{"id":"https://openalex.org/W2897174746","doi":"https://doi.org/10.1109/ijcnn.2018.8489607","title":"Fine-Grained Air Quality Prediction using Attention Based Neural Network","display_name":"Fine-Grained Air Quality Prediction using Attention Based Neural Network","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2897174746","doi":"https://doi.org/10.1109/ijcnn.2018.8489607","mag":"2897174746"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2018.8489607","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489607","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5005660159","display_name":"Tianyu Liu","orcid":"https://orcid.org/0000-0001-9121-9021"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyu Liu","raw_affiliation_strings":["School of Information Science and Technology, Beijing Forestry University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Beijing Forestry University, Beijing, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113852219","display_name":"Ying Yongzhi","orcid":null},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongzhi Ying","raw_affiliation_strings":["School of Information Science and Technology, Beijing Forestry University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Beijing Forestry University, Beijing, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100704964","display_name":"Yanyan Xu","orcid":"https://orcid.org/0000-0001-7174-6588"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Xu","raw_affiliation_strings":["School of Information Science and Technology, Beijing Forestry University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Beijing Forestry University, Beijing, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102845007","display_name":"Dengfeng Ke","orcid":"https://orcid.org/0000-0001-8459-0412"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dengfeng Ke","raw_affiliation_strings":["National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037325282","display_name":"Kaile Su","orcid":"https://orcid.org/0000-0001-6741-9699"},"institutions":[{"id":"https://openalex.org/I159948400","display_name":"Jinan University","ror":"https://ror.org/02xe5ns62","country_code":"CN","type":"education","lineage":["https://openalex.org/I159948400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaile Su","raw_affiliation_strings":["Department of Computer Science, Jinan University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Jinan University, Guangzhou, China","institution_ids":["https://openalex.org/I159948400"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"39","issue":null,"first_page":"1","last_page":"6"},"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":0.9947999715805054,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/artificial-neural-network","display_name":"Artificial neural network","score":0.6733460426330566},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6691908836364746},{"id":"https://openalex.org/keywords/air-quality-index","display_name":"Air quality index","score":0.6562960743904114},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5224465727806091},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4728938639163971},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4649222195148468},{"id":"https://openalex.org/keywords/air-pollution","display_name":"Air pollution","score":0.4613054394721985},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45069369673728943},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.44580379128456116},{"id":"https://openalex.org/keywords/atmospheric-model","display_name":"Atmospheric model","score":0.437163770198822},{"id":"https://openalex.org/keywords/pollution","display_name":"Pollution","score":0.4235849380493164},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39261654019355774},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.3826923668384552}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6733460426330566},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6691908836364746},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.6562960743904114},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5224465727806091},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4728938639163971},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4649222195148468},{"id":"https://openalex.org/C559116025","wikidata":"https://www.wikidata.org/wiki/Q131123","display_name":"Air pollution","level":2,"score":0.4613054394721985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45069369673728943},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.44580379128456116},{"id":"https://openalex.org/C118365302","wikidata":"https://www.wikidata.org/wiki/Q4817115","display_name":"Atmospheric model","level":2,"score":0.437163770198822},{"id":"https://openalex.org/C521259446","wikidata":"https://www.wikidata.org/wiki/Q58734","display_name":"Pollution","level":2,"score":0.4235849380493164},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39261654019355774},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.3826923668384552},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn.2018.8489607","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2018.8489607","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/384045","is_oa":false,"landing_page_url":"http://hdl.handle.net/10072/384045","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference output"},{"id":"mag:3160943083","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=201802250868634930","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.550000011920929,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W956374238","https://openalex.org/W1514535095","https://openalex.org/W1522301498","https://openalex.org/W2025800405","https://openalex.org/W2048752066","https://openalex.org/W2131613942","https://openalex.org/W2134265359","https://openalex.org/W2165680013","https://openalex.org/W2293634267","https://openalex.org/W2365037844","https://openalex.org/W2521003103","https://openalex.org/W2552027021","https://openalex.org/W2609148117","https://openalex.org/W2767894694","https://openalex.org/W2962965405","https://openalex.org/W2963871484","https://openalex.org/W2964121744","https://openalex.org/W2964199361","https://openalex.org/W6630875275","https://openalex.org/W6631190155","https://openalex.org/W6696934422","https://openalex.org/W6729263887","https://openalex.org/W6746294351"],"related_works":["https://openalex.org/W3094447531","https://openalex.org/W2991488401","https://openalex.org/W1603912562","https://openalex.org/W2388613575","https://openalex.org/W3080344894","https://openalex.org/W4318499393","https://openalex.org/W2082703639","https://openalex.org/W2066546759","https://openalex.org/W2362463548","https://openalex.org/W2733363865"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,13,121,134,148,154],"new":[3],"two-stage":[4],"fine-grained":[5],"air":[6,116,125],"Particulate":[7],"Matter":[8],"(PM)":[9],"prediction":[10,151],"system":[11],"using":[12],"variety":[14],"of":[15,89,97,170,172],"deep":[16],"memory":[17],"networks.":[18],"Our":[19],"model":[20,81,165],"is":[21,133,183],"significantly":[22],"simpler":[23],"than":[24],"traditional":[25,65,149],"weather":[26],"report":[27],"systems,":[28],"which":[29],"rely":[30],"heavily":[31],"on":[32,166],"the":[33,95,102,164],"atmospheric":[34],"reaction":[35,58],"equations":[36,59],"and":[37,48,107,115,158],"pollutant":[38],"emissions":[39],"inventories.":[40],"Pollutant":[41],"inventories":[42,88],"are":[43,49,60],"notoriously":[44],"difficult":[45],"to":[46,62,69,100,124,130,146],"obtain":[47],"often":[50],"packed":[51],"with":[52,193],"declination":[53],"for":[54],"multiple":[55,142],"reasons,":[56],"while":[57],"impossible":[61],"exhaust.":[63],"These":[64],"models":[66],"also":[67,189],"tend":[68],"perform":[70],"poorly":[71],"when":[72],"affected":[73],"by":[74,176],"strong":[75],"convective":[76],"weather.":[77],"In":[78],"contrast,":[79],"our":[80,131,177],"does":[82],"not":[83,184],"need":[84],"precise":[85],"hand":[86],"collected":[87,175],"pollution":[90,126],"sources.":[91],"It":[92],"can":[93],"utilize":[94],"potential":[96],"Deep-Neural-Networks":[98],"(DNN)":[99],"reveal":[101],"relationship":[103],"among":[104],"different":[105],"locations":[106],"even":[108],"find":[109],"relations":[110],"between":[111],"known":[112],"social":[113],"events":[114],"quality.":[117],"Both":[118],"potentially":[119],"provide":[120],"valid":[122],"path":[123],"control.":[127],"The":[128],"key":[129],"approach":[132],"well-tuned":[135],"sophisticated":[136],"attention":[137],"based":[138],"network":[139],"that":[140,181],"uses":[141],"GPUs,":[143],"allowing":[144],"us":[145],"transform":[147],"sparse":[150],"problem":[152,157],"into":[153],"sequence-to-sequence":[155],"learning":[156],"train":[159],"it":[160,182],"end-to-end.":[161],"By":[162],"evaluating":[163],"over":[167],"1,625":[168],"instances":[169],"data":[171],"Northern":[173],"China":[174],"team,":[178],"we":[179],"show":[180],"only":[185],"computationally":[186],"efficient":[187],"but":[188],"accurately":[190],"feasible":[191],"compared":[192],"other":[194],"optional":[195],"models.":[196]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
