{"id":"https://openalex.org/W4417289390","doi":"https://doi.org/10.48550/arxiv.2512.09076","title":"Beyond the Hype: Comparing Lightweight and Deep Learning Models for Air Quality Forecasting","display_name":"Beyond the Hype: Comparing Lightweight and Deep Learning Models for Air Quality Forecasting","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W4417289390","doi":"https://doi.org/10.48550/arxiv.2512.09076"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.09076","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.09076","pdf_url":"https://arxiv.org/pdf/2512.09076","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.09076","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120789515","display_name":"Moazzam Umer Gondal","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gondal, Moazzam Umer","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093953984","display_name":"Hamad ul Qudous","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qudous, Hamad ul","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5067561067","display_name":"Asma Ahmad Farhan","orcid":"https://orcid.org/0009-0004-4267-0253"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Farhan, Asma Ahmad","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5120789515"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9843999743461609,"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":0.9843999743461609,"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":0.01140000019222498,"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/T10075","display_name":"Atmospheric chemistry and aerosols","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8959000110626221},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6693000197410583},{"id":"https://openalex.org/keywords/air-quality-index","display_name":"Air quality index","score":0.5738000273704529},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5223000049591064},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41190001368522644},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.39430001378059387},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.39399999380111694},{"id":"https://openalex.org/keywords/air-pollution","display_name":"Air pollution","score":0.3930000066757202},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.3806000053882599}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8959000110626221},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6693000197410583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6258999705314636},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.5738000273704529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5708000063896179},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5602999925613403},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5223000049591064},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41190001368522644},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.39430001378059387},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.39399999380111694},{"id":"https://openalex.org/C559116025","wikidata":"https://www.wikidata.org/wiki/Q131123","display_name":"Air pollution","level":2,"score":0.3930000066757202},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32350000739097595},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C194648359","wikidata":"https://www.wikidata.org/wiki/Q3318054","display_name":"Generalized additive model","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2754000127315521},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2702000141143799},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.09076","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.09076","pdf_url":"https://arxiv.org/pdf/2512.09076","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.09076","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.09076","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2512.09076","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.09076","pdf_url":"https://arxiv.org/pdf/2512.09076","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"forecasting":[1],"of":[2,165,170],"urban":[3],"air":[4],"pollution":[5],"is":[6],"essential":[7],"for":[8,53,143],"protecting":[9],"public":[10],"health":[11],"and":[12,20,28,45,64,78,88,105,124,134,158,168],"guiding":[13],"mitigation":[14],"policies.":[15],"While":[16],"Deep":[17],"Learning":[18],"(DL)":[19],"hybrid":[21],"pipelines":[22],"dominate":[23],"recent":[24],"research,":[25],"their":[26],"complexity":[27],"limited":[29],"interpretability":[30],"hinder":[31],"operational":[32],"use.":[33],"This":[34],"study":[35],"investigates":[36],"whether":[37],"lightweight":[38],"additive":[39,151],"models":[40,83,152],"--":[41,48],"Facebook":[42],"Prophet":[43],"(FBP)":[44],"NeuralProphet":[46],"(NP)":[47],"can":[49],"deliver":[50],"competitive":[51,154],"forecasts":[52],"particulate":[54],"matter":[55],"(PM$_{2.5}$,":[56],"PM$_{10}$)":[57],"in":[58],"Beijing,":[59],"China.":[60],"Using":[61],"multi-year":[62],"pollutant":[63,87],"meteorological":[65],"data,":[66],"we":[67],"applied":[68],"systematic":[69],"feature":[70],"selection":[71],"(correlation,":[72],"mutual":[73],"information,":[74],"mRMR),":[75],"leakage-safe":[76],"scaling,":[77],"chronological":[79],"data":[80],"splits.":[81],"Both":[82],"were":[84,111],"trained":[85],"with":[86,91,155],"precursor":[89],"regressors,":[90],"NP":[92],"additionally":[93],"leveraging":[94],"lagged":[95],"dependencies.":[96],"For":[97],"context,":[98],"two":[99],"machine":[100],"learning":[101],"baselines":[102],"(LSTM,":[103],"LightGBM)":[104],"one":[106],"traditional":[107,157],"statistical":[108],"model":[109],"(SARIMAX)":[110],"also":[112],"implemented.":[113],"Performance":[114],"was":[115],"evaluated":[116],"on":[117],"a":[118,162],"7-day":[119],"holdout":[120],"using":[121],"MAE,":[122],"RMSE,":[123],"$R^2$.":[125],"Results":[126],"show":[127],"that":[128,149],"FBP":[129],"consistently":[130],"outperformed":[131],"NP,":[132],"SARIMAX,":[133],"the":[135],"learning-based":[136],"baselines,":[137],"achieving":[138],"test":[139],"$R^2$":[140],"above":[141],"0.94":[142],"both":[144,156],"pollutants.":[145],"These":[146],"findings":[147],"demonstrate":[148],"interpretable":[150],"remain":[153],"complex":[159],"approaches,":[160],"offering":[161],"practical":[163],"balance":[164],"accuracy,":[166],"transparency,":[167],"ease":[169],"deployment.":[171]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-12T00:00:00"}
