{"id":"https://openalex.org/W3139102848","doi":"https://doi.org/10.1109/bigdata50022.2020.9378094","title":"EvaNet: An Extreme Value Attention Network for Long-Term Air Quality Prediction","display_name":"EvaNet: An Extreme Value Attention Network for Long-Term Air Quality Prediction","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3139102848","doi":"https://doi.org/10.1109/bigdata50022.2020.9378094","mag":"3139102848"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5068707911","display_name":"Zechuan Chen","orcid":"https://orcid.org/0000-0003-4363-5949"},"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":"Zechuan Chen","raw_affiliation_strings":["Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103975844","display_name":"Haomin Yu","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":"Haomin Yu","raw_affiliation_strings":["Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018191388","display_name":"Yangli\u2010ao Geng","orcid":"https://orcid.org/0000-0003-0173-4041"},"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":"Yangli-ao Geng","raw_affiliation_strings":["Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005350496","display_name":"Qingyong Li","orcid":"https://orcid.org/0000-0002-3860-4809"},"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":"Qingyong Li","raw_affiliation_strings":["Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078952133","display_name":"Yingjun Zhang","orcid":"https://orcid.org/0000-0003-4279-2715"},"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":"Yingjun Zhang","raw_affiliation_strings":["Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Lab. of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4545","last_page":"4552"},"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/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9884999990463257,"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/T10190","display_name":"Air Quality and Health Impacts","score":0.987500011920929,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7097074389457703},{"id":"https://openalex.org/keywords/air-quality-index","display_name":"Air quality index","score":0.6451875567436218},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5884360671043396},{"id":"https://openalex.org/keywords/extreme-learning-machine","display_name":"Extreme learning machine","score":0.5459995269775391},{"id":"https://openalex.org/keywords/long-term-prediction","display_name":"Long-term prediction","score":0.4616350829601288},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.4609120488166809},{"id":"https://openalex.org/keywords/extreme-value-theory","display_name":"Extreme value theory","score":0.4571710228919983},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.4430449903011322},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42992573976516724},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3512345850467682},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2351667284965515},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12536954879760742},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.08196333050727844},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07853224873542786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7097074389457703},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.6451875567436218},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5884360671043396},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.5459995269775391},{"id":"https://openalex.org/C2776537626","wikidata":"https://www.wikidata.org/wiki/Q4047883","display_name":"Long-term prediction","level":2,"score":0.4616350829601288},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.4609120488166809},{"id":"https://openalex.org/C147581598","wikidata":"https://www.wikidata.org/wiki/Q729429","display_name":"Extreme value theory","level":2,"score":0.4571710228919983},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.4430449903011322},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42992573976516724},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3512345850467682},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2351667284965515},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12536954879760742},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.08196333050727844},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07853224873542786},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null},{"id":"https://openalex.org/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1591801644","https://openalex.org/W1969852690","https://openalex.org/W1971402834","https://openalex.org/W2038878767","https://openalex.org/W2051937251","https://openalex.org/W2092650071","https://openalex.org/W2098849981","https://openalex.org/W2130942839","https://openalex.org/W2157331557","https://openalex.org/W2172073485","https://openalex.org/W2209610041","https://openalex.org/W2489467228","https://openalex.org/W2613328025","https://openalex.org/W2808535700","https://openalex.org/W2809533013","https://openalex.org/W2928323670","https://openalex.org/W2942521752","https://openalex.org/W2950418200","https://openalex.org/W2952042565","https://openalex.org/W2952135817","https://openalex.org/W2964199361","https://openalex.org/W2964927812","https://openalex.org/W2990955039","https://openalex.org/W2997979947","https://openalex.org/W6635446068","https://openalex.org/W6679436768","https://openalex.org/W6757003350"],"related_works":["https://openalex.org/W2067443264","https://openalex.org/W31566076","https://openalex.org/W4297902562","https://openalex.org/W2741186499","https://openalex.org/W2804652951","https://openalex.org/W2556335056","https://openalex.org/W2002678693","https://openalex.org/W1584764049","https://openalex.org/W2743832667","https://openalex.org/W2148113366"],"abstract_inverted_index":{"Air":[0,8],"quality":[1,9,66,123],"affects":[2],"social":[3],"activities":[4],"and":[5,28,42,59],"human":[6],"health.":[7],"prediction,":[10],"especially":[11],"for":[12],"extreme":[13,40,51,72],"events":[14],"such":[15],"as":[16],"severe":[17],"haze":[18],"pollution,":[19],"plays":[20],"an":[21,50,71],"essential":[22],"guiding":[23],"role":[24],"in":[25],"government":[26],"decision-making":[27],"outdoor":[29],"activity":[30],"scheduling.":[31],"Established":[32],"prediction":[33],"models":[34],"face":[35],"the":[36,78,99,103,113,126],"challenges":[37],"of":[38,80,128],"forecasting":[39],"values":[41],"long-term":[43,64,89],"tendency.":[44],"In":[45,85],"this":[46],"paper,":[47],"we":[48],"propose":[49],"value":[52,73],"attention":[53,74,96,101],"network":[54],"(EvaNet)":[55],"based":[56],"on":[57,83,119],"encoder":[58],"decoder":[60,110],"framework":[61],"to":[62,76,87,111],"achieve":[63],"air":[65,122],"prediction.":[67,84,115],"This":[68],"model":[69],"designs":[70],"mechanism":[75],"alleviate":[77],"impact":[79],"sudden":[81],"changes":[82],"addition,":[86],"capture":[88],"dependence":[90],"relationships,":[91],"EvaNet":[92],"introduces":[93],"a":[94,109],"temporal":[95],"mechanism.":[97],"Integrating":[98],"dual":[100],"mechanisms,":[102],"extracted":[104],"features":[105],"are":[106],"fed":[107],"into":[108],"yield":[112],"final":[114],"The":[116],"experiments":[117],"evaluated":[118],"two":[120],"real-world":[121],"datasets":[124],"show":[125],"superiority":[127],"our":[129],"method":[130],"against":[131],"other":[132],"state-of-the-art":[133],"baselines.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
