2166_hydro_2021_178 crossref_primary_10_1007_s00703_021_00787_0 crossref_primary_10_1007_s13349_024_00776_y crossref_primary_10_1002_stc_2859 crossref_primary_10_3390_rs15082188 crossref_primary_10_1007_s12665_024_11696_x crossref_primary_10_1016_j_advengsoft_2019_02_005 crossref_primary_10_1007_s13349_022_00603_2 crossref_primary_10_3390_w15010059 crossref_primary_10_1016_j_engstruct_2023_115950 crossref_primary_10_1155_2023_6929861 crossref_primary_10_3390_app11146625 crossref_primary_10_3390_rs12213620 crossref_primary_10_1016_j_compgeo_2023_105611 crossref_primary_10_1155_2020_8831965 crossref_primary_10_3390_w16131766 crossref_primary_10_3390_w16213043 crossref_primary_10_1016_j_aei_2025_103782 crossref_primary_10_3233_JIFS_234409 crossref_primary_10_1007_s00500_021_06025_4 crossref_primary_10_1007_s10489_021_02270_0 crossref_primary_10_3390_app9142802 crossref_primary_10_3390_su17167401 crossref_primary_10_1016_j_engstruct_2021_113400 crossref_primary_10_1109_ACCESS_2019_2890819 crossref_primary_10_3390_w15020319 crossref_primary_10_3390_app12010481 crossref_primary_10_1016_j_aei_2021_101407 crossref_primary_10_1002_stc_2716 crossref_primary_10_1016_j_engstruct_2020_111488 crossref_primary_10_1016_j_engstruct_2024_118845 crossref_primary_10_1016_j_jhydrol_2020_125033 crossref_primary_10_1016_j_measurement_2021_109377 crossref_primary_10_1111_mice_12654 crossref_primary_10_1016_j_fuel_2021_122184 crossref_primary_10_1080_17452759_2024_2352066 crossref_primary_10_3390_w15193511 crossref_primary_10_1016_j_ejrh_2025_102285 crossref_primary_10_1016_j_engstruct_2022_114686 crossref_primary_10_1109_ACCESS_2020_2995592 crossref_primary_10_1016_j_asoc_2024_112356 crossref_primary_10_1007_s42107_025_01474_w crossref_primary_10_1016_j_asoc_2023_110411 crossref_primary_10_3390_app131910827 crossref_primary_10_1016_j_aei_2018_11_006 crossref_primary_10_1016_j_istruc_2024_107072 crossref_primary_10_3390_w16131868 crossref_primary_10_1371_journal_pone_0267434 crossref_primary_10_1177_14759217211044116 crossref_primary_10_1177_14759217211069639 crossref_primary_10_1002_stc_2940 crossref_primary_10_1016_j_fuel_2023_128646 crossref_primary_10_26599_TST_2022_9010015 crossref_primary_10_1016_j_engstruct_2021_112652 crossref_primary_10_1016_j_gsf_2020_10_009 crossref_primary_10_1016_j_ecoinf_2023_101980 crossref_primary_10_1016_j_neucom_2020_04_105 crossref_primary_10_3390_app11167334 |
| Cites_doi | 10.1109/72.914517 10.1016/j.camwa.2011.09.057 10.1002/stc.1767 10.1016/j.ymssp.2016.02.031 10.1103/PhysRevA.45.3403 10.1016/j.ymssp.2006.12.007 10.1117/12.833745 10.1016/S0165-0114(02)00283-X 10.1016/j.strusafe.2014.02.004 10.1007/s11069-011-9743-6 10.1016/j.engstruct.2010.12.011 10.1016/S0167-2789(97)00118-8 10.1016/0167-2789(85)90011-9 10.1007/s11431-009-0275-1 10.1177/1475921711419993 10.1007/s11721-007-0002-0 10.1016/j.ymssp.2012.08.029 10.1109/TEVC.2004.826071 10.1002/stc.492 10.1109/TNN.2003.809414 10.1016/j.eswa.2011.02.176 10.1111/j.1467-8667.2007.00499.x 10.1061/(ASCE)0733-9399(2007)133:3(267) 10.1016/j.engstruct.2006.04.022 10.1023/A:1018628609742 10.1177/1475921716654963 10.1016/j.ymssp.2017.03.016 10.1016/j.eswa.2013.02.033 10.1016/j.asoc.2016.07.044 10.1007/s11431-008-0103-z 10.1016/j.advwatres.2006.10.001 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier Ltd Copyright Elsevier BV Sep 15, 2018 |
| Copyright_xml | – notice: 2018 Elsevier Ltd – notice: Copyright Elsevier BV Sep 15, 2018 |
| DBID | AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D |
| DOI | 10.1016/j.ymssp.2018.03.022 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef 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 Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1096-1216 |
| EndPage | 427 |
| ExternalDocumentID | 10_1016_j_ymssp_2018_03_022 S0888327018301419 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO AAYFN ABBOA ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BJAXD BKOJK BLXMC CS3 DM4 DU5 EBS EFBJH EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W JJJVA KOM LG5 LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SDP SES SPC SPCBC SPD SST SSV SSZ T5K XPP ZMT ZU3 ~G- 29M 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABDPE ABEFU ABFNM ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADFGL ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CAG CITATION COF EFKBS FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET SEW WUQ ~HD 7SC 7SP 8FD JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c397t-d20219da5060b1047b811fb5f7f3cbe3dc9c901c8789e4651b1ec0228a0d400b3 |
| ISICitedReferencesCount | 214 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000431162900026&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0888-3270 |
| IngestDate | Sun Oct 05 00:20:26 EDT 2025 Sat Nov 29 02:08:15 EST 2025 Tue Nov 18 21:49:55 EST 2025 Fri Feb 23 02:29:58 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Dam deformation Improved particle swarm optimization algorithm Characteristics identification Prediction model Wavelet support vector machine |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c397t-d20219da5060b1047b811fb5f7f3cbe3dc9c901c8789e4651b1ec0228a0d400b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2069500688 |
| PQPubID | 2045429 |
| PageCount | 16 |
| ParticipantIDs | proquest_journals_2069500688 crossref_citationtrail_10_1016_j_ymssp_2018_03_022 crossref_primary_10_1016_j_ymssp_2018_03_022 elsevier_sciencedirect_doi_10_1016_j_ymssp_2018_03_022 |
| PublicationCentury | 2000 |
| PublicationDate | 2018-09-15 |
| PublicationDateYYYYMMDD | 2018-09-15 |
| PublicationDate_xml | – month: 09 year: 2018 text: 2018-09-15 day: 15 |
| PublicationDecade | 2010 |
| PublicationPlace | Berlin |
| PublicationPlace_xml | – name: Berlin |
| PublicationTitle | Mechanical systems and signal processing |
| PublicationYear | 2018 |
| Publisher | Elsevier Ltd Elsevier BV |
| Publisher_xml | – name: Elsevier Ltd – name: Elsevier BV |
| References | Xi, Yue, Zhou, Tang (b0050) 2011; 62 Kennel, Brown, Abarbanel (b0150) 1992; 45 Su, Wen, Wang, Hu (b0135) 2016; 48 Su, Wen, Wang, Wei, Hu (b0145) 2013; 40 Cao (b0155) 1997; 110 Chen, McPhee, Yeh (b0170) 2007; 30 Yang, Nagarajaiah (b0110) 2014; 47 Léger, Leclerc (b0020) 2007; 133 Su, Wen, Chen, Tian (b0030) 2016; 15 J. Yang, J.W Tian, J. Liu, F. Wei, SVM algorithm based on wavelet kernel function for medical image segmentation, in: Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971Z, 2009. An, Jiang, Liu, Zhao (b0140) 2011; 38 Wolf, Swift, Swinney, Vastano (b0160) 1985; 16 Vapnik (b0055) 1995 Cristianini, Shawe-Taylor (b0090) 2000 Gangsar, Tiwari (b0100) 2017; 94 Suykens, Van Gestel, Vandewalle, De Moor (b0105) 2003; 14 Kao, Loh (b0045) 2013; 20 Zhong, Sun, Li (b0015) 2011; 59 M.H. Law, J.T. Kwok, Bayesian support vector regression, in: Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS), Key West, Florida, USA, 2001, pp. 239–244. Ranković, Grujović, Divac, Milivojević (b0080) 2014; 48 Su, Chen, Wen (b0085) 2016; 23 Suykens, Vandewalle (b0065) 1999; 9 Poli, Kennedy, Blackwell (b0165) 2007; 1 Lu, Chen, Hong, Feng, Li (b0125) 2016; 76–77 Zhang, Li, Xuan, Li (b0005) 2009; 52 De Sortis, Paoliani (b0025) 2007; 29 Widodo, Yang (b0060) 2007; 21 Wang, Lin (b0075) 2003; 134 Mata (b0040) 2011; 33 Muller, Mika, Ratsch, Tsuda, Scholkopf (b0095) 2001; 12 Su, Wu, Wen (b0115) 2007; 22 Wu, Su, Guo (b0010) 2008; 51 Su, Hu, Wu (b0035) 2012; 11 Jin, Liu (b0120) 2008; 19 Ratnaweera, Halgamuge, Watson (b0175) 2004; 8 Zhang (10.1016/j.ymssp.2018.03.022_b0005) 2009; 52 Xi (10.1016/j.ymssp.2018.03.022_b0050) 2011; 62 Jin (10.1016/j.ymssp.2018.03.022_b0120) 2008; 19 Ranković (10.1016/j.ymssp.2018.03.022_b0080) 2014; 48 Wang (10.1016/j.ymssp.2018.03.022_b0075) 2003; 134 Wolf (10.1016/j.ymssp.2018.03.022_b0160) 1985; 16 Wu (10.1016/j.ymssp.2018.03.022_b0010) 2008; 51 Muller (10.1016/j.ymssp.2018.03.022_b0095) 2001; 12 Lu (10.1016/j.ymssp.2018.03.022_b0125) 2016; 76–77 Vapnik (10.1016/j.ymssp.2018.03.022_b0055) 1995 De Sortis (10.1016/j.ymssp.2018.03.022_b0025) 2007; 29 10.1016/j.ymssp.2018.03.022_b0130 10.1016/j.ymssp.2018.03.022_b0070 Chen (10.1016/j.ymssp.2018.03.022_b0170) 2007; 30 Su (10.1016/j.ymssp.2018.03.022_b0030) 2016; 15 Su (10.1016/j.ymssp.2018.03.022_b0115) 2007; 22 Su (10.1016/j.ymssp.2018.03.022_b0035) 2012; 11 Su (10.1016/j.ymssp.2018.03.022_b0145) 2013; 40 Mata (10.1016/j.ymssp.2018.03.022_b0040) 2011; 33 Zhong (10.1016/j.ymssp.2018.03.022_b0015) 2011; 59 Cristianini (10.1016/j.ymssp.2018.03.022_b0090) 2000 An (10.1016/j.ymssp.2018.03.022_b0140) 2011; 38 Gangsar (10.1016/j.ymssp.2018.03.022_b0100) 2017; 94 Léger (10.1016/j.ymssp.2018.03.022_b0020) 2007; 133 Widodo (10.1016/j.ymssp.2018.03.022_b0060) 2007; 21 Su (10.1016/j.ymssp.2018.03.022_b0085) 2016; 23 Ratnaweera (10.1016/j.ymssp.2018.03.022_b0175) 2004; 8 Kennel (10.1016/j.ymssp.2018.03.022_b0150) 1992; 45 Su (10.1016/j.ymssp.2018.03.022_b0135) 2016; 48 Cao (10.1016/j.ymssp.2018.03.022_b0155) 1997; 110 Kao (10.1016/j.ymssp.2018.03.022_b0045) 2013; 20 Suykens (10.1016/j.ymssp.2018.03.022_b0065) 1999; 9 Poli (10.1016/j.ymssp.2018.03.022_b0165) 2007; 1 Suykens (10.1016/j.ymssp.2018.03.022_b0105) 2003; 14 Yang (10.1016/j.ymssp.2018.03.022_b0110) 2014; 47 |
| References_xml | – volume: 1 start-page: 33 year: 2007 end-page: 57 ident: b0165 article-title: Particle swarm optimization publication-title: Swarm Intell. – volume: 59 start-page: 129 year: 2011 end-page: 147 ident: b0015 article-title: Dam break threshold value and risk probability assessment for an earth dam publication-title: Nat. Hazards – reference: M.H. Law, J.T. Kwok, Bayesian support vector regression, in: Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS), Key West, Florida, USA, 2001, pp. 239–244. – volume: 33 start-page: 903 year: 2011 end-page: 910 ident: b0040 article-title: Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models publication-title: Eng. Struct. – volume: 16 start-page: 285 year: 1985 end-page: 317 ident: b0160 article-title: Determining Lyapunov exponents from a time series publication-title: Physica D – volume: 133 start-page: 267 year: 2007 end-page: 277 ident: b0020 article-title: Hydrostatic, temperature, time-displacement model for concrete dams publication-title: J. Eng. Mech. – volume: 48 start-page: 612 year: 2016 end-page: 620 ident: b0135 article-title: Dam structural behavior identification and prediction by using variable dimension fractal model and iterated function system publication-title: Appl. Soft Comput. – volume: 21 start-page: 2560 year: 2007 end-page: 2574 ident: b0060 article-title: Support vector machine in machine condition monitoring and fault diagnosis publication-title: Mech. Syst. Sig. Process. – volume: 12 start-page: 181 year: 2001 end-page: 201 ident: b0095 article-title: An introduction to kernel-based learning algorithms publication-title: IEEE Trans. Neural Netw. – volume: 48 start-page: 33 year: 2014 end-page: 39 ident: b0080 article-title: Development of support vector regression identification model for prediction of dam structural behavior publication-title: Struct. Saf. – volume: 52 start-page: 3024 year: 2009 end-page: 3029 ident: b0005 article-title: Overtopping breaching of cohesive homogeneous earth dam with different cohesive strength publication-title: Sci. China Ser. E-Tech. Sci. – volume: 11 start-page: 269 year: 2012 end-page: 279 ident: b0035 article-title: A study of safety evaluation and early-warning method for dam global behavior publication-title: Struct. Health Monit. – volume: 14 start-page: 447 year: 2003 end-page: 450 ident: b0105 article-title: A support vector machine formulation to PCA analysis and its kernel version publication-title: IEEE Trans. Neural Netw. – volume: 9 start-page: 293 year: 1999 end-page: 300 ident: b0065 article-title: Least squares support vector machine classifiers publication-title: Neural Process. Lett. – year: 1995 ident: b0055 article-title: The Nature of Statistical Learning Theory – volume: 38 start-page: 11280 year: 2011 end-page: 11285 ident: b0140 article-title: Wind farm power prediction based on wavelet decomposition and chaotic time series publication-title: Expert Syst. Appl. – reference: J. Yang, J.W Tian, J. Liu, F. Wei, SVM algorithm based on wavelet kernel function for medical image segmentation, in: Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74971Z, 2009. – volume: 134 start-page: 343 year: 2003 end-page: 363 ident: b0075 article-title: A fuzzy multicriteria group decision making approach to select configuration items for software development publication-title: Fuzzy Set. Syst. – volume: 20 start-page: 282 year: 2013 end-page: 303 ident: b0045 article-title: Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches publication-title: Struct. Contr. Health Monit. – volume: 23 start-page: 252 year: 2016 end-page: 266 ident: b0085 article-title: Performance improvement method of support vector machine-based model monitoring dam safety publication-title: Struct. Contr. Health Monit. – volume: 19 start-page: 500 year: 2008 end-page: 505 ident: b0120 article-title: Wavelet basis function neural networks for sequential learning publication-title: IEEE Trans. Neural Netw. – volume: 51 start-page: 1345 year: 2008 end-page: 1352 ident: b0010 article-title: Risk assessment method of major unsafe hydroelectric project publication-title: Sci. China Ser. E-Tech. Sci. – volume: 15 start-page: 639 year: 2016 end-page: 649 ident: b0030 article-title: Dam safety prediction model considering chaotic characteristics in prototype monitoring data series publication-title: Struct. Health Monit. – volume: 62 start-page: 3980 year: 2011 end-page: 3986 ident: b0050 article-title: Application of an artificial immune algorithm on a statistical model of dam displacement publication-title: Comput. Math. Appl. – year: 2000 ident: b0090 article-title: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods – volume: 110 start-page: 43 year: 1997 end-page: 50 ident: b0155 article-title: Practical method for determining the minimum embedding dimensions of a scalar time series publication-title: Physica D – volume: 8 start-page: 240 year: 2004 end-page: 255 ident: b0175 article-title: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients publication-title: IEEE Trans. Evol. Comput. – volume: 47 start-page: 3 year: 2014 end-page: 20 ident: b0110 article-title: Blind identification of damage in time-varying systems using independent component analysis with wavelet transform publication-title: Mech. Syst. Sig. Process. – volume: 30 start-page: 1082 year: 2007 end-page: 1093 ident: b0170 article-title: A diversified multiobjective GA for optimizing reservoir rule curves publication-title: Adv. Water Resour. – volume: 40 start-page: 4922 year: 2013 end-page: 4933 ident: b0145 article-title: Multifractal scaling behavior analysis for existing dams publication-title: Expert Syst. Appl. – volume: 94 start-page: 464 year: 2017 end-page: 481 ident: b0100 article-title: Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms publication-title: Mech. Syst. Sig. Process. – volume: 45 start-page: 3403 year: 1992 end-page: 3411 ident: b0150 article-title: Determining embedding dimension for phase-space reconstruction using a geometrical construction publication-title: Phys. Rev. A – volume: 76–77 start-page: 353 year: 2016 end-page: 366 ident: b0125 article-title: Degradation trend estimation of slewing bearing based on LSSVM model publication-title: Mech. Syst. Sig. Process. – volume: 29 start-page: 110 year: 2007 end-page: 120 ident: b0025 article-title: Statistical analysis and structural identification in concrete dam monitoring publication-title: Eng. Struct. – volume: 22 start-page: 438 year: 2007 end-page: 448 ident: b0115 article-title: Identification model for dam behavior based on wavelet network publication-title: Comput-Aided Civ. Inf. – volume: 12 start-page: 181 issue: 2 year: 2001 ident: 10.1016/j.ymssp.2018.03.022_b0095 article-title: An introduction to kernel-based learning algorithms publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.914517 – volume: 62 start-page: 3980 issue: 10 year: 2011 ident: 10.1016/j.ymssp.2018.03.022_b0050 article-title: Application of an artificial immune algorithm on a statistical model of dam displacement publication-title: Comput. Math. Appl. doi: 10.1016/j.camwa.2011.09.057 – volume: 23 start-page: 252 issue: 2 year: 2016 ident: 10.1016/j.ymssp.2018.03.022_b0085 article-title: Performance improvement method of support vector machine-based model monitoring dam safety publication-title: Struct. Contr. Health Monit. doi: 10.1002/stc.1767 – volume: 76–77 start-page: 353 year: 2016 ident: 10.1016/j.ymssp.2018.03.022_b0125 article-title: Degradation trend estimation of slewing bearing based on LSSVM model publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2016.02.031 – volume: 45 start-page: 3403 issue: 6 year: 1992 ident: 10.1016/j.ymssp.2018.03.022_b0150 article-title: Determining embedding dimension for phase-space reconstruction using a geometrical construction publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.45.3403 – volume: 21 start-page: 2560 year: 2007 ident: 10.1016/j.ymssp.2018.03.022_b0060 article-title: Support vector machine in machine condition monitoring and fault diagnosis publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2006.12.007 – ident: 10.1016/j.ymssp.2018.03.022_b0130 doi: 10.1117/12.833745 – volume: 134 start-page: 343 issue: 3 year: 2003 ident: 10.1016/j.ymssp.2018.03.022_b0075 article-title: A fuzzy multicriteria group decision making approach to select configuration items for software development publication-title: Fuzzy Set. Syst. doi: 10.1016/S0165-0114(02)00283-X – volume: 48 start-page: 33 year: 2014 ident: 10.1016/j.ymssp.2018.03.022_b0080 article-title: Development of support vector regression identification model for prediction of dam structural behavior publication-title: Struct. Saf. doi: 10.1016/j.strusafe.2014.02.004 – volume: 59 start-page: 129 issue: 1 year: 2011 ident: 10.1016/j.ymssp.2018.03.022_b0015 article-title: Dam break threshold value and risk probability assessment for an earth dam publication-title: Nat. Hazards doi: 10.1007/s11069-011-9743-6 – volume: 33 start-page: 903 issue: 3 year: 2011 ident: 10.1016/j.ymssp.2018.03.022_b0040 article-title: Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2010.12.011 – volume: 110 start-page: 43 issue: 1–2 year: 1997 ident: 10.1016/j.ymssp.2018.03.022_b0155 article-title: Practical method for determining the minimum embedding dimensions of a scalar time series publication-title: Physica D doi: 10.1016/S0167-2789(97)00118-8 – volume: 16 start-page: 285 issue: 3 year: 1985 ident: 10.1016/j.ymssp.2018.03.022_b0160 article-title: Determining Lyapunov exponents from a time series publication-title: Physica D doi: 10.1016/0167-2789(85)90011-9 – volume: 52 start-page: 3024 issue: 10 year: 2009 ident: 10.1016/j.ymssp.2018.03.022_b0005 article-title: Overtopping breaching of cohesive homogeneous earth dam with different cohesive strength publication-title: Sci. China Ser. E-Tech. Sci. doi: 10.1007/s11431-009-0275-1 – ident: 10.1016/j.ymssp.2018.03.022_b0070 – volume: 11 start-page: 269 issue: 3 year: 2012 ident: 10.1016/j.ymssp.2018.03.022_b0035 article-title: A study of safety evaluation and early-warning method for dam global behavior publication-title: Struct. Health Monit. doi: 10.1177/1475921711419993 – year: 1995 ident: 10.1016/j.ymssp.2018.03.022_b0055 – volume: 1 start-page: 33 issue: 1 year: 2007 ident: 10.1016/j.ymssp.2018.03.022_b0165 article-title: Particle swarm optimization publication-title: Swarm Intell. doi: 10.1007/s11721-007-0002-0 – year: 2000 ident: 10.1016/j.ymssp.2018.03.022_b0090 – volume: 47 start-page: 3 year: 2014 ident: 10.1016/j.ymssp.2018.03.022_b0110 article-title: Blind identification of damage in time-varying systems using independent component analysis with wavelet transform publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2012.08.029 – volume: 8 start-page: 240 issue: 3 year: 2004 ident: 10.1016/j.ymssp.2018.03.022_b0175 article-title: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2004.826071 – volume: 20 start-page: 282 year: 2013 ident: 10.1016/j.ymssp.2018.03.022_b0045 article-title: Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches publication-title: Struct. Contr. Health Monit. doi: 10.1002/stc.492 – volume: 14 start-page: 447 issue: 2 year: 2003 ident: 10.1016/j.ymssp.2018.03.022_b0105 article-title: A support vector machine formulation to PCA analysis and its kernel version publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2003.809414 – volume: 38 start-page: 11280 issue: 9 year: 2011 ident: 10.1016/j.ymssp.2018.03.022_b0140 article-title: Wind farm power prediction based on wavelet decomposition and chaotic time series publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.02.176 – volume: 22 start-page: 438 issue: 6 year: 2007 ident: 10.1016/j.ymssp.2018.03.022_b0115 article-title: Identification model for dam behavior based on wavelet network publication-title: Comput-Aided Civ. Inf. doi: 10.1111/j.1467-8667.2007.00499.x – volume: 133 start-page: 267 issue: 3 year: 2007 ident: 10.1016/j.ymssp.2018.03.022_b0020 article-title: Hydrostatic, temperature, time-displacement model for concrete dams publication-title: J. Eng. Mech. doi: 10.1061/(ASCE)0733-9399(2007)133:3(267) – volume: 29 start-page: 110 issue: 1 year: 2007 ident: 10.1016/j.ymssp.2018.03.022_b0025 article-title: Statistical analysis and structural identification in concrete dam monitoring publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2006.04.022 – volume: 19 start-page: 500 issue: 3 year: 2008 ident: 10.1016/j.ymssp.2018.03.022_b0120 article-title: Wavelet basis function neural networks for sequential learning publication-title: IEEE Trans. Neural Netw. – volume: 9 start-page: 293 issue: 3 year: 1999 ident: 10.1016/j.ymssp.2018.03.022_b0065 article-title: Least squares support vector machine classifiers publication-title: Neural Process. Lett. doi: 10.1023/A:1018628609742 – volume: 15 start-page: 639 issue: 6 year: 2016 ident: 10.1016/j.ymssp.2018.03.022_b0030 article-title: Dam safety prediction model considering chaotic characteristics in prototype monitoring data series publication-title: Struct. Health Monit. doi: 10.1177/1475921716654963 – volume: 94 start-page: 464 year: 2017 ident: 10.1016/j.ymssp.2018.03.022_b0100 article-title: Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms publication-title: Mech. Syst. Sig. Process. doi: 10.1016/j.ymssp.2017.03.016 – volume: 40 start-page: 4922 issue: 12 year: 2013 ident: 10.1016/j.ymssp.2018.03.022_b0145 article-title: Multifractal scaling behavior analysis for existing dams publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2013.02.033 – volume: 48 start-page: 612 year: 2016 ident: 10.1016/j.ymssp.2018.03.022_b0135 article-title: Dam structural behavior identification and prediction by using variable dimension fractal model and iterated function system publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2016.07.044 – volume: 51 start-page: 1345 issue: 9 year: 2008 ident: 10.1016/j.ymssp.2018.03.022_b0010 article-title: Risk assessment method of major unsafe hydroelectric project publication-title: Sci. China Ser. E-Tech. Sci. doi: 10.1007/s11431-008-0103-z – volume: 30 start-page: 1082 issue: 5 year: 2007 ident: 10.1016/j.ymssp.2018.03.022_b0170 article-title: A diversified multiobjective GA for optimizing reservoir rule curves publication-title: Adv. Water Resour. doi: 10.1016/j.advwatres.2006.10.001 |
| SSID | ssj0009406 |
| Score | 2.6339617 |
| Snippet | •SVM and other methods are combined to build prediction model of dam deformation.•Three key problems on SVM are investigated.•The proposed approach can reduce... Considering the strong nonlinear dynamic characteristics of dam deformation, the prediction model of dam deformation is investigated. Support vector machine... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 412 |
| SubjectTerms | Basis functions Characteristics identification Dam deformation Dams Deformation Design engineering Dynamic characteristics Improved particle swarm optimization algorithm Kernel functions Mathematical models Model accuracy Morlet wavelet Particle swarm optimization Prediction model Reconstruction Support vector machines Wavelet analysis Wavelet support vector machine Wavelet transforms |
| Title | Wavelet support vector machine-based prediction model of dam deformation |
| URI | https://dx.doi.org/10.1016/j.ymssp.2018.03.022 https://www.proquest.com/docview/2069500688 |
| Volume | 110 |
| WOSCitedRecordID | wos000431162900026&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: 1096-1216 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009406 issn: 0888-3270 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwELa20EM5VIW2ghaQD71tXW3ezpEiEK0o6gHKthcrfgSC2LAiu6ttfz3jR7wB1FU59BJFiWMlmfHM2P7mG4Q-yCTNeCkoKYOSk1iKlFCaU6KTLFMJEwpqUJU_jrOTEzoc5t97vXmbCzO7zuqazuf5-L-KGq6BsHXq7BPE7TuFC3AOQocjiB2O_yT480LXkpj0m-lYx9b9mVmX748MalIR7bYMNYCsbJVwUwtHx4yyGPWl8tmM3bD1m9IJwjaD0lKcm00HDf7QmVw22aB1gmaLybi0aVH9uaw85scAB4adZj_dYvVnVXHl251bS_jrUpfVvuguSwQGQ2ETM731oiQKbVUQb2odhNUay9gBqK3fjS1HwCOTblcXrj79HjWNJhgNqGGlDcOFB2t37R84Ng83bJFsV8x0wnQnbBAx6OQZWg2zJAd7uLr35WD4dUHYHJu6rP4zWsYqgw189C5_i2oe-HcTtJy-Qi_dbAPvWS1ZRz1Vb6C1Dgfla3Tk9AU7fcFWX_A9fcELfcFGX_BNiUFfcEdf3qCzw4PT_SPiymsQAUHohMgQ4rtcFppikmvGDk4DGLBJmZWR4CqSIhcQLQqa0VzFaRLwQAlNl1QMYBAPePQWrdQ3tdpEGPwGDaJEpQrm50UU00FRRCKTZQzxpsz4Fgrb38OE457XJVCu2RLRbKGP_qGxpV5Z3jxt_ztz0aONChlo0vIHt1spMTeOG7if5onOn6LvnvYa79GLxXDYRiuT26naQc_FbFI1t7tOy-4Av4mdaA |
| 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=Wavelet+support+vector+machine-based+prediction+model+of+dam+deformation&rft.jtitle=Mechanical+systems+and+signal+processing&rft.au=Su%2C+Huaizhi&rft.au=Li%2C+Xing&rft.au=Yang%2C+Beibei&rft.au=Wen%2C+Zhiping&rft.date=2018-09-15&rft.issn=0888-3270&rft.volume=110&rft.spage=412&rft.epage=427&rft_id=info:doi/10.1016%2Fj.ymssp.2018.03.022&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_ymssp_2018_03_022 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0888-3270&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0888-3270&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0888-3270&client=summon |