{"id":"https://openalex.org/W7083168373","doi":"https://doi.org/10.32604/cmc.2025.067952","title":"Prediction of Landslide Displacement Using a BiLSTM-RBF Model Based on a Hybrid Attention Mechanism","display_name":"Prediction of Landslide Displacement Using a BiLSTM-RBF Model Based on a Hybrid Attention Mechanism","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7083168373","doi":"https://doi.org/10.32604/cmc.2025.067952"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2025.067952","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.067952","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.32604/cmc.2025.067952","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jiao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jiao Chen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Xiao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao Wang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhiqin He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhiqin He","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Yi Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi Chen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Chao Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chao Ma","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.57611292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"85","issue":"3","first_page":"5423","last_page":"5450"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T13370","display_name":"Diverse Scientific and Economic Studies","score":0.08879999816417694,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13370","display_name":"Diverse Scientific and Economic Studies","score":0.08879999816417694,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13398","display_name":"Data Analysis with R","score":0.01850000023841858,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13523","display_name":"Mathematics, Computing, and Information Processing","score":0.017000000923871994,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6237999796867371},{"id":"https://openalex.org/keywords/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.6046000123023987},{"id":"https://openalex.org/keywords/displacement","display_name":"Displacement (psychology)","score":0.4634000062942505},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.46140000224113464},{"id":"https://openalex.org/keywords/exponential-smoothing","display_name":"Exponential smoothing","score":0.4487999975681305},{"id":"https://openalex.org/keywords/landslide","display_name":"Landslide","score":0.44679999351501465},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.42989999055862427},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42800000309944153},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.42260000109672546}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7245000004768372},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6237999796867371},{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.6046000123023987},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5202999711036682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49050000309944153},{"id":"https://openalex.org/C107551265","wikidata":"https://www.wikidata.org/wiki/Q1458245","display_name":"Displacement (psychology)","level":2,"score":0.4634000062942505},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.46140000224113464},{"id":"https://openalex.org/C133710760","wikidata":"https://www.wikidata.org/wiki/Q775837","display_name":"Exponential smoothing","level":2,"score":0.4487999975681305},{"id":"https://openalex.org/C186295008","wikidata":"https://www.wikidata.org/wiki/Q167903","display_name":"Landslide","level":2,"score":0.44679999351501465},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.42260000109672546},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4065999984741211},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.39259999990463257},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34540000557899475},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.3361999988555908},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C2779629037","wikidata":"https://www.wikidata.org/wiki/Q5535282","display_name":"Geohazard","level":3,"score":0.298799991607666},{"id":"https://openalex.org/C136428324","wikidata":"https://www.wikidata.org/wiki/Q838414","display_name":"Deformation monitoring","level":3,"score":0.2851000130176544},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.32604/cmc.2025.067952","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.067952","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2025.067952","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2025.067952","pdf_url":null,"source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.655074417591095,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2174066569","https://openalex.org/W2910183581","https://openalex.org/W2971930569","https://openalex.org/W3137303737","https://openalex.org/W4200149104","https://openalex.org/W4245444932","https://openalex.org/W4281752625","https://openalex.org/W4283785408","https://openalex.org/W4307130830","https://openalex.org/W4309457030","https://openalex.org/W4311567873","https://openalex.org/W4319594516","https://openalex.org/W4323364788","https://openalex.org/W4360986054","https://openalex.org/W4377096625","https://openalex.org/W4382809718","https://openalex.org/W4391804647","https://openalex.org/W4391918666","https://openalex.org/W4392601919","https://openalex.org/W4399442824","https://openalex.org/W4401107041","https://openalex.org/W4402275521","https://openalex.org/W4402307867","https://openalex.org/W4407150266"],"related_works":[],"abstract_inverted_index":{"This":[0],"research":[1],"proposes":[2],"an":[3,59,164],"innovative":[4,165],"solution":[5],"to":[6,62,107,134,243],"the":[7,22,30,37,140,175,179,182,201,207,212,218,232,240],"inherent":[8],"challenges":[9,65],"faced":[10],"by":[11,123,163,194,225],"landslide":[12,42],"displacement":[13,85],"prediction":[14,52,68,141],"models":[15],"based":[16],"on":[17,32],"data-driven":[18],"methods,":[19],"such":[20],"as":[21],"need":[23],"for":[24,28],"extensive":[25],"historical":[26,43],"datasets":[27],"training,":[29],"reliance":[31],"manual":[33],"feature":[34,87,136,149,251],"selection,":[35],"and":[36,58,66,86,185,197,228,250],"difficulty":[38],"in":[39,206,254],"effectively":[40,82],"utilizing":[41],"data.":[44],"We":[45,90],"have":[46,91],"developed":[47,92],"a":[48,74,93,101,113,124,130],"dual-channel":[49],"deep":[50],"learning":[51],"model":[53,204,235],"that":[54],"integrates":[55],"multimodal":[56],"decomposition":[57],"attention":[60,126,153],"mechanism":[61,151],"overcome":[63,244],"these":[64],"improve":[67],"performance.":[69],"The":[70],"proposed":[71],"methodology":[72],"follows":[73],"three-stage":[75],"framework:":[76],"(1)":[77],"Empirical":[78],"Mode":[79],"Decomposition":[80],"(EMD)":[81],"segregates":[83],"cumulative":[84],"factors;":[88],"(2)":[89],"Double":[94],"Exponential":[95],"Smoothing":[96],"(DES)":[97],"ensemble":[98],"optimized":[99],"through":[100,159],"Non-dominated":[102],"Sorting":[103],"Genetic":[104],"Algorithm-II":[105],"(NSGA-II)":[106],"enhance":[108],"trend":[109],"prediction;":[110],"while":[111],"employing":[112],"Bidirectional":[114],"Long":[115],"Short-Term":[116],"Memory-Radial":[117],"Basis":[118],"Function":[119],"(BiLSTM-RBF)":[120],"network":[121],"enhanced":[122],"hybrid":[125],"mechanism,":[127],"which":[128],"facilitates":[129],"global-local":[131],"synergistic":[132],"approach":[133],"hierarchical":[135],"extraction,":[137],"thereby":[138],"improving":[139],"of":[142,178,188,214,247],"periodic":[143],"displacements;":[144],"(3)":[145],"A":[146],"bidirectional":[147],"adaptive":[148],"extraction":[150],"aligns":[152],"weights":[154],"with":[155,200,231],"BiLSTM":[156],"propagation":[157],"paths":[158],"spatial":[160],"mapping,":[161],"complemented":[162],"loss":[166],"function":[167],"incorporating":[168],"Prediction":[169],"Interval":[170],"(PI)":[171],"width":[172],"optimization.":[173],"In":[174],"comparative":[176],"experiments":[177],"Baishuihe":[180],"landslide:":[181],"RMSE,":[183],"MAE,":[184],"R2":[186],"indexes":[187,220],"monitoring":[189,208],"point":[190,209],"ZG118":[191],"are":[192,221],"improved":[193,224],"19.8%,":[195],"35.2%,":[196],"3.2%":[198],"compared":[199,230],"optimal":[202,233],"baseline":[203,234],"(RBF-MIC);":[205],"ZG93,":[210],"where":[211],"amount":[213],"data":[215,248],"is":[216],"less,":[217],"three":[219],"even":[222],"more":[223],"52.1%,":[226],"32.3%,":[227],"21.8%":[229],"(GRU-None).":[236],"These":[237],"results":[238],"substantiate":[239],"model\u2019s":[241],"capacity":[242],"dual":[245],"constraints":[246],"paucity":[249],"engineering":[252],"limitations":[253],"geohazard":[255],"prediction.":[256]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}