{"id":"https://openalex.org/W4414231907","doi":"https://doi.org/10.1109/sera65747.2025.11154585","title":"Forecasting Air Quality Index (AQI) Using Machine Learning Techniques","display_name":"Forecasting Air Quality Index (AQI) Using Machine Learning Techniques","publication_year":2025,"publication_date":"2025-05-29","ids":{"openalex":"https://openalex.org/W4414231907","doi":"https://doi.org/10.1109/sera65747.2025.11154585"},"language":"en","primary_location":{"id":"doi:10.1109/sera65747.2025.11154585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sera65747.2025.11154585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACIS 23rd International Conference on Software Engineering Research, Management and Applications (SERA)","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/A5117309143","display_name":"Lord Coffie","orcid":null},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lord Coffie","raw_affiliation_strings":["Georgia Southern University,Information Technology,Statesboro,GA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Southern University,Information Technology,Statesboro,GA,USA","institution_ids":["https://openalex.org/I39815113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119631207","display_name":"Emmanuella Bosompema Obeng","orcid":null},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Emmanuella Bosompema Obeng","raw_affiliation_strings":["Georgia Southern University,Geology and Geography,Statesboro,GA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Southern University,Geology and Geography,Statesboro,GA,USA","institution_ids":["https://openalex.org/I39815113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117309074","display_name":"Mary Dufie Afrane","orcid":"https://orcid.org/0009-0001-8057-7314"},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mary Dufie Afrane","raw_affiliation_strings":["Georgia Southern University,Computer Science,Statesboro,GA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Southern University,Computer Science,Statesboro,GA,USA","institution_ids":["https://openalex.org/I39815113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047923732","display_name":"Jongyeop Kim","orcid":"https://orcid.org/0000-0002-1068-9855"},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jongyeop Kim","raw_affiliation_strings":["Georgia Southern University,Information Technology,Statesboro,GA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Southern University,Information Technology,Statesboro,GA,USA","institution_ids":["https://openalex.org/I39815113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113978179","display_name":"Yao Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Xu","raw_affiliation_strings":["Georgia Southern University,Computer Science,Statesboro,GA,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Southern University,Computer Science,Statesboro,GA,USA","institution_ids":["https://openalex.org/I39815113"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1663,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.78707811,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"431","last_page":"437"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9951000213623047,"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":0.9951000213623047,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.7250999808311462},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5839999914169312},{"id":"https://openalex.org/keywords/air-quality-index","display_name":"Air quality index","score":0.5608000159263611},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5342000126838684},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.421999990940094},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4147999882698059},{"id":"https://openalex.org/keywords/air-pollution","display_name":"Air pollution","score":0.39239999651908875},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37209999561309814}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7250999808311462},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.692799985408783},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6309999823570251},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5839999914169312},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.5608000159263611},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5544000267982483},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5342000126838684},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.421999990940094},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C559116025","wikidata":"https://www.wikidata.org/wiki/Q131123","display_name":"Air pollution","level":2,"score":0.39239999651908875},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32120001316070557},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C170061395","wikidata":"https://www.wikidata.org/wiki/Q5468164","display_name":"Forecast skill","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.3019999861717224},{"id":"https://openalex.org/C2777570903","wikidata":"https://www.wikidata.org/wiki/Q4698151","display_name":"Air Pollution Index","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26330000162124634}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sera65747.2025.11154585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sera65747.2025.11154585","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/ACIS 23rd International Conference on Software Engineering Research, Management and Applications (SERA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2687560032","https://openalex.org/W2791640723","https://openalex.org/W2794210464","https://openalex.org/W2889990933","https://openalex.org/W2914477964","https://openalex.org/W2916789854","https://openalex.org/W2997444524","https://openalex.org/W3114314572","https://openalex.org/W3127387526","https://openalex.org/W3131333586","https://openalex.org/W3154223757","https://openalex.org/W3163591183","https://openalex.org/W4200026345","https://openalex.org/W4240075522","https://openalex.org/W4280583385","https://openalex.org/W4283379645","https://openalex.org/W4293055828","https://openalex.org/W4307816161","https://openalex.org/W4386814473"],"related_works":[],"abstract_inverted_index":{"Air":[0,35],"pollution":[1],"is":[2],"still":[3],"a":[4],"major":[5],"problem":[6],"in":[7,59,128,168],"cities":[8],"where":[9],"pollutants,":[10],"such":[11,81],"as":[12,82,90,92],"ozone":[13],"(O<inf":[14],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[15,21],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">3</inf>)":[16],"and":[17,26,44,56,86,98,121,137,149,157],"sulfur":[18],"dioxide":[19],"(SO<inf":[20],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</inf>),":[22],"can":[23,39],"harm":[24],"health":[25],"the":[27,34,122,164],"environment.":[28],"Thus,":[29],"being":[30],"able":[31],"to":[32,71],"forecast":[33],"Quality":[36],"Index":[37],"(AQI)":[38],"help":[40],"make":[41],"better-informed":[42],"decisions":[43],"interventions.":[45],"This":[46],"study":[47],"assesses":[48],"traditional":[49],"machine":[50,78],"learning":[51,54,79],"(ML),":[52],"deep":[53],"(DL),":[55],"hybrid":[57,99,152],"models":[58,80,100,167],"AQI":[60],"prediction":[61],"using":[62],"real-world":[63],"data":[64],"from":[65,69],"New":[66],"York":[67],"City":[68],"2014":[70],"2015.":[72],"The":[73,131],"research":[74],"involved":[75],"comparing":[76],"various":[77],"Random":[83,135,155],"Forest,":[84],"XGBoost,":[85],"Support":[87],"Vector":[88],"Regression,":[89],"well":[91],"Long":[93],"Short-Term":[94],"Memory":[95],"(LSTM)":[96],"networks":[97],"combining":[101],"ML":[102],"with":[103,154],"DL":[104],"techniques.":[105],"Model":[106],"performance":[107],"was":[108,159],"evaluated":[109],"based":[110],"on":[111,144],"Mean":[112,117],"Absolute":[113],"Error":[114,119],"(MAE),":[115],"Root":[116],"Squared":[118],"(RMSE),":[120],"coefficient":[123],"of":[124,147],"determination":[125],"($\\mathbf{R}^{2}$)":[126],"values":[127],"multiple":[129],"neighborhoods.":[130,170],"authors":[132],"found":[133],"that":[134],"Forest":[136,156],"XGBoost":[138],"performed":[139],"better":[140],"than":[141,163],"stand-alone":[142,165],"LSTM":[143,158,166],"both":[145],"accuracy":[146],"forecasting":[148],"stability.":[150],"A":[151],"model":[153],"also":[160],"more":[161],"robust":[162],"all":[169]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}