{"id":"https://openalex.org/W4312346007","doi":"https://doi.org/10.1109/ijcnn55064.2022.9891881","title":"Spatio-Temporal Modeling For Air Quality Prediction Based On Spectral Graph Convolutional Network And Attention Mechanism","display_name":"Spatio-Temporal Modeling For Air Quality Prediction Based On Spectral Graph Convolutional Network And Attention Mechanism","publication_year":2022,"publication_date":"2022-07-18","ids":{"openalex":"https://openalex.org/W4312346007","doi":"https://doi.org/10.1109/ijcnn55064.2022.9891881"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn55064.2022.9891881","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9891881","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","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":"https://openalex.org/A5102296817","display_name":"Yiyi Song","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiyi Song","raw_affiliation_strings":["East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China","Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084769547","display_name":"Hongyan Mao","orcid":"https://orcid.org/0000-0001-6749-1607"},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyan Mao","raw_affiliation_strings":["East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China","Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100690418","display_name":"Hongwei Li","orcid":"https://orcid.org/0000-0002-7792-8611"},"institutions":[{"id":"https://openalex.org/I4210139618","display_name":"Shanghai Key Laboratory of Trustworthy Computing","ror":"https://ror.org/030qbr085","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210139618"]},{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Li","raw_affiliation_strings":["East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China","Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"East China Normal University,Shanghai Key Laboratory of Trustworthy Computing,Shanghai,China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]},{"raw_affiliation_string":"Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I4210139618","https://openalex.org/I66867065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.1943,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.94009714,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.9743000268936157,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7025874257087708},{"id":"https://openalex.org/keywords/air-quality-index","display_name":"Air quality index","score":0.6317214369773865},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5299295783042908},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5041743516921997},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.49752429127693176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34185174107551575},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1700945794582367},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.15201380848884583}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7025874257087708},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.6317214369773865},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5299295783042908},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5041743516921997},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.49752429127693176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34185174107551575},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1700945794582367},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.15201380848884583},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn55064.2022.9891881","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn55064.2022.9891881","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"2022 International Joint Conference on Neural Networks (IJCNN)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2818542160","display_name":"\u9762\u5411\u4fe1\u606f\u7269\u7406\u878d\u5408\u7cfb\u7edf\u7684\u7a0b\u5e8f\u7edf\u4e00\u7406\u8bba\u7814\u7a76","funder_award_id":"61872145","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W956374238","https://openalex.org/W2095239580","https://openalex.org/W2570322979","https://openalex.org/W2760506659","https://openalex.org/W2793258900","https://openalex.org/W2795470013","https://openalex.org/W2893104802","https://openalex.org/W2905872298","https://openalex.org/W2907492528","https://openalex.org/W2914487400","https://openalex.org/W2991648381","https://openalex.org/W3090663188","https://openalex.org/W3115103108","https://openalex.org/W3140897233","https://openalex.org/W3196823293"],"related_works":["https://openalex.org/W2067317451","https://openalex.org/W2154771632","https://openalex.org/W4211085505","https://openalex.org/W3122478268","https://openalex.org/W2084758217","https://openalex.org/W408804804","https://openalex.org/W4231021675","https://openalex.org/W3086365953","https://openalex.org/W4226072953","https://openalex.org/W2392606101"],"abstract_inverted_index":{"Air":[0],"quality":[1,72,165,253],"prediction":[2,73,173],"is":[3,93,108,125,145],"always":[4],"an":[5],"urgent":[6],"and":[7,14,20,55,95,140,159,209,226,242],"challenging":[8],"issue":[9],"due":[10],"to":[11,75,237,247],"the":[12,33,41,51,56,116,132,148,207,216],"diffusivity":[13],"anelasticity":[15],"of":[16,35,58,89,134,153,178],"air":[17,38,71,90,156,164,252],"pollutants.":[18],"Temporal":[19],"spatial":[21,53,82,112,135,138],"dependency,":[22],"as":[23,46],"influencing":[24],"factors,":[25,155],"have":[26],"progressively":[27],"represented":[28],"a":[29,69,81,175,223,234,243],"significant":[30],"portion":[31],"in":[32,168,183,205,213,251],"forecast":[34],"constant":[36],"change-making":[37],"pollution.":[39],"Nevertheless,":[40],"existing":[42],"hybrid":[43],"models,":[44],"such":[45],"GC-LSTM,":[47],"fully":[48],"connected-LSTM,":[49],"disregard":[50],"long-distance":[52],"dependence":[54],"distinction":[57],"weights":[59],"at":[60,162],"various":[61,184],"timesteps.":[62],"For":[63],"avoiding":[64],"these":[65],"limitations,":[66],"we":[67],"propose":[68],"novel":[70],"model":[74,144,197,219,232],"process":[76],"spatiotemporal":[77,154,218],"dependency.":[78],"In":[79],"detail,":[80],"adjacency":[83],"graph":[84,98],"structure":[85],"based":[86,188],"on":[87,147,189],"correlations":[88,250],"monitoring":[91,166],"stations":[92,167],"constructed,":[94],"then":[96],"spectral":[97],"convolution":[99],"with":[100,123,131,239],"2":[101],"<sup":[102],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[103],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">nd</sup>":[104],"-order":[105],"Chebyshev":[106],"polynomial":[107],"well":[109],"designed.":[110],"This":[111,143],"module":[113,139],"can":[114,220],"capture":[115],"impacts":[117],"from":[118,137],"remote":[119],"stations.":[120],"Attention":[121],"mechanism":[122],"LSTM":[124],"utilized":[126],"for":[127],"capturing":[128],"temporal":[129],"dependency":[130],"inputs":[133],"features":[136],"node":[141],"features.":[142],"applied":[146],"hourly":[149],"scaled":[150],"dataset":[151],"consisting":[152],"pollutant":[157],"concentrations,":[158],"meteorological":[160],"factors":[161],"12":[163],"Shanghai.":[169],"To":[170],"evaluate":[171],"our":[172,196,231],"effects,":[174],"large":[176],"number":[177],"comparative":[179],"experiments":[180],"are":[181],"implemented":[182],"preset":[185],"time":[186],"windows":[187],"5":[190],"metrics.":[191],"The":[192],"results":[193],"illustrate":[194],"that":[195],"achieves":[198],"higher":[199],"accuracy":[200],"than":[201],"other":[202],"compared":[203],"models":[204],"both":[206],"short":[208],"long":[210],"term.":[211],"Furthermore,":[212],"exceptional":[214],"cases,":[215],"proposed":[217],"also":[221],"make":[222],"relatively":[224],"stable":[225],"accurate":[227],"prediction,":[228],"which":[229],"demonstrates":[230],"has":[233],"satisfactory":[235],"ability":[236,246],"deal":[238],"special":[240],"situations":[241],"great":[244],"generalization":[245],"handle":[248],"complex":[249],"prediction.":[254]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}