{"id":"https://openalex.org/W4402594687","doi":"https://doi.org/10.1109/icce-taiwan62264.2024.10674217","title":"A Method for Predicting Peak Ground Acceleration using Graph Neural Networks","display_name":"A Method for Predicting Peak Ground Acceleration using Graph Neural Networks","publication_year":2024,"publication_date":"2024-07-09","ids":{"openalex":"https://openalex.org/W4402594687","doi":"https://doi.org/10.1109/icce-taiwan62264.2024.10674217"},"language":"en","primary_location":{"id":"doi:10.1109/icce-taiwan62264.2024.10674217","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhou Datian","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou Datian","raw_affiliation_strings":["Beijing Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026760255","display_name":"Qianwen Ma","orcid":"https://orcid.org/0000-0003-2419-2951"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ma Qianwen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003640685","display_name":"Yang Lin","orcid":"https://orcid.org/0000-0001-8963-5384"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Lin","raw_affiliation_strings":["The Center of National Railway Intelligent Transportation System Engineering and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Center of National Railway Intelligent Transportation System Engineering and Technology","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114228431","display_name":"G. Kun","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141966","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210141966"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gu Kun","raw_affiliation_strings":["China Academy of Railway Sciences Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Academy of Railway Sciences Group","institution_ids":["https://openalex.org/I4210141966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"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":0.12836025,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"41","issue":null,"first_page":"179","last_page":"180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.4893999993801117,"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"}},"topics":[{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.4893999993801117,"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/T13734","display_name":"Advanced Computational Techniques and Applications","score":0.4237000048160553,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.6798067092895508},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6723178029060364},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5464895367622375},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41632869839668274},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3480437397956848},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.18721771240234375},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1618538498878479}],"concepts":[{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.6798067092895508},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6723178029060364},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5464895367622375},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41632869839668274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3480437397956848},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.18721771240234375},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1618538498878479},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce-taiwan62264.2024.10674217","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320328872","display_name":"China Academy of Railway Sciences","ror":"https://ror.org/051wv2j09"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W6754929296"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Peak":[0],"Ground":[1],"Acceleration":[2],"(PGA)":[3],"reflects":[4],"the":[5,24,110],"seismic":[6,49,65],"intensity":[7],"of":[8,18,26,46,93],"a":[9,14,27,35,42,55,64,105],"specific":[10,94],"earthquake":[11],"event":[12],"in":[13,29],"given":[15,106],"region.":[16],"Prediction":[17],"PGA":[19,75,103,112,127],"is":[20,88],"crucial":[21],"for":[22,37,54,84,104],"assessing":[23],"severity":[25],"disaster":[28],"an":[30],"area.":[31],"This":[32],"paper":[33],"proposed":[34],"method":[36],"predicting":[38],"PGA.":[39],"It":[40,108],"utilizes":[41],"dataset":[43],"comprising":[44],"records":[45],"over":[47],"1600":[48],"events":[50],"as":[51],"training":[52],"samples":[53],"Graph":[56],"Neural":[57],"Network":[58],"(GNN).":[59],"Each":[60],"sample":[61],"corresponds":[62],"to":[63],"event,":[66],"with":[67,132],"data":[68,82],"including":[69],"latitude,":[70],"longitude,":[71],"elevation,":[72],"and":[73,125],"three-component":[74],"observed":[76],"from":[77],"multiple":[78],"stations.":[79,95],"A":[80],"graph":[81,85],"structure":[83],"level":[86],"tasks":[87],"constructed,":[89],"capturing":[90],"site":[91,122],"effects":[92,123],"The":[96,117],"model,":[97],"trained":[98],"on":[99],"this":[100],"dataset,":[101],"infers":[102],"location.":[107],"validates":[109],"inferred":[111],"by":[113],"corresponding":[114],"real":[115],"data.":[116],"experiments":[118],"show":[119],"it":[120],"learns":[121],"effectively":[124],"predicts":[126],"at":[128],"any":[129],"concerned":[130],"location":[131],"demonstrating":[133],"strong":[134],"generalization":[135],"capabilities.":[136]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}